Logistic Regression - Case Study.ipynb

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    "# Lead Scoring Case Study\n",
    "\n",
    "Problem Statement :\n",
    "\n",
    "An education company named X Education sells online courses to industry professionals. The company markets its courses on several websites and search engines like Google. Once these people land on the website, they might browse the courses or fill up a form for the course or watch some videos. When these people fill up a form providing their email address or phone number, they are classified to be a lead, although X Education gets a lot of leads, its lead conversion rate is very poor. The task is to build a model wherein you need to assign a lead score to each of the leads such that the customers with higher lead score have a higher conversion chance and the customers with lower lead score have a lower conversion chance. The CEO, in particular, has given a ballpark of the target lead conversion rate to be around 80%.\n",
    "\n",
    "Aproach:\n",
    "\n",
    "With around 37 variables , we need predict whether a lead can be able to opt for the course offered , this is termed as lead conversion\n",
    "\n",
    "\n",
    "### Step 1: Importing and Merging Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9240, 37)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead_data=pd.read_csv(r'C:\\Users\\gdasari\\OneDrive - Majid Al Futtaim Ventures\\01 MY DOCS\\11 DATA SCIENCE\\GroupStudy\\Leads.csv')\n",
    "\n",
    "lead_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Inspecting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9240 entries, 0 to 9239\n",
      "Data columns (total 37 columns):\n",
      "Prospect ID                                      9240 non-null object\n",
      "Lead Number                                      9240 non-null int64\n",
      "Lead Origin                                      9240 non-null object\n",
      "Lead Source                                      9204 non-null object\n",
      "Do Not Email                                     9240 non-null object\n",
      "Do Not Call                                      9240 non-null object\n",
      "Converted                                        9240 non-null int64\n",
      "TotalVisits                                      9103 non-null float64\n",
      "Total Time Spent on Website                      9240 non-null int64\n",
      "Page Views Per Visit                             9103 non-null float64\n",
      "Last Activity                                    9137 non-null object\n",
      "Country                                          6779 non-null object\n",
      "Specialization                                   7802 non-null object\n",
      "How did you hear about X Education               7033 non-null object\n",
      "What is your current occupation                  6550 non-null object\n",
      "What matters most to you in choosing a course    6531 non-null object\n",
      "Search                                           9240 non-null object\n",
      "Magazine                                         9240 non-null object\n",
      "Newspaper Article                                9240 non-null object\n",
      "X Education Forums                               9240 non-null object\n",
      "Newspaper                                        9240 non-null object\n",
      "Digital Advertisement                            9240 non-null object\n",
      "Through Recommendations                          9240 non-null object\n",
      "Receive More Updates About Our Courses           9240 non-null object\n",
      "Tags                                             5887 non-null object\n",
      "Lead Quality                                     4473 non-null object\n",
      "Update me on Supply Chain Content                9240 non-null object\n",
      "Get updates on DM Content                        9240 non-null object\n",
      "Lead Profile                                     6531 non-null object\n",
      "City                                             7820 non-null object\n",
      "Asymmetrique Activity Index                      5022 non-null object\n",
      "Asymmetrique Profile Index                       5022 non-null object\n",
      "Asymmetrique Activity Score                      5022 non-null float64\n",
      "Asymmetrique Profile Score                       5022 non-null float64\n",
      "I agree to pay the amount through cheque         9240 non-null object\n",
      "A free copy of Mastering The Interview           9240 non-null object\n",
      "Last Notable Activity                            9240 non-null object\n",
      "dtypes: float64(4), int64(3), object(30)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "lead_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lead Number</th>\n",
       "      <th>Converted</th>\n",
       "      <th>TotalVisits</th>\n",
       "      <th>Total Time Spent on Website</th>\n",
       "      <th>Page Views Per Visit</th>\n",
       "      <th>Asymmetrique Activity Score</th>\n",
       "      <th>Asymmetrique Profile Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>9240.000000</td>\n",
       "      <td>9240.000000</td>\n",
       "      <td>9103.000000</td>\n",
       "      <td>9240.000000</td>\n",
       "      <td>9103.000000</td>\n",
       "      <td>5022.000000</td>\n",
       "      <td>5022.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>617188.435606</td>\n",
       "      <td>0.385390</td>\n",
       "      <td>3.445238</td>\n",
       "      <td>487.698268</td>\n",
       "      <td>2.362820</td>\n",
       "      <td>14.306252</td>\n",
       "      <td>16.344883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>23405.995698</td>\n",
       "      <td>0.486714</td>\n",
       "      <td>4.854853</td>\n",
       "      <td>548.021466</td>\n",
       "      <td>2.161418</td>\n",
       "      <td>1.386694</td>\n",
       "      <td>1.811395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>579533.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>596484.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>615479.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>637387.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>660737.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>2272.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Lead Number    Converted  TotalVisits  Total Time Spent on Website  \\\n",
       "count    9240.000000  9240.000000  9103.000000                  9240.000000   \n",
       "mean   617188.435606     0.385390     3.445238                   487.698268   \n",
       "std     23405.995698     0.486714     4.854853                   548.021466   \n",
       "min    579533.000000     0.000000     0.000000                     0.000000   \n",
       "25%    596484.500000     0.000000     1.000000                    12.000000   \n",
       "50%    615479.000000     0.000000     3.000000                   248.000000   \n",
       "75%    637387.250000     1.000000     5.000000                   936.000000   \n",
       "max    660737.000000     1.000000   251.000000                  2272.000000   \n",
       "\n",
       "       Page Views Per Visit  Asymmetrique Activity Score  \\\n",
       "count           9103.000000                  5022.000000   \n",
       "mean               2.362820                    14.306252   \n",
       "std                2.161418                     1.386694   \n",
       "min                0.000000                     7.000000   \n",
       "25%                1.000000                    14.000000   \n",
       "50%                2.000000                    14.000000   \n",
       "75%                3.000000                    15.000000   \n",
       "max               55.000000                    18.000000   \n",
       "\n",
       "       Asymmetrique Profile Score  \n",
       "count                 5022.000000  \n",
       "mean                    16.344883  \n",
       "std                      1.811395  \n",
       "min                     11.000000  \n",
       "25%                     15.000000  \n",
       "50%                     16.000000  \n",
       "75%                     18.000000  \n",
       "max                     20.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Data Cleaning\n",
    "\n",
    "\n",
    "Most of the variables are user entries , seems to be from website so the fields with no entry have \"Select\" as value. For modelling we would need to replace thse values with NAN \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Replace Select with NAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "lead_data=lead_data.replace(\"Select\",np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Prospect ID</th>\n",
       "      <th>Lead Number</th>\n",
       "      <th>Lead Origin</th>\n",
       "      <th>Lead Source</th>\n",
       "      <th>Do Not Email</th>\n",
       "      <th>Do Not Call</th>\n",
       "      <th>Converted</th>\n",
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       "      <th>Page Views Per Visit</th>\n",
       "      <th>...</th>\n",
       "      <th>Get updates on DM Content</th>\n",
       "      <th>Lead Profile</th>\n",
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       "      <th>Asymmetrique Profile Index</th>\n",
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       "      <th>Asymmetrique Profile Score</th>\n",
       "      <th>I agree to pay the amount through cheque</th>\n",
       "      <th>A free copy of Mastering The Interview</th>\n",
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       "      <td>0</td>\n",
       "      <td>7927b2df-8bba-4d29-b9a2-b6e0beafe620</td>\n",
       "      <td>660737</td>\n",
       "      <td>API</td>\n",
       "      <td>Olark Chat</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>02.Medium</td>\n",
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       "      <td>15.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Modified</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
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       "      <td>660728</td>\n",
       "      <td>API</td>\n",
       "      <td>Organic Search</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>674</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>8cc8c611-a219-4f35-ad23-fdfd2656bd8a</td>\n",
       "      <td>660727</td>\n",
       "      <td>Landing Page Submission</td>\n",
       "      <td>Direct Traffic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1532</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Potential Lead</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>01.High</td>\n",
       "      <td>14.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Email Opened</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0cc2df48-7cf4-4e39-9de9-19797f9b38cc</td>\n",
       "      <td>660719</td>\n",
       "      <td>Landing Page Submission</td>\n",
       "      <td>Direct Traffic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>305</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>01.High</td>\n",
       "      <td>13.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Modified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3256f628-e534-4826-9d63-4a8b88782852</td>\n",
       "      <td>660681</td>\n",
       "      <td>Landing Page Submission</td>\n",
       "      <td>Google</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1428</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>01.High</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Modified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 37 columns</p>\n",
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      "text/plain": [
       "                            Prospect ID  Lead Number              Lead Origin  \\\n",
       "0  7927b2df-8bba-4d29-b9a2-b6e0beafe620       660737                      API   \n",
       "1  2a272436-5132-4136-86fa-dcc88c88f482       660728                      API   \n",
       "2  8cc8c611-a219-4f35-ad23-fdfd2656bd8a       660727  Landing Page Submission   \n",
       "3  0cc2df48-7cf4-4e39-9de9-19797f9b38cc       660719  Landing Page Submission   \n",
       "4  3256f628-e534-4826-9d63-4a8b88782852       660681  Landing Page Submission   \n",
       "\n",
       "      Lead Source Do Not Email Do Not Call  Converted  TotalVisits  \\\n",
       "0      Olark Chat           No          No          0          0.0   \n",
       "1  Organic Search           No          No          0          5.0   \n",
       "2  Direct Traffic           No          No          1          2.0   \n",
       "3  Direct Traffic           No          No          0          1.0   \n",
       "4          Google           No          No          1          2.0   \n",
       "\n",
       "   Total Time Spent on Website  Page Views Per Visit  ...  \\\n",
       "0                            0                   0.0  ...   \n",
       "1                          674                   2.5  ...   \n",
       "2                         1532                   2.0  ...   \n",
       "3                          305                   1.0  ...   \n",
       "4                         1428                   1.0  ...   \n",
       "\n",
       "  Get updates on DM Content    Lead Profile    City  \\\n",
       "0                        No             NaN     NaN   \n",
       "1                        No             NaN     NaN   \n",
       "2                        No  Potential Lead  Mumbai   \n",
       "3                        No             NaN  Mumbai   \n",
       "4                        No             NaN  Mumbai   \n",
       "\n",
       "  Asymmetrique Activity Index Asymmetrique Profile Index  \\\n",
       "0                   02.Medium                  02.Medium   \n",
       "1                   02.Medium                  02.Medium   \n",
       "2                   02.Medium                    01.High   \n",
       "3                   02.Medium                    01.High   \n",
       "4                   02.Medium                    01.High   \n",
       "\n",
       "  Asymmetrique Activity Score Asymmetrique Profile Score  \\\n",
       "0                        15.0                       15.0   \n",
       "1                        15.0                       15.0   \n",
       "2                        14.0                       20.0   \n",
       "3                        13.0                       17.0   \n",
       "4                        15.0                       18.0   \n",
       "\n",
       "  I agree to pay the amount through cheque  \\\n",
       "0                                       No   \n",
       "1                                       No   \n",
       "2                                       No   \n",
       "3                                       No   \n",
       "4                                       No   \n",
       "\n",
       "  A free copy of Mastering The Interview Last Notable Activity  \n",
       "0                                     No              Modified  \n",
       "1                                     No          Email Opened  \n",
       "2                                    Yes          Email Opened  \n",
       "3                                     No              Modified  \n",
       "4                                     No              Modified  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Handling missing values\n",
    "\n",
    "we will identify the % of missing values for each feild \n",
    "\n",
    "1. drop those feilds with more than 45% missing values\n",
    "2. replacing the NaN values with most occuring values \n",
    "3. If there is no obvous most occuring value then simply replace NaN with \"Unknown\"  \n",
    "4. Dropping the records if the missing % value is lesser than 2%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prospect ID                                       0.00\n",
       "I agree to pay the amount through cheque          0.00\n",
       "Get updates on DM Content                         0.00\n",
       "Update me on Supply Chain Content                 0.00\n",
       "Receive More Updates About Our Courses            0.00\n",
       "Through Recommendations                           0.00\n",
       "Digital Advertisement                             0.00\n",
       "Newspaper                                         0.00\n",
       "X Education Forums                                0.00\n",
       "A free copy of Mastering The Interview            0.00\n",
       "Magazine                                          0.00\n",
       "Search                                            0.00\n",
       "Newspaper Article                                 0.00\n",
       "Last Notable Activity                             0.00\n",
       "Total Time Spent on Website                       0.00\n",
       "Converted                                         0.00\n",
       "Do Not Call                                       0.00\n",
       "Do Not Email                                      0.00\n",
       "Lead Number                                       0.00\n",
       "Lead Origin                                       0.00\n",
       "Lead Source                                       0.39\n",
       "Last Activity                                     1.11\n",
       "Page Views Per Visit                              1.48\n",
       "TotalVisits                                       1.48\n",
       "Country                                          26.63\n",
       "What is your current occupation                  29.11\n",
       "What matters most to you in choosing a course    29.32\n",
       "Tags                                             36.29\n",
       "Specialization                                   36.58\n",
       "City                                             39.71\n",
       "Asymmetrique Activity Index                      45.65\n",
       "Asymmetrique Profile Index                       45.65\n",
       "Asymmetrique Activity Score                      45.65\n",
       "Asymmetrique Profile Score                       45.65\n",
       "Lead Quality                                     51.59\n",
       "Lead Profile                                     74.19\n",
       "How did you hear about X Education               78.46\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the percentage of missing values\n",
    "round(100*(lead_data.isnull().sum()/len(lead_data.index)), 2).sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. drop those feilds with more than 45% missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "delete_columns=['Asymmetrique Activity Index'\n",
    "                ,'Asymmetrique Profile Index'\n",
    "                ,'Asymmetrique Activity Score'\n",
    "                ,'Asymmetrique Profile Score'\n",
    "                ,'Lead Quality'\n",
    "                ,'Lead Profile'\n",
    "                ,'How did you hear about X Education']\n",
    "\n",
    "lead_data=lead_data.drop(delete_columns,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prospect ID                                       0.00\n",
       "I agree to pay the amount through cheque          0.00\n",
       "Get updates on DM Content                         0.00\n",
       "Update me on Supply Chain Content                 0.00\n",
       "Receive More Updates About Our Courses            0.00\n",
       "Through Recommendations                           0.00\n",
       "Digital Advertisement                             0.00\n",
       "Newspaper                                         0.00\n",
       "X Education Forums                                0.00\n",
       "Newspaper Article                                 0.00\n",
       "Magazine                                          0.00\n",
       "Search                                            0.00\n",
       "A free copy of Mastering The Interview            0.00\n",
       "Last Notable Activity                             0.00\n",
       "Total Time Spent on Website                       0.00\n",
       "Converted                                         0.00\n",
       "Do Not Call                                       0.00\n",
       "Do Not Email                                      0.00\n",
       "Lead Origin                                       0.00\n",
       "Lead Number                                       0.00\n",
       "Lead Source                                       0.39\n",
       "Last Activity                                     1.11\n",
       "Page Views Per Visit                              1.48\n",
       "TotalVisits                                       1.48\n",
       "Country                                          26.63\n",
       "What is your current occupation                  29.11\n",
       "What matters most to you in choosing a course    29.32\n",
       "Tags                                             36.29\n",
       "Specialization                                   36.58\n",
       "City                                             39.71\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the percentage of missing values\n",
    "round(100*(lead_data.isnull().sum()/len(lead_data.index)), 2).sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Replacing the NaN values with most occuring values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ee1e8acfc8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# City\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,8))\n",
    "\n",
    "\n",
    "sns.countplot(lead_data.City)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City\n",
       "Mumbai                         34.87\n",
       "Other Cities                    7.42\n",
       "Other Cities of Maharashtra     4.95\n",
       "Other Metro Cities              4.11\n",
       "Thane & Outskirts               8.14\n",
       "Tier II Cities                  0.80\n",
       "Name: Prospect ID, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "City=round((lead_data.groupby('City').agg('count')/len(lead_data))*100,2)\n",
    "\n",
    "City['Prospect ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 35% of the leads are from Mumbai , so we can map the NAN values of City to Mumbai\n",
    "\n",
    "lead_data['City']=lead_data['City'].replace(np.nan,'Mumbai')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prospect ID                                       0.00\n",
       "I agree to pay the amount through cheque          0.00\n",
       "City                                              0.00\n",
       "Get updates on DM Content                         0.00\n",
       "Update me on Supply Chain Content                 0.00\n",
       "Receive More Updates About Our Courses            0.00\n",
       "Through Recommendations                           0.00\n",
       "Digital Advertisement                             0.00\n",
       "Newspaper                                         0.00\n",
       "X Education Forums                                0.00\n",
       "Newspaper Article                                 0.00\n",
       "Magazine                                          0.00\n",
       "Search                                            0.00\n",
       "A free copy of Mastering The Interview            0.00\n",
       "Last Notable Activity                             0.00\n",
       "Total Time Spent on Website                       0.00\n",
       "Converted                                         0.00\n",
       "Do Not Call                                       0.00\n",
       "Do Not Email                                      0.00\n",
       "Lead Origin                                       0.00\n",
       "Lead Number                                       0.00\n",
       "Lead Source                                       0.39\n",
       "Last Activity                                     1.11\n",
       "Page Views Per Visit                              1.48\n",
       "TotalVisits                                       1.48\n",
       "Country                                          26.63\n",
       "What is your current occupation                  29.11\n",
       "What matters most to you in choosing a course    29.32\n",
       "Tags                                             36.29\n",
       "Specialization                                   36.58\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the percentage of missing values\n",
    "round(100*(lead_data.isnull().sum()/len(lead_data.index)), 2).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Business Administration'),\n",
       " Text(0, 0, 'Media and Advertising'),\n",
       " Text(0, 0, 'Supply Chain Management'),\n",
       " Text(0, 0, 'IT Projects Management'),\n",
       " Text(0, 0, 'Finance Management'),\n",
       " Text(0, 0, 'Travel and Tourism'),\n",
       " Text(0, 0, 'Human Resource Management'),\n",
       " Text(0, 0, 'Marketing Management'),\n",
       " Text(0, 0, 'Banking, Investment And Insurance'),\n",
       " Text(0, 0, 'International Business'),\n",
       " Text(0, 0, 'E-COMMERCE'),\n",
       " Text(0, 0, 'Operations Management'),\n",
       " Text(0, 0, 'Retail Management'),\n",
       " Text(0, 0, 'Services Excellence'),\n",
       " Text(0, 0, 'Hospitality Management'),\n",
       " Text(0, 0, 'Rural and Agribusiness'),\n",
       " Text(0, 0, 'Healthcare Management'),\n",
       " Text(0, 0, 'E-Business')]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Specialization \n",
    "\n",
    "chart=sns.countplot(lead_data.Specialization)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So , the specilization is distributed across mulitple values , we can put the Nan values as 'Unknown'\n",
    "\n",
    "lead_data['Specialization']=lead_data['Specialization'].replace(np.nan,'Unknown')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prospect ID                                       0.00\n",
       "I agree to pay the amount through cheque          0.00\n",
       "City                                              0.00\n",
       "Get updates on DM Content                         0.00\n",
       "Update me on Supply Chain Content                 0.00\n",
       "Receive More Updates About Our Courses            0.00\n",
       "Through Recommendations                           0.00\n",
       "Digital Advertisement                             0.00\n",
       "Newspaper                                         0.00\n",
       "X Education Forums                                0.00\n",
       "Newspaper Article                                 0.00\n",
       "Magazine                                          0.00\n",
       "Search                                            0.00\n",
       "A free copy of Mastering The Interview            0.00\n",
       "Last Notable Activity                             0.00\n",
       "Specialization                                    0.00\n",
       "Total Time Spent on Website                       0.00\n",
       "Converted                                         0.00\n",
       "Do Not Call                                       0.00\n",
       "Do Not Email                                      0.00\n",
       "Lead Origin                                       0.00\n",
       "Lead Number                                       0.00\n",
       "Lead Source                                       0.39\n",
       "Last Activity                                     1.11\n",
       "Page Views Per Visit                              1.48\n",
       "TotalVisits                                       1.48\n",
       "Country                                          26.63\n",
       "What is your current occupation                  29.11\n",
       "What matters most to you in choosing a course    29.32\n",
       "Tags                                             36.29\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the percentage of missing values\n",
    "round(100*(lead_data.isnull().sum()/len(lead_data.index)), 2).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Interested in other courses'),\n",
       " Text(0, 0, 'Ringing'),\n",
       " Text(0, 0, 'Will revert after reading the email'),\n",
       " Text(0, 0, 'Lost to EINS'),\n",
       " Text(0, 0, 'In confusion whether part time or DLP'),\n",
       " Text(0, 0, 'Busy'),\n",
       " Text(0, 0, 'switched off'),\n",
       " Text(0, 0, 'in touch with EINS'),\n",
       " Text(0, 0, 'Already a student'),\n",
       " Text(0, 0, 'Diploma holder (Not Eligible)'),\n",
       " Text(0, 0, 'Graduation in progress'),\n",
       " Text(0, 0, 'Closed by Horizzon'),\n",
       " Text(0, 0, 'number not provided'),\n",
       " Text(0, 0, 'opp hangup'),\n",
       " Text(0, 0, 'Not doing further education'),\n",
       " Text(0, 0, 'invalid number'),\n",
       " Text(0, 0, 'wrong number given'),\n",
       " Text(0, 0, 'Interested  in full time MBA'),\n",
       " Text(0, 0, 'Still Thinking'),\n",
       " Text(0, 0, 'Lost to Others'),\n",
       " Text(0, 0, 'Shall take in the next coming month'),\n",
       " Text(0, 0, 'Lateral student'),\n",
       " Text(0, 0, 'Interested in Next batch'),\n",
       " Text(0, 0, 'Recognition issue (DEC approval)'),\n",
       " Text(0, 0, 'Want to take admission but has financial problems'),\n",
       " Text(0, 0, 'University not recognized')]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tags \n",
    "\n",
    "chart=sns.countplot(lead_data.Tags)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# smilar to Country , most of the values in Tags is related to a single value 'Will revert after reading the email' so we shall map the Nan values accordingly\n",
    "\n",
    "lead_data['Tags']=lead_data['Tags'].replace(np.nan,'Will revert after reading the email')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prospect ID                                       0.00\n",
       "I agree to pay the amount through cheque          0.00\n",
       "City                                              0.00\n",
       "Get updates on DM Content                         0.00\n",
       "Update me on Supply Chain Content                 0.00\n",
       "Tags                                              0.00\n",
       "Receive More Updates About Our Courses            0.00\n",
       "Through Recommendations                           0.00\n",
       "Digital Advertisement                             0.00\n",
       "Newspaper                                         0.00\n",
       "X Education Forums                                0.00\n",
       "Newspaper Article                                 0.00\n",
       "Magazine                                          0.00\n",
       "Search                                            0.00\n",
       "Last Notable Activity                             0.00\n",
       "Specialization                                    0.00\n",
       "Total Time Spent on Website                       0.00\n",
       "Converted                                         0.00\n",
       "Do Not Call                                       0.00\n",
       "Do Not Email                                      0.00\n",
       "Lead Origin                                       0.00\n",
       "Lead Number                                       0.00\n",
       "A free copy of Mastering The Interview            0.00\n",
       "Lead Source                                       0.39\n",
       "Last Activity                                     1.11\n",
       "Page Views Per Visit                              1.48\n",
       "TotalVisits                                       1.48\n",
       "Country                                          26.63\n",
       "What is your current occupation                  29.11\n",
       "What matters most to you in choosing a course    29.32\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the percentage of missing values\n",
    "round(100*(lead_data.isnull().sum()/len(lead_data.index)), 2).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Better Career Prospects'),\n",
       " Text(0, 0, 'Flexibility & Convenience'),\n",
       " Text(0, 0, 'Other')]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What matters most to you in choosing a course \n",
    "\n",
    "chart=sns.countplot(lead_data['What matters most to you in choosing a course'])\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# smilar to Country , most of the values here is related to a single value 'Better Career Prospects' so we shall map the Nan values accordingly\n",
    "\n",
    "lead_data['What matters most to you in choosing a course']=lead_data['What matters most to you in choosing a course'].replace(np.nan,'Better Career Prospects')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prospect ID                                       0.00\n",
       "I agree to pay the amount through cheque          0.00\n",
       "City                                              0.00\n",
       "Get updates on DM Content                         0.00\n",
       "Update me on Supply Chain Content                 0.00\n",
       "Tags                                              0.00\n",
       "Receive More Updates About Our Courses            0.00\n",
       "Through Recommendations                           0.00\n",
       "Digital Advertisement                             0.00\n",
       "Newspaper                                         0.00\n",
       "X Education Forums                                0.00\n",
       "Newspaper Article                                 0.00\n",
       "Magazine                                          0.00\n",
       "Search                                            0.00\n",
       "What matters most to you in choosing a course     0.00\n",
       "Specialization                                    0.00\n",
       "Total Time Spent on Website                       0.00\n",
       "Converted                                         0.00\n",
       "Do Not Call                                       0.00\n",
       "Do Not Email                                      0.00\n",
       "Lead Origin                                       0.00\n",
       "Lead Number                                       0.00\n",
       "A free copy of Mastering The Interview            0.00\n",
       "Last Notable Activity                             0.00\n",
       "Lead Source                                       0.39\n",
       "Last Activity                                     1.11\n",
       "Page Views Per Visit                              1.48\n",
       "TotalVisits                                       1.48\n",
       "Country                                          26.63\n",
       "What is your current occupation                  29.11\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the percentage of missing values\n",
    "round(100*(lead_data.isnull().sum()/len(lead_data.index)), 2).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Unemployed'),\n",
       " Text(0, 0, 'Student'),\n",
       " Text(0, 0, 'Working Professional'),\n",
       " Text(0, 0, 'Businessman'),\n",
       " Text(0, 0, 'Other'),\n",
       " Text(0, 0, 'Housewife')]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is your current occupation\n",
    "\n",
    "chart=sns.countplot(lead_data['What is your current occupation'])\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clearly we can replace all the Nan values with 'Unemployed'\n",
    "\n",
    "lead_data['What is your current occupation']=lead_data['What is your current occupation'].replace(np.nan,'Unemployed')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prospect ID                                       0.00\n",
       "I agree to pay the amount through cheque          0.00\n",
       "City                                              0.00\n",
       "Get updates on DM Content                         0.00\n",
       "Update me on Supply Chain Content                 0.00\n",
       "Tags                                              0.00\n",
       "Receive More Updates About Our Courses            0.00\n",
       "Through Recommendations                           0.00\n",
       "Digital Advertisement                             0.00\n",
       "Newspaper                                         0.00\n",
       "X Education Forums                                0.00\n",
       "Newspaper Article                                 0.00\n",
       "Magazine                                          0.00\n",
       "Search                                            0.00\n",
       "What matters most to you in choosing a course     0.00\n",
       "What is your current occupation                   0.00\n",
       "Specialization                                    0.00\n",
       "Total Time Spent on Website                       0.00\n",
       "Converted                                         0.00\n",
       "Do Not Call                                       0.00\n",
       "Do Not Email                                      0.00\n",
       "Lead Origin                                       0.00\n",
       "Lead Number                                       0.00\n",
       "A free copy of Mastering The Interview            0.00\n",
       "Last Notable Activity                             0.00\n",
       "Lead Source                                       0.39\n",
       "Last Activity                                     1.11\n",
       "Page Views Per Visit                              1.48\n",
       "TotalVisits                                       1.48\n",
       "Country                                          26.63\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the percentage of missing values\n",
    "round(100*(lead_data.isnull().sum()/len(lead_data.index)), 2).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'India'),\n",
       " Text(0, 0, 'Russia'),\n",
       " Text(0, 0, 'Kuwait'),\n",
       " Text(0, 0, 'Oman'),\n",
       " Text(0, 0, 'United Arab Emirates'),\n",
       " Text(0, 0, 'United States'),\n",
       " Text(0, 0, 'Australia'),\n",
       " Text(0, 0, 'United Kingdom'),\n",
       " Text(0, 0, 'Bahrain'),\n",
       " Text(0, 0, 'Ghana'),\n",
       " Text(0, 0, 'Singapore'),\n",
       " Text(0, 0, 'Qatar'),\n",
       " Text(0, 0, 'Saudi Arabia'),\n",
       " Text(0, 0, 'Belgium'),\n",
       " Text(0, 0, 'France'),\n",
       " Text(0, 0, 'Sri Lanka'),\n",
       " Text(0, 0, 'China'),\n",
       " Text(0, 0, 'Canada'),\n",
       " Text(0, 0, 'Netherlands'),\n",
       " Text(0, 0, 'Sweden'),\n",
       " Text(0, 0, 'Nigeria'),\n",
       " Text(0, 0, 'Hong Kong'),\n",
       " Text(0, 0, 'Germany'),\n",
       " Text(0, 0, 'Asia/Pacific Region'),\n",
       " Text(0, 0, 'Uganda'),\n",
       " Text(0, 0, 'Kenya'),\n",
       " Text(0, 0, 'Italy'),\n",
       " Text(0, 0, 'South Africa'),\n",
       " Text(0, 0, 'Tanzania'),\n",
       " Text(0, 0, 'unknown'),\n",
       " Text(0, 0, 'Malaysia'),\n",
       " Text(0, 0, 'Liberia'),\n",
       " Text(0, 0, 'Switzerland'),\n",
       " Text(0, 0, 'Denmark'),\n",
       " Text(0, 0, 'Philippines'),\n",
       " Text(0, 0, 'Bangladesh'),\n",
       " Text(0, 0, 'Vietnam'),\n",
       " Text(0, 0, 'Indonesia')]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Country\n",
    "\n",
    "chart=sns.countplot(lead_data['Country'])\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can replace all the Nan values with India\n",
    "\n",
    "\n",
    "lead_data['Country']=lead_data['Country'].replace(np.nan,'India')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prospect ID                                      0.00\n",
       "I agree to pay the amount through cheque         0.00\n",
       "City                                             0.00\n",
       "Get updates on DM Content                        0.00\n",
       "Update me on Supply Chain Content                0.00\n",
       "Tags                                             0.00\n",
       "Receive More Updates About Our Courses           0.00\n",
       "Through Recommendations                          0.00\n",
       "Digital Advertisement                            0.00\n",
       "Newspaper                                        0.00\n",
       "X Education Forums                               0.00\n",
       "Newspaper Article                                0.00\n",
       "Magazine                                         0.00\n",
       "Search                                           0.00\n",
       "What matters most to you in choosing a course    0.00\n",
       "What is your current occupation                  0.00\n",
       "Specialization                                   0.00\n",
       "Country                                          0.00\n",
       "Total Time Spent on Website                      0.00\n",
       "Converted                                        0.00\n",
       "Do Not Call                                      0.00\n",
       "Do Not Email                                     0.00\n",
       "Lead Origin                                      0.00\n",
       "Lead Number                                      0.00\n",
       "A free copy of Mastering The Interview           0.00\n",
       "Last Notable Activity                            0.00\n",
       "Lead Source                                      0.39\n",
       "Last Activity                                    1.11\n",
       "Page Views Per Visit                             1.48\n",
       "TotalVisits                                      1.48\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the percentage of missing values\n",
    "round(100*(lead_data.isnull().sum()/len(lead_data.index)), 2).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# so as the TotalVisits, Page Views Per Visit , Last Activity and Lead Source have less than 2% of Nan Values we can remove those rows .\n",
    "\n",
    "lead_data.dropna(inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prospect ID                                      0.0\n",
       "I agree to pay the amount through cheque         0.0\n",
       "City                                             0.0\n",
       "Get updates on DM Content                        0.0\n",
       "Update me on Supply Chain Content                0.0\n",
       "Tags                                             0.0\n",
       "Receive More Updates About Our Courses           0.0\n",
       "Through Recommendations                          0.0\n",
       "Digital Advertisement                            0.0\n",
       "Newspaper                                        0.0\n",
       "X Education Forums                               0.0\n",
       "Newspaper Article                                0.0\n",
       "Magazine                                         0.0\n",
       "Search                                           0.0\n",
       "What matters most to you in choosing a course    0.0\n",
       "What is your current occupation                  0.0\n",
       "Specialization                                   0.0\n",
       "Country                                          0.0\n",
       "Last Activity                                    0.0\n",
       "Page Views Per Visit                             0.0\n",
       "Total Time Spent on Website                      0.0\n",
       "TotalVisits                                      0.0\n",
       "Converted                                        0.0\n",
       "Do Not Call                                      0.0\n",
       "Do Not Email                                     0.0\n",
       "Lead Source                                      0.0\n",
       "Lead Origin                                      0.0\n",
       "Lead Number                                      0.0\n",
       "A free copy of Mastering The Interview           0.0\n",
       "Last Notable Activity                            0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the percentage of missing values\n",
    "round(100*(lead_data.isnull().sum()/len(lead_data.index)), 2).sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Now all the Nan values are removed we can proceed with next steps\n",
    "\n",
    "\n",
    "### Step 4 : Explorartory Data Analysis\n",
    "\n",
    "\n",
    "we would need to analyse the distribution of various values of all the above considered feilds against the \"Converted\" value . \n",
    "\n",
    "Intent is to check if the convertion is really dependent on the variable \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I agree to pay the amount through cheque"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'No')]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(lead_data['I agree to pay the amount through cheque'])\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "    \n",
    "As all the values are \"No\" no conclusion can be made with this .\n",
    "we can even drop this feild "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Mumbai'),\n",
       " Text(0, 0, 'Thane & Outskirts'),\n",
       " Text(0, 0, 'Other Metro Cities'),\n",
       " Text(0, 0, 'Other Cities'),\n",
       " Text(0, 0, 'Other Cities of Maharashtra'),\n",
       " Text(0, 0, 'Tier II Cities')]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='City',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion:\n",
    "    \n",
    "    Most of the leads converted from Mumbai. But we cannot infer from this as most of the leads are also from Mumbai\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get updates on DM Content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'No')]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='Get updates on DM Content',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "    \n",
    "As all the values are \"No\" no conclusion can be made with this .\n",
    "we can even drop this feild "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Update me on Supply Chain Content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'No')]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='Update me on Supply Chain Content',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "\n",
    "As all the values are \"No\" no conclusion can be made with this . we can even drop this feild "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tags                                             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Interested in other courses'),\n",
       " Text(0, 0, 'Ringing'),\n",
       " Text(0, 0, 'Will revert after reading the email'),\n",
       " Text(0, 0, 'Lost to EINS'),\n",
       " Text(0, 0, 'In confusion whether part time or DLP'),\n",
       " Text(0, 0, 'Busy'),\n",
       " Text(0, 0, 'switched off'),\n",
       " Text(0, 0, 'in touch with EINS'),\n",
       " Text(0, 0, 'Already a student'),\n",
       " Text(0, 0, 'Diploma holder (Not Eligible)'),\n",
       " Text(0, 0, 'Graduation in progress'),\n",
       " Text(0, 0, 'number not provided'),\n",
       " Text(0, 0, 'opp hangup'),\n",
       " Text(0, 0, 'Closed by Horizzon'),\n",
       " Text(0, 0, 'Not doing further education'),\n",
       " Text(0, 0, 'invalid number'),\n",
       " Text(0, 0, 'wrong number given'),\n",
       " Text(0, 0, 'Interested  in full time MBA'),\n",
       " Text(0, 0, 'Still Thinking'),\n",
       " Text(0, 0, 'Lost to Others'),\n",
       " Text(0, 0, 'Shall take in the next coming month'),\n",
       " Text(0, 0, 'Lateral student'),\n",
       " Text(0, 0, 'Interested in Next batch'),\n",
       " Text(0, 0, 'Recognition issue (DEC approval)'),\n",
       " Text(0, 0, 'Want to take admission but has financial problems'),\n",
       " Text(0, 0, 'University not recognized')]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='Tags',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "    \n",
    "    As most of the leads have \"revert after reading the email\" , more leads are mapped to this. but to be observed that \n",
    "    more than 50% of the leads are converted with this status as well.\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Receive More Updates About Our Courses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'No')]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='Receive More Updates About Our Courses',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "\n",
    "As all the values are \"No\" no conclusion can be made with this . we can even drop this feild "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Through Recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'No'), Text(0, 0, 'Yes')]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='Through Recommendations',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "As most of the values are \"No\" no conclusion can be made with this . we can even drop this feild \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Digital Advertisement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'No'), Text(0, 0, 'Yes')]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='Digital Advertisement',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "    \n",
    "    \n",
    "As most of the values are \"No\" no conclusion can be made with this . we can even drop this feild \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Newspaper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'No'), Text(0, 0, 'Yes')]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='Newspaper',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "    \n",
    "    \n",
    "As most of the values are \"No\" no conclusion can be made with this . we can even drop this feild \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### X Education Forums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'No'), Text(0, 0, 'Yes')]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='X Education Forums',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "    \n",
    "    \n",
    "As most of the values are \"No\" no conclusion can be made with this . we can even drop this feild \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Newspaper Article"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'No'), Text(0, 0, 'Yes')]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='Newspaper Article',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "    \n",
    "    \n",
    "As most of the values are \"No\" no conclusion can be made with this . we can even drop this feild \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Magazine                                         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'No')]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='Magazine',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "    \n",
    "    \n",
    "As All of the values are \"No\" no conclusion can be made with this . we can even drop this feild \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Search                                           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'No'), Text(0, 0, 'Yes')]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='Search',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "    \n",
    "    \n",
    "As most of the values are \"No\" no conclusion can be made with this . we can even drop this feild \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What matters most to you in choosing a course "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Better Career Prospects'),\n",
       " Text(0, 0, 'Flexibility & Convenience'),\n",
       " Text(0, 0, 'Other')]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='What matters most to you in choosing a course',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "    \n",
    "    \n",
    "As most of the values are \"Better Career Prospects\" no conclusion can be made with this . we can even drop this feild \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is your current occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Unemployed'),\n",
       " Text(0, 0, 'Student'),\n",
       " Text(0, 0, 'Working Professional'),\n",
       " Text(0, 0, 'Businessman'),\n",
       " Text(0, 0, 'Other'),\n",
       " Text(0, 0, 'Housewife')]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='What is your current occupation',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclustion : \n",
    "    \n",
    "    Unemployed leads though are high in total count the converion rate is low\n",
    "    Student are ver low in count \n",
    "    Working Profewssionals have high convertion rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Specialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Unknown'),\n",
       " Text(0, 0, 'Business Administration'),\n",
       " Text(0, 0, 'Media and Advertising'),\n",
       " Text(0, 0, 'Supply Chain Management'),\n",
       " Text(0, 0, 'IT Projects Management'),\n",
       " Text(0, 0, 'Finance Management'),\n",
       " Text(0, 0, 'Travel and Tourism'),\n",
       " Text(0, 0, 'Human Resource Management'),\n",
       " Text(0, 0, 'Marketing Management'),\n",
       " Text(0, 0, 'Banking, Investment And Insurance'),\n",
       " Text(0, 0, 'International Business'),\n",
       " Text(0, 0, 'E-COMMERCE'),\n",
       " Text(0, 0, 'Operations Management'),\n",
       " Text(0, 0, 'Retail Management'),\n",
       " Text(0, 0, 'Services Excellence'),\n",
       " Text(0, 0, 'Hospitality Management'),\n",
       " Text(0, 0, 'Rural and Agribusiness'),\n",
       " Text(0, 0, 'Healthcare Management'),\n",
       " Text(0, 0, 'E-Business')]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='Specialization',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "    \n",
    "    Seems many courses like 'buisiness adminitration','banking investment and insurance','finance management' and few \n",
    "    others have high convertion\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'India'),\n",
       " Text(0, 0, 'Russia'),\n",
       " Text(0, 0, 'Kuwait'),\n",
       " Text(0, 0, 'Oman'),\n",
       " Text(0, 0, 'United Arab Emirates'),\n",
       " Text(0, 0, 'United States'),\n",
       " Text(0, 0, 'Australia'),\n",
       " Text(0, 0, 'United Kingdom'),\n",
       " Text(0, 0, 'Bahrain'),\n",
       " Text(0, 0, 'Ghana'),\n",
       " Text(0, 0, 'Singapore'),\n",
       " Text(0, 0, 'Qatar'),\n",
       " Text(0, 0, 'Saudi Arabia'),\n",
       " Text(0, 0, 'Belgium'),\n",
       " Text(0, 0, 'France'),\n",
       " Text(0, 0, 'Sri Lanka'),\n",
       " Text(0, 0, 'China'),\n",
       " Text(0, 0, 'Canada'),\n",
       " Text(0, 0, 'Netherlands'),\n",
       " Text(0, 0, 'Sweden'),\n",
       " Text(0, 0, 'Nigeria'),\n",
       " Text(0, 0, 'Hong Kong'),\n",
       " Text(0, 0, 'Germany'),\n",
       " Text(0, 0, 'Asia/Pacific Region'),\n",
       " Text(0, 0, 'Uganda'),\n",
       " Text(0, 0, 'Kenya'),\n",
       " Text(0, 0, 'Italy'),\n",
       " Text(0, 0, 'South Africa'),\n",
       " Text(0, 0, 'Tanzania'),\n",
       " Text(0, 0, 'unknown'),\n",
       " Text(0, 0, 'Malaysia'),\n",
       " Text(0, 0, 'Liberia'),\n",
       " Text(0, 0, 'Switzerland'),\n",
       " Text(0, 0, 'Denmark'),\n",
       " Text(0, 0, 'Philippines'),\n",
       " Text(0, 0, 'Bangladesh'),\n",
       " Text(0, 0, 'Vietnam'),\n",
       " Text(0, 0, 'Indonesia')]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='Country',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "    \n",
    "    \n",
    "As most of the values are \"India\" no conclusion can be made with this , we can drop this feild"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Last Activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Page Visited on Website'),\n",
       " Text(0, 0, 'Email Opened'),\n",
       " Text(0, 0, 'Unreachable'),\n",
       " Text(0, 0, 'Converted to Lead'),\n",
       " Text(0, 0, 'Olark Chat Conversation'),\n",
       " Text(0, 0, 'Email Bounced'),\n",
       " Text(0, 0, 'Email Link Clicked'),\n",
       " Text(0, 0, 'Form Submitted on Website'),\n",
       " Text(0, 0, 'Unsubscribed'),\n",
       " Text(0, 0, 'Had a Phone Conversation'),\n",
       " Text(0, 0, 'View in browser link Clicked'),\n",
       " Text(0, 0, 'SMS Sent'),\n",
       " Text(0, 0, 'Visited Booth in Tradeshow'),\n",
       " Text(0, 0, 'Approached upfront'),\n",
       " Text(0, 0, 'Resubscribed to emails'),\n",
       " Text(0, 0, 'Email Received'),\n",
       " Text(0, 0, 'Email Marked Spam')]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='Last Activity',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion:\n",
    "    \n",
    "    Last Activity SMS sent has very high convertion rate\n",
    "    last Activity Email Opened though has high count but has relateively less convertion "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Page Views Per Visit\n",
    "\n",
    "As this is numeric field we shall check the quantile distribution using box plots.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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upLe3F4De3l66uroqrmh6GzX4I+K8iHgmIq4E/pJsjX3vkzsFNTY21mybVWXZsmWD2suXL6+okjSM+Ddf0myylTlbgM3AtRFxe70Ks4k3ZE+FV7TNqnLooYcOas+bN6+iStJQq8ffAbSShf7ZwN/VpSIrzdB7Norcw2FWD+vXrx/U9hh/uWr9W//EiPhlAEnXAnfVpyQrS/8Y6khtM0tDrR7/vv4nEeGEmAZmz55ds21maajV4z9J0q78uYA5eVtARMRhpVdnE2rfvn0122ZVmTdvHs8+++xL7fnz51dYzfQ3YvBHREM9C7Hy9fX11WybVWVg6AM888wzFVWShqJr9dg04OmcNlktWbKkZtsmloM/IUP/Mh1//PHVFGI2xMqVKwe129vbK6okDQ7+hGzZsmVQ+8c//nFFlZgNNnT6ZkdHR0WVpMHBb2aV27p1a822TSwHv5lV7rDDBk8S9J275XLwm1nldu3aNag9dJaPTSwHv5lZYkoLfklflPSkpPsGHDtCUpekh/LHw8u6vpmZDa/MHv+XgbYhxy4Hbo2IpcCtedvqZOi46eGH+/euTQ5DfxabmpoqqiQNpQV/RHyXbHP2gc4lW/WT/NHr+tfR0HHTnTt3VlSJ2WBDfxa99WK56j3GvygiHgfIH48c6YWSVknqltS9Y4e3+DUzmyiT9sPdiFgbEa0R0bpw4cKqyzEzmzbqHfxPSDoaIH98ss7XN7NJaOi6UTNnzqyokjTUO/hvBvoX4WgHbqrz9c1sEhq6KZCXDC9XmdM5rwfuBF4jaZukS4BPAMslPQQsz9tmZlZHpa3LGxErRvjWW8u6ppmZjc4Lspslbs2aNa9YubXeZs2axd69ewe1L7vsskpqaWlp4f3vf38l166XSTurx8zSccIJJ9Rs28Ryj98scZOld3vWWWexd+9eWlpauPrqq6suZ1pz8JvZpNDfy3fol89DPWZmiXHwm5klxsFvZpYYj/HXyWSYMjccT5kzS497/GZmiXGPv04mQ+/2C1/4AuvXr3+pvXLlSi6++OIKKzKzKrjHn5BLL710UNuhb5YmB39iFixYAGS9fTNLk4d6EtPc3Exzc7N7+2YJc4/fzCwxDn4zs8Q4+M3MEuMxfrMKTdYb+6rQ/9+hqpsKJ5syb3J08JtVaMuWLTx0/w9YPLev6lIqN2tfNgCx55Huiiup3qO7G0o9fxLB717Vy9yretlkWTZi8dw+rjh1V9Vl2CTyV3cfVur5kwj+LVu2cM99D9B38BFVl1K5GXsDgE0PP1FxJdVqeP7pqkswq0wSwQ/Qd/ARvPALb6+6DJsk5jz4rapLMKuMZ/WYmSUmiR7/9u3baXj+Wffy7CUNz/ewfXtv1WWwfft2fvZcQ+ljuja1PPJcA4ds317a+d3jNzNLTBI9/ubmZv53T6PH+O0lcx78Fs3Ni6oug+bmZvb0Pu5ZPTbIX919GAc1N5d2/iSCH7JZHB7qgRkvZgGzf3baQwvZrJ7qg9+sCkkEf0tLS9UlTBpbtjwHQMvxqYfeIv9cWLIqCX5JbcDVQAPwTxHxiTKvNxlu0pks+m/cuvrqqyuuxMyqUvcPdyU1AJ8FzgZOBFZIOrHedZiZpaqKHv9pwJaIeBhA0leBc4EfVVBL3UyWZSMmy5INk2W5hMng0d3VTud84vkZvNinyq4/2cxuCBYdvL/SGh7d3cDSEs9fRfA3Az8d0N4G/MrQF0laBawCWLx4cX0qS8CcOXOqLsEGmAyfMzRs386MF16ouoxJo2HOnFJn1BSxlHJ/NhQRpZ182AtK7wbeFhG/n7d/DzgtIkbs/rW2tkZ3t1fsMzMbC0mbIqJ16PEqbuDaBhw7oH0M8FgFdZiZJamK4P8+sFTScZJmAb8D3FxBHWZmSar7GH9E9Er6Q+DbZNM5vxgR99e7DjOzVFUyjz8ivgX4Nlozswp4kTYzs8Q4+M3MEuPgNzNLjIPfzCwxdb+Bazwk7QAeqbqOaWQB8FTVRZgNwz+bE+vnImLh0INTIvhtYknqHu5uPrOq+WezPjzUY2aWGAe/mVliHPxpWlt1AWYj8M9mHXiM38wsMe7xm5klxsFvZpYYB39CJLVJ+m9JWyRdXnU9Zv0kfVHSk5Luq7qWFDj4E+FN7m2S+zLQVnURqXDwp+OlTe4jYi/Qv8m9WeUi4rvA01XXkQoHfzqG2+S+2h2lzawSDv50aJhjnstrliAHfzq8yb2ZAQ7+lHiTezMDHPzJiIheoH+T+weAG73JvU0Wkq4H7gReI2mbpEuqrmk685INZmaJcY/fzCwxDn4zs8Q4+M3MEuPgNzNLjIPfzCwxDn6b1iQdJemrkv5H0o8kfUvSz1dQx0WSXjXG9yzxapVWBge/TVuSBHwTuC0iXh0RJwJXAIvqXEcDcBEwpuA3K4uD36aztwD7IuLz/Qci4h7gDkl/I+k+SZslnQ8g6c2SbpP0dUkPSlqvzNmSbuw/R/66W/LnZ0m6U9Ldkr4maW5+fKuk/yvpDmAF0Aqsl3SPpDmSXifpdkmbJH1b0tH5+14n6V5JdwLvq9d/KEuLg9+ms18CNg1z/J3AycBJwDLgb/qDFzgF+COyPQuOB84AuoA3SDokf835wA2SFgB/ASyLiFOBbuADA67zYkS8KSKuy793QUScDPQCa4B3RcTrgC8CH8/f8yVgdUScfsB/erMRNFZdgFkF3gRcHxF9wBOSbgdeD+wC7oqIbQCS7gGWRMQdkjqB35T0deA3gA8Bv072C+I/s1ElZpEtO9DvhhGu/xqyX0pd+fsagMclzQPmR8Tt+ev+mWzjHLMJ5eC36ex+4F3DHB9uiep+ewY87+PlvyM3kA29PA18PyKeyz9D6IqIFSOc62cjHBdw/9BevaT5eKlsqwMP9dh09h3gIEmX9h+Q9HpgJ3C+pAZJC4FfA+4a5Vy3AacCl/JyT/7/AWdIasnPfXCNGUPPAYfmz/8bWCjp9Px9MyX9YkQ8Azwr6U356y4o/kc1K87Bb9NWZCsQngcsz6dz3g9cCXwF+CFwL9kvhw9FxP+Ocq4+YAPZ0MuG/NgOstk610v6Idkvgl8Y4RRfBj6fDx81kP1L5JOS7gXuAd6Yv+69wGfzD3dfGPuf2mx0Xp3TzCwx7vGbmSXGwW9mlhgHv5lZYhz8ZmaJcfCbmSXGwW9mlhgHv5lZYv4/v+2C8QGUOfwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.boxplot(y='Page Views Per Visit',x='Converted',data=lead_data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we have to take out the outliers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "acceptedvalues = lead_data['Page Views Per Visit'].quantile([0.05,0.95]).values\n",
    "lead_data['Page Views Per Visit'][lead_data['Page Views Per Visit'] <= acceptedvalues[0]] = acceptedvalues[0]\n",
    "lead_data['Page Views Per Visit'][lead_data['Page Views Per Visit'] >= acceptedvalues[1]] = acceptedvalues[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEGCAYAAABvtY4XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAATKElEQVR4nO3df5BlZX3n8feH5tcAImsxQdMwGbUVi03CD1tXxJDIGgWjMRp3CaXZRU2mUmvGzmZXy7DZLVMV2azZdSETozu1qCQiajTmh0WBbBQMu0TTQwblx2TTUZEZfw0hEYThxwzf/ePeXpqh+/aZ7nvu7TnzflVN9b3n3vM836HufPrw3Oc8T6oKSVL3HDbuAiRJ7TDgJamjDHhJ6igDXpI6yoCXpI46fNwFLHTiiSfWxo0bx12GJB00tm3bdk9VrV/stTUV8Bs3bmR2dnbcZUjSQSPJXUu95hCNJHWUAS9JHWXAS1JHGfCS1FEGvCR1VKsBn+SEJJ9MsiPJnUnObrM/SdLj2p4meTlwbVW9PsmRwDEt9ydJ6mst4JMcD5wLXAxQVY8Aj7TV31qxZcsW5ubmxlrDrl27AJicnBxrHQBTU1Ns3rx53GWItfHZhLXz+TwUPpttDtE8C9gNfCjJXyf5n0mO3f9NSTYlmU0yu3v37hbLOXTs2bOHPXv2jLsMaVF+PkcnbW34kWQa+EvgnKr6YpLLgfuq6j8udc709HR5J+vqzczMAHD55ZePuRLpyfx8DleSbVU1vdhrbV7B7wR2VtUX+88/CZzVYn+SpAVaC/iq+jZwd5JT+4f+OXBHW/1Jkp6o7Vk0m4Gr+jNovgq8qeX+JEl9rQZ8VW0HFh0bkiS1yztZJamjDHhJ6igDXpI6yoCXpI4y4CWpowx4SeooA16SOsqAl6SOMuAlqaMMeEnqKANekjrKgJekjjLgJamjDHhJ6igDXpI6yoCXpI4y4CWpowx4SeooA16SOsqAl6SOMuAlqaMMeEnqKANekjrKgJekjjq8zcaTfB24H9gH7K2q6Tb7kyQ9rtWA73tpVd0zgn4kSQs4RCNJHdV2wBfw2STbkmxa7A1JNiWZTTK7e/fulsuRpENH2wF/TlWdBVwAvDXJufu/oaq2VtV0VU2vX7++5XIk6dDRasBX1Tf7P78LfBp4YZv9SZIe11rAJzk2yVPmHwMvB25rqz9J0hO1OYvmJODTSeb7+WhVXdtif5KkBVoL+Kr6KnB6W+1LkgZzmqQkdZQBL0kdZcBLUkcZ8JLUUQa8JHWUAS9JHWXAS1JHGfCS1FEGvCR1lAEvSR1lwEtSRy0b8EmOanJMkrS2NLmCv7nhMUnSGrLkapJJng5MAuuSnAmk/9LxwDEjqE2StAqDlgt+BXAxcDLw3gXH7wcuabEmSdIQLBnwVXUlcGWSn62qT42wJknSEAwaonljVX0E2JjkV/d/vareu8hpkqQ1YtAQzbH9n8eNohBJ0nANGqL5H/2fvzG6ciRJw9JkHvx7khyf5Igkf57kniRvHEVxkqSVazIP/uVVdR/wKmAn8Fzg7a1WJUlatSYBf0T/5yuBq6vq3hbrkSQNyaAvWef9WZIdwB7g3yRZDzzUblmSpNVa9gq+qt4JnA1MV9WjwAPAa9ouTJK0OoPmwZ9XVZ9L8roFxxa+5Y+adJBkApgFdlXVq1ZaqCTpwAwaovlx4HPAqxd5rWgY8MAMcCe9NWwkSSMyKOB/D6Cq3rTSxpOcDPwU8G7gSXfDSpLaM2gM/tYk1yd5c5KnrrD9y4B3AI8t9YYkm5LMJpndvXv3CruRJO1vUMBPAv8V+DHg/yb54yQXJlnXpOEkrwK+W1XbBr2vqrZW1XRVTa9fv75x4ZKkwZYM+KraV1XX9YdoTgE+BPwM8LUkVzVo+xzgp5N8HfgYcF6SjwyhZklSA432ZK2qR4A76H1Zeh9wWoNzfq2qTq6qjcDPAZ+rKpc4kKQRGRjwSTYkeXuSW4DPABPAa6rqzJFUJ0lasUHz4P8PvXH4PwQ2VdXsSjupqhuAG1Z6viTpwA2aJvlrwBeqqkZVjCRpeAatB3/jKAuRJA1Xoy9ZJUkHn+W+ZD0syb8cVTGSpOEZGPBV9RjwyyOqRZI0RE2GaK5P8u+TnJLkafN/Wq9MkrQqTTb8eHP/51sXHCvgWcMvR5I0LMsGfFU9cxSFSJKGa9khmiTHJPn1JFv7z5/TX0hMkrSGNRmD/xDwCPDi/vOdwG+2VpEkaSiajME/u6ouTHIRQFXtyX5790kabMuWLczNzY27jDVh/r/DzMzMmCtZG6ampti8eXMrbTcJ+Ef6a8AXQJJnAw+3Uo3UUXNzc/zt7X/NhuP2jbuUsTvy0d7AwcN3rXh5q874xvcnWm2/ScC/C7gWOKW/Dvw5wMUt1iR10obj9nHJWfeNuwytIZfe0u5W1U1m0Xw2yTbgRUCAmaq6p9WqJEmrNmi54B8ALgGmgK8A/7mqvPyQpIPEoFk0vw88AGwBjgN+ZyQVSZKGYtAQzdOr6j/0H1/X39VJknSQGBTwSfJP6I27A0wsfF5V97ZdnCRp5QYF/FOBbTwe8ADzV/GuRSNJa9ygHZ02jrAOSdKQuaOTJHWUAS9JHWXAS1JHNVku+NlJjuo//okkb0tyQvulSZJWo8kV/KeAfUmmgCuAZwIfXe6kJEcn+VKSW5PcnuQ3VlmrJOkANAn4x6pqL/Ba4LKq+rfAMxqc9zBwXlWdDpwBnJ/kRSsvVZJ0IJqsJvlofy34fw28un/siOVOqqoCvr/g/UfQX3K4Da63/TjX236iNtfbltayJgH/JuCXgHdX1deSPBP4SJPGk0zQu1lqCnhfVX1xkfdsAjYBbNiwoWndTzI3N8f22+5k3zFPW3EbXXHYI73fo9u++p0xVzJ+Ew96w7UOXU0C/unAO6vqQYCq+hrwW00ar6p9wBn9L2U/neSHq+q2/d6zFdgKMD09vaor/H3HPI09z3vlappQx6zbcc24S5DGpskY/MXA9iQ3J3lPklf316RprKr+EbgBOP/AS5QkrcSyAV9V/6qqngv8LL0Nt98H7F7uvCTr56dT9rf8exmwY3XlSpKaWnaIJskbgR8DfgS4B/hd4C8atP0M4Mr+OPxhwCeq6jOrqFWSdACajMFfBvwd8AHg81X19SYNV9WXgTNXXpokaTWaDNGcCLwZOBp4d//mpT9ovTJJ0qo0WargeGAD8EPARnrrxD/WblmSpNVqMkRz04I/v1tVO9stSZI0DMsGfFX9KECSY6vqgfZLkiQNQ5MhmrOT3AHc2X9+epLfa70ySdKqNLnR6TLgFcDfA1TVrcC5bRYlSVq9Rht+VNXd+x3a10ItkqQhavIl691JXgxUkiOBt9EfrpEkrV1NruB/CXgrMElvqYIz+s8lSWtYk1k09wBvGEEtkqQhWjLgk7yjqt6TZAuLbNRRVW9rtTJJ0qoMuoKfH2efHUUhkqThGhTwRyU5uqquHFk1kqShGfQl6xuAbyT5/SQX9Jf9lSQdJJYM+Kp6Lb29VP+c3tTIu5O8P4k3OUnSQWDgNMmquq+qrqyqC+ht+LEd2JJk/xufJElrTKM7Wft7sL4OuBB4GvCpNouSJK3eoGmSTwF+BrgIOAv4U+A36e3q9KRpk5KktWXQLJqvAdcB7weurapHR1OSJGkYBgX8hqp6cGSVSJKGatAsGsNdkg5ijb5klSQdfBoHfJJj2yxEkjRcTbbse7Fb9knSwafJFfx/ZwVb9iU5Jcnnk9yZ5PYkM6srVZJ0IJrs6ERV3Z1k4aEmW/btBf5dVd3Sn1O/Lcn1VXXHCuqUJB2g1rbsq6pvAd/qP74/yZ30doVqJeB37drFxIPfY92Oa9poXgepiQf/nl279o67DHbt2sUD909w6S3Hj7sUrSF33T/Bsbt2tdb+SLbsS7IROBP44iKvbUoym2R29+7dB9KsJGmA1rfsS3IcvbVrfqWq7luk/a3AVoDp6ekVL4EwOTnJtx8+nD3Pe+VKm1AHrdtxDZOTJ427DCYnJ3l477e45Kwn/RPQIezSW47nqMnJ1tpfNuCT/M4ih78HzFbVnyxz7hH0wv2qqvqjlZUoSVqJJkM0R9Mblvnb/p8fpbei5FuSXLbUSel9K3sFcGdVvXcItUqSDkCTL1mngPOqai9AkvcDnwV+EvjKgPPOAX4e+EqS7f1jl1SV34JK0gg0CfhJ4Fh6wzL0H/9gVe1L8vBSJ1XVTUCWel2S1K4mAf8eYHuSG+gF9rnApf2lC/5Xi7VJklahySyaK5JcA7yQXsBfUlXf7L/89jaLkyStXNPFxh6id9PSvcCUG29L0trXZJrkLwAzwMn0Nt1+EXAzcF67pUmSVqPJFfwM8ALgrqp6Kb07Ur3lVJLWuCYB/1BVPQSQ5Kiq2gGc2m5ZkqTVajKLZmeSE4A/Bq5P8g/AN5c5R5I0Zk1m0by2//BdST4PPBW4ttWqJEmrtmTAJzma3kqSU/TuWL2iqm4cVWGSpNUZNAZ/JTBNL9wvAP7bSCqSJA3FoCGa06rqRwCSXAF8aTQlSZKGYdAV/KPzD+YXGpMkHTwGXcGfnmR+d4IA6/rPA1RVufeYJK1hSwZ8VU2MshBJ0nA1XYtGknSQMeAlqaMMeEnqKANekjrKgJekjjLgJamjDHhJ6igDXpI6yoCXpI4y4CWpo1oL+CQfTPLdJLe11YckaWltXsF/GDi/xfYlSQM02ZN1RarqC0k2ttX+YiYevJd1O64ZZZdr0mEP9RYBfexoF/ycePBe4KRxlyGNRWsB31SSTcAmgA0bNqy4nampqWGVdNCbm7sfgKlnGWxwkp8NHbLGHvBVtRXYCjA9PV0rbWfz5s1Dq+lgNzMzA8Dll18+5kokjZOzaCSpowx4SeqoNqdJXg3cDJyaZGeSt7TVlyTpydqcRXNRW21LkpbnEI0kdZQBL0kdZcBLUkcZ8JLUUQa8JHWUAS9JHWXAS1JHGfCS1FEGvCR1lAEvSR1lwEtSRxnwktRRBrwkdZQBL0kdZcBLUkcZ8JLUUQa8JHWUAS9JHWXAS1JHGfCS1FEGvCR1lAEvSR1lwEtSRxnwktRRrQZ8kvOT/E2SuSTvbLMvSdITtRbwSSaA9wEXAKcBFyU5ra3+JElPdHiLbb8QmKuqrwIk+RjwGuCOFvscuy1btjA3NzfWGub7n5mZGWsdAFNTU2zevHncZawJ3/j+BJfecvzY+v/Og4fx0L6Mrf+15uiJ4qRjHhtrDd/4/gTPabH9NgN+Erh7wfOdwD/b/01JNgGbADZs2NBiOYeOdevWjbsE7WdqamrcJTCxaxeH7dkz7jLWjIl16zhqcnKsNTyHdj8bqap2Gk7+BfCKqvqF/vOfB15YVUtezk1PT9fs7Gwr9UhSFyXZVlXTi73W5pesO4FTFjw/Gfhmi/1JkhZoM+D/CnhOkmcmORL4OeBPW+xPkrRAa2PwVbU3yS8D1wETwAer6va2+pMkPVGbX7JSVdcA17TZhyRpcd7JKkkdZcBLUkcZ8JLUUQa8JHVUazc6rUSS3cBd466jI04E7hl3EdIS/HwOzw9V1frFXlhTAa/hSTK71N1t0rj5+RwNh2gkqaMMeEnqKAO+u7aOuwBpAD+fI+AYvCR1lFfwktRRBrwkdZQB30Fudq61KskHk3w3yW3jruVQYMB3jJuda437MHD+uIs4VBjw3fP/NzuvqkeA+c3OpbGrqi8A9467jkOFAd89i212Pt6dhSWNhQHfPVnkmHNhpUOQAd89bnYuCTDgu8jNziUBBnznVNVeYH6z8zuBT7jZudaKJFcDNwOnJtmZ5C3jrqnLXKpAkjrKK3hJ6igDXpI6yoCXpI4y4CWpowx4SeooA16dkOTpST6W5O+S3JHkmiTPHUMdFyf5wQM8Z6OrK6oNBrwOekkCfBq4oaqeXVWnAZcAJ424jgngYuCAAl5qiwGvLngp8GhVfWD+QFVtB25K8ttJbkvylSQXAiT5iSQ3JPlkkh1JrkrPBUk+Md9G/31/1n/88iQ3J7klyR8mOa5//OtJ/lOSm4CLgGngqiTbk6xL8vwkNybZluS6JM/on/f8JLcmuRl466j+Q+nQYsCrC34Y2LbI8dcBZwCnAy8Dfns+YIEzgV+ht2b+s4BzgOuBFyU5tv+eC4GPJzkR+HXgZVV1FjAL/OqCfh6qqpdU1Uf6r72hqs4A9gJbgNdX1fOBDwLv7p/zIeBtVXX2qv/20hIOH3cBUoteAlxdVfuA7yS5EXgBcB/wparaCZBkO7Cxqm5Kci3w6iSfBH4KeAfw4/R+Efzv3mgQR9K73X7ex5fo/1R6v3yu7583AXwryVOBE6rqxv77/oDeBi3SUBnw6oLbgdcvcnyxpZPnPbzg8T4e/7fwcXpDJvcCf1VV9/fH+K+vqouWaOuBJY4HuH3/q/QkJ+ASzhoBh2jUBZ8Djkryi/MHkrwA+AfgwiQTSdYD5wJfWqatG4CzgF/k8SvzvwTOSTLVb/uYATN07gee0n/8N8D6JGf3zzsiyT+tqn8EvpfkJf33vaH5X1VqzoDXQa96K+a9FvjJ/jTJ24F3AR8FvgzcSu+XwDuq6tvLtLUP+Ay9IZPP9I/tpjc75uokX6YX+M9bookPAx/oD/tM0Ps/i/+S5FZgO/Di/vveBLyv/yXrngP/W0vLczVJSeoor+AlqaMMeEnqKANekjrKgJekjjLgJamjDHhJ6igDXpI66v8BFrfIrVU7bLwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.boxplot(y='Page Views Per Visit',x='Converted',data=lead_data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "    \n",
    "    As the median for both Converted and not- converted are almost the same , we cannot colclude if high page views affect the convertion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total Time Spent on Website"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.boxplot(y='Total Time Spent on Website',x='Converted',data=lead_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion:\n",
    "    \n",
    "    Median for converted is higher , this indicates if leads spend more time on the website there is a higher chance of conversion. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TotalVisits                                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.boxplot(y='TotalVisits',x='Converted',data=lead_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "    \n",
    "    As the median for both Converted and not- converted are almost the same , we cannot colclude if high Total Visits \n",
    "    affect the convertion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Do Not Call "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'No'), Text(0, 0, 'Yes')]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='Do Not Call',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "    \n",
    "    \n",
    "As most of the values are \"No\" no conclusion can be made with this . we can even drop this feild \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Do Not Email"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'No'), Text(0, 0, 'Yes')]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='Do Not Email',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "    \n",
    "    \n",
    "As most of the values are \"No\" no conclusion can be made with this . we can even drop this feild \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lead Source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Olark Chat'),\n",
       " Text(0, 0, 'Organic Search'),\n",
       " Text(0, 0, 'Direct Traffic'),\n",
       " Text(0, 0, 'Google'),\n",
       " Text(0, 0, 'Referral Sites'),\n",
       " Text(0, 0, 'Reference'),\n",
       " Text(0, 0, 'google'),\n",
       " Text(0, 0, 'Welingak Website'),\n",
       " Text(0, 0, 'Facebook'),\n",
       " Text(0, 0, 'blog'),\n",
       " Text(0, 0, 'Pay per Click Ads'),\n",
       " Text(0, 0, 'bing'),\n",
       " Text(0, 0, 'Social Media'),\n",
       " Text(0, 0, 'WeLearn'),\n",
       " Text(0, 0, 'Click2call'),\n",
       " Text(0, 0, 'Live Chat'),\n",
       " Text(0, 0, 'welearnblog_Home'),\n",
       " Text(0, 0, 'youtubechannel'),\n",
       " Text(0, 0, 'testone'),\n",
       " Text(0, 0, 'Press_Release'),\n",
       " Text(0, 0, 'NC_EDM')]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='Lead Source',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion : \n",
    "    \n",
    "    The lead sources like Reference , welingak Website is very high , should implement strategies increasing leads from this\n",
    "    Google and , Direct chat,organic search and Olark chat have higher count of leads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lead Origin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'API'),\n",
       " Text(0, 0, 'Landing Page Submission'),\n",
       " Text(0, 0, 'Lead Add Form'),\n",
       " Text(0, 0, 'Lead Import')]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='Lead Origin',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "    \n",
    "    The count of leads is higher for Landing page submission and API but the convertion is less\n",
    "    the count of leads is low for Lead Add form but the convertion is very high"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A free copy of Mastering The Interview  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'No'), Text(0, 0, 'Yes')]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "chart=sns.countplot(x='A free copy of Mastering The Interview',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion : \n",
    "    \n",
    "    we cannot make any conclusion with this distribution ,we can drop this feild"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Last Notable Activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Modified'),\n",
       " Text(0, 0, 'Email Opened'),\n",
       " Text(0, 0, 'Page Visited on Website'),\n",
       " Text(0, 0, 'Email Bounced'),\n",
       " Text(0, 0, 'Email Link Clicked'),\n",
       " Text(0, 0, 'Unreachable'),\n",
       " Text(0, 0, 'Unsubscribed'),\n",
       " Text(0, 0, 'Had a Phone Conversation'),\n",
       " Text(0, 0, 'Olark Chat Conversation'),\n",
       " Text(0, 0, 'SMS Sent'),\n",
       " Text(0, 0, 'Approached upfront'),\n",
       " Text(0, 0, 'Resubscribed to emails'),\n",
       " Text(0, 0, 'View in browser link Clicked'),\n",
       " Text(0, 0, 'Form Submitted on Website'),\n",
       " Text(0, 0, 'Email Received'),\n",
       " Text(0, 0, 'Email Marked Spam')]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BELQVPVUWJ9Q5iSAIgqC96Wk01O11TyQIgiBoX3oaDfU6HZnTHwAWAt6QtHhdEwuCIAjah57uLIYUj83sc3QU+guCIAje55QqUS7p98BWFc8lCIIgaFN6aob6fOFwATzvInIugiAIBgg9jYbaoXD7PbxUx46VzyYIgiBoS3rqs9i/7okEQRAE7UtPmx8NN7MrU0/t58zsd2Y2vO7JBUEQBO1BTx3c5+GlwT+Ed6q7Jo0FQRAEA4CeKothks6T9F66nA8Mq3FeQRAEQRvRU2XxgpntZWaD0mUv4MU6JxYEQRC0Dz1VFgcAuwL/AJ4FdgbC6R0EQTBA6Gno7PeBfSW9DGBmQ4H/wpVIEARB8D6npzuLdRuKAkDSS8D69UwpCIIgaDd6qiwWMLOlGgdpZ9HTXUkQBEEwn9PTE/7PgbvM7HK8zMeuNOmXHQRBELw/6WkG94VmNgkvHmjA5yVNr3VmQRAEQdvQY1NSUg6hIIIgCAYgpUqUB0EQBAOLUBZBEARBS0JZBEEQBC0JZREEQRC0JJRFEARB0JJQFkEQBEFLQlkEQRAELQllEQRBELQk6jvVwJMnrtPt/SsdN7WPZhIEQVANte0szOzc1LN7WmFsqJndZGaPpeul0riZ2almNsPMHjSzDQrP2Tc9/jEz27eu+QZBEARdU6cZ6nxg205jRwO3SFoNuCUdA2wHrJYuBwFnwpzqtscDmwAbA8cXq98GQRAEfUNtykLSHcBLnYZ3BC5Ity8APlcYv1DO3cCSZrYCsA1wk6SXUj+Nm5hXAQVBEAQ109cO7uUkPQuQrpdN4x8Gnio8bmYa62p8HszsIDObZGaTZs2aVfnEgyAIBjLtEg1lTcbUzfi8g9JZkkZLGj1s2LBKJxcEQTDQ6Wtl8VwyL5Gun0/jM4EVC48bDjzTzXgQBEHQh/S1srgaaEQ07QtcVRjfJ0VFbQq8msxUNwKfNrOlkmP702ksCIIg6ENqy7Mws/HAlsAyZjYTj2r6CXCpmX0ReBLYJT38OmB7YAbwJrA/gKSXzOz7wMT0uBMldXaaB0EQBDVTm7KQNK6Luz7V5LECDulCzrnAuRVOLQiCIOgl7eLgDoIgCNqYUBZBEARBS0JZBEEQBC0JZREEQRC0JJRFEARB0JJQFkEQBEFLQlkEQRAELQllEQRBELQklEUQBEHQklAWQRAEQUtCWQRBEAQtCWURBEEQtCSURRAEQdCSUBZBEARBS0JZBEEQBC0JZREEQRC0JJRFEARB0JJQFkEQBEFLQlkEQRAELamtB/f8wpMnrtPt/SsdN7WPZhIEQdC+xM4iCIIgaMmA31kEwfuJDY+6sNv7rxzSRxMJ3nfEziIIgiBoSSiLIAiCoCWhLIIgCIKWhLIIgiAIWhLKIgiCIGhJKIsgCIKgJaEsgiAIgpaEsgiCIAhaEsoiCIIgaEm/KAsze8LMpprZFDOblMaGmtlNZvZYul4qjZuZnWpmM8zsQTPboD/mHARBMJDpz53FJyWNkjQ6HR8N3CJpNeCWdAywHbBauhwEnNnnMw2CIBjgtJMZakfggnT7AuBzhfEL5dwNLGlmK/THBIMgCAYq/aUsBPzRzCab2UFpbDlJzwKk62XT+IeBpwrPnZnG5sLMDjKzSWY2adasWTVOPQiCYODRX1VnN5P0jJktC9xkZo9081hrMqZ5BqSzgLMARo8ePc/9QRAEQXn6ZWch6Zl0/TxwJbAx8FzDvJSun08PnwmsWHj6cOCZvpttEARB0OfKwswWNbMhjdvAp4FpwNXAvulh+wJXpdtXA/ukqKhNgVcb5qogCIKgb+gPM9RywJVm1nj930q6wcwmApea2ReBJ4Fd0uOvA7YHZgBvAvv3/ZSDIAgGNn2uLCQ9DqzXZPxF4FNNxgUc0gdTC4IgCLqgnUJngyAIgjYllEUQBEHQklAWQRAEQUtCWQRBEAQtCWURBEEQtCSURRAEQdCSUBZBEARBS0JZBEEQBC0JZREEQRC0JJRFEARB0JJQFkEQBEFLQlkEQRAELQllEQRBELQklEUQBEHQklAWQRAEQUtCWQRBEAQt6Y9OeUEnNjzqwpaPmfyzffpgJkEQBM2JnUUQBEHQklAWQRAEQUtCWQRBEAQtCWURBEEQtCQc3EHQT7QKbIighqCdiJ1FEARB0JJQFkEQBEFLwgwV9JjIBwmCgUvsLIIgCIKWxM5iPuHJE9fp9v6VjpvaRzMJgmAgEjuLIAiCoCWhLIIgCIKWhLIIgiAIWhI+i/cp/RW5FL6VIHh/Mt8oCzPbFjgFGAT8WtJP+nlKQZsSmdFBUD3zhbIws0HAGcDWwExgopldLWl6/84s6AsG6m6l1fuG9+97D9qP+UJZABsDMyQ9DmBmFwM7AqEsMmiHk3BPzGVXDqn2Nes4CceJPXi/Y5L6ew4tMbOdgW0lHZiO9wY2kfS1wmMOAg5Kh6sDj/ZQ/DLACxVON2S2v8z5YY4hM2T2h8yVJQ1rdsf8srOwJmNzaTlJZwFn9Vqw2SRJo8tOLGTOfzLnhzmGzJDZbjLnl9DZmcCKhePhwDP9NJcgCIIBx/yiLCYCq5nZKmb2AWB34Op+nlMQBMGAYb4wQ0l6z8y+BtyIh86eK+mhisT32nQVMud7mfPDHENmyGwrmfOFgzsIgiDoX+YXM1QQBEHQj4SyCIIgCFoSyiIIgl5hZof1ZCx4fxE+izbGzMYCq0k6z8yGAYtJ+lsJOafRKS+liKSvZ0xzwGJmHwdGUAgUkdQ6Jb17mYOA5TrJfDJD3k8lfbvVWC9l3idpg05j90taP0PmYcB5wOvAr4H1gaMl/bGErKHd3S/ppVKTdNkfBI4AVpL0JTNbDVhd0rUlZE2l+//luhnzXBLYh3l/n6X/6/NFNFS7Ymav0/2XvXiG7OOB0Xg2+nnAQsD/ApuVEDcpXW8GrAVcko53ASaXnF+d73054EfAhyRtZ2ZrAWMknVNC1gbd3S/pvpJz/A2wKjAFmN0QB5RWFmZ2KHA88Bzw74LM0icNvJ5aZ8WwXZOxnsxvHLAHsIqZFUPXhwAvlp6hc4CkU8xsG2AYsD/+u++1ssB/08KTeVcCXk63lwSeBFbJmOd5Sf6YdDwTuAzotbIAPpOuD0nXv0nXewJvlp1g4jrgbmAqHb+lLAaUsqj6BCdpSJJ7IvAP/Ms2/MvOrWi0E766ui+91jNmVkqmpAvSPPcDPinp3XT8K8r9Get+7+fjf8pj0/FfcAXXa2UB/DxdD8aV7wP4PNcF7gHGlpzjaGAtVbs1PwxfpeaeeDGzrwBfBT5iZg8W7hoC3FlS7F3As3jpiJ8Xxl8HHmz6jJ7TqNKwPXCepAfMrFnlhpZIWgXm/L6vlnRdOt4O+I/Mea4qabekOJH0r4x5/j3NazNJxUXg0WZ2J3BixjwHS/pmxvPnRdKAu+BfwlfxP87iwFeAb2XIu6cnY72UeW+6vi9dLwo8mCnzUWBo4Xgp4NFMmXW894np+v7C2JRMmRcD6xSO1wbOz5B3GbBCzpyayLwVWLAiWUvgJojxwMqFy9Aq5Fd9oWMX8RjwwfTfnJwpc57nA5MyZd4FLFL4X67a+K9myJwCjC0cf7yC3/s3gC8BKwBDG5ccmQNqZ1FgG0mbFI7PNLN7gJNKypttZnviJyQB4+gwTZTlUjP7H2BJM/sScABuy83hJ8D9ZnZrOt4COCFTZh3v/Q0zWzrJw8w2BV7NlLmGpDllXyVNM7NRGfKWAaab2b3A2wW5n82Q+Thwm5n9oZPMk3srSNKr+Gc2rpMfZDEzW0x5fpDPAz8FlsV3BOYvWd70CHwRGAU8LunN9P3vnyEP4AUz+3+4+VbAXuSby04AbgBWNLOLcNNu7jy/CJxrZkvg83wV/7/n8A7wM3x33tj9CvhIWYED0sFtZnfh/TGKJ7hDJH28pLwReGOmzZK8O4HDJT2ROc+tgU/jf8YbJd2UIy/JXB5oKMp7JP0jU94IKn7vyc9wGr76n4bbsHeWVNrUYWbjgTeY+8SxmKRxJeVt0Wxc0u0Zczy+C5nfy5D5NfwEN5cfRHnO0xnADpIeLiujIKsWn1KSPRT3AW2Of+d3ACcqw8Gd5C4NbIr/L++WVEmFWDNbHD8n5y6MMLO/4pW5K6teO1CVxQhqOLlXSU1RLA2fwkcknWhmKwHLS7o3c7qVY2YL4s59w01l72bKG4ybGzdPQ3cAZ0p6K0PmcsBG6fBeSc/nzLEgdwh+Qv9nBbJm4CeNbD9IQeadmtvGniPr1m7ulqStKniNxar4LJOsWyR9qtVYL2VWFtBRkHk1sLukXEd5h8yBqCyqxsw+BpwJLCdpbTNbF/ispB9kyGwWnvhg5orwTHx1uZWkNc1sKeCPkjZq8dTuZFb23pN5o0skXVFymg35i+Ahjz3tddKdrF3xbf5tuEL7BHCUpMszZK6NBwo0Qj9fAPZRRh20dDLeWtJ7ZWU0kXkKsDzwe+Y2l2V9P1WTQpt/je8gVzKz9YCDJX21hKzBuC/lVmBLOhzyiwPXS1ozY57XkwI6JK2XFkr3S2rdUatrmVcCI9N8i99RhM72hhpO7mcDRwH/AyDpQTP7LVDmhFlHFEuDTSRtYGb3p3m+nKr45lDZewd26OY+AaVPRmb2Wfzk/gE89HMUbpIo62M4FtiosZswz4O5GSitLPBib9+UdGuSuSX++ZYyjyYq84MUWBwP7fx0YazU92NmW0n6v64WCpkK6L+BbUgVquURVpt3/5QuORg4HPgQKUIx8Rpu0s5hGUmXmtkxMKdwaq7f7/fpUhkDUllQ7QkO4IOS7u0UQVd2Jfdb4Hrgx8DRhfHXc22twLvJ2dlwHA8jPwa7svcuKddR2B3H4+15b0uvNSWZI8uyQCez04vkV0RYtKEoACTdZmaLZsp8Ml0+kC7ZVPw9bQH8H80XClkLBABJT3X6bZY6CUs6BTjFzA6VdFrOnJpQeUCHUrh8lQxUZVHlyR086mJVOr7snfF49DJI0hNmdkjnO8xsaKbCOBW4EljWzH4I7Az8vwx5UO17J8lYGj+5j01yJ+C7gBy7+3uSXi0ZEt+MG8zsRjw0FWA3PBEqh8fN7Lt0JGftBfQ6Y79IwzluZotKeiNzfiRZw/EAhIbPbwJwmKSZJeZ3fLquY6HwVDJFKe2gvw6Ucso3dkDA0812QZk7oCPw3c+qKb9iGP7fLI15ZvmP8STcwYV5lo6GGqjKouoT3CG4CWENM3sa/4PvVVLWb/HMzmIWaoOs0DdJF5nZZOBTSe7nKohoqfK9N7gYd0B/IR3viSfl5SRUTTOzPYBB6Y/0dTxmvhSSjjKzL+AnTAPOknRlxvzAwyW/h6+mDf8Msk6iZjYGT2ZcDMiy2xc4D/+d7pKO90pjW2fO9T9xO3vx5JaTmPZlPJDlw3im9R/pyJbuLbXtgCRNTtF1lQV04N/H8bgp7pP47yhvpdRdEsb79YKfcG/G7a5P4yujERXIXRQY0t/vr5v5bVqcH+4H2SRT5sKd3zuZyT/Uk0z1QeCHeNfFSen24P7+TvrgO78Hb0lcTHCclilznoSxZmO9lPkrvFTKU+kkNxU4J1PmPL9DYJX+/k6azOkB4Dt4dnhVMien66mFsT/lyByQVWclPS7pP/Dt3hqSxiovL2DhtGo9DPiGmR1nZsflzNHMNmvYq81sLzM7OYW65nAmUAwhfCON5XCFmS0o6Q1Jr6c8jtx8kFvNbHczWyBddgX+kCNQ0puSjsV3VZ+UdKxKhM2a2YR0/bqZvVa4vG5mr5WZm5n9Il1fY2ZXd76UkVlE0lOdhnKdpy+k3+SgdKki2e3jkvYBXpabzsbgSi6Ha8xzFwAwszWBa3IEmtlhZra4Ob82s/vM7NOtn9ktn8XN4Jea2UQzO7KC//pbZrYA8JiZfc3MdsKTKEszoMxQZraXpP81s292GgeyIkSuwh1SkylEnGRyJrBeMht8Czcl/AbfDpfFlJYYAJL+ncL0cvg9cHkyyayI216PLDW5jtpdBnwTT6ADdxz/E19xlsLMNgLOJdWtMrNX8eJ1vSqkKGlsus6tf1Wk4aP4rwplNqjMbl/gAOB03MQh3JyXm3H8r3T9ppl9CFc+OQX/wHMXrknmrdXxncuemTKLBQ+XJa/gITCnRmFmZFIAACAASURBVNRJwEnJRPpdPEN+UMY8D8d3018Hvg9sBeybIW9gKQv8w4P8QnedGS5p24plvidJZrYjcIqkc8ws68vGHahfp2M38VU8tLI0ks5OJ6Hf47WIDpZUyhdQ8Qm4M+cAX5X0J6BR/v08SlZ0NbPfSNq71VhPKCisUfKom6LMw4DSWeHMbbd/Gu9jX9Zu3yih/gXllTVpxrXmZbV/hoemiszyNpL+YGYL4SfyIbiP7rHMeVZW8HAuoR6ZtyseKDEbXyCWRtLEJHcB4OuSXs+c4sBKyrOUAW1mu0i6rEK5ZwGnqVB7qAKZt+M1aA7AE75m4XbhnESdZfGIqK3wP+MteOZ6rzOPO+3ODNgbtzM3cjhy4vgxTxhcjbmdnXdkyJsn67jZWC/kzZU0mXZoD0paK2OOlfeJqAMzu03SljXKXxj3J5UKH7V5+7dshS+KnoC8xDQzOw9XvKsA6+Gr/9skbZgh8x68BcFlwCWSshZwSeZofDHUWICV2knPJXOAKYupwAZ4TaRua9L0Uu504KN4JNDbMKewWk629fJ474CJkv6UbJhbKrO5TlVYF3WMGiivntGBuP9nOF6Rc1PgzypR+sE6ag/tje8sx+Mnkt1w+/ixXT23C3nH4M7IRejoOWB44bazJB1TYo6NPhFjgT8V7hoCzE7+td7K/BJ+EnssrXzPwaPL/g7sp7yaSz/Eq9pegvu9gOw6ToOA/2TeZj29XnS02oErIwchrdQbBQ9fMQ/z/rDy6patIemRss/vQuaDeL274k76l1nnpAGmLH4GHIRH7hRrpmRVzTSzlZuNJ1tkaZLC2Bg/uU1UftG/YXjZ4hHM/YfMtTdXSlLqG+FF2kaZ2RrA9yTtVkJWLbWHzOzHZRRDF7JWxleq8yRi4ruVXucAmdk0YH1J76bgiyPwjOv1geMlfSJjvs0+09KfZZJ5HfAWnZr1ZC46FgXekjQ7HQ/Co/ey6iWZ51nMyQFSyZDprnyoDXJ251XvpGGA+SwkHQUcZWZXSdqxQrl/tyYtUHNkptX1cXhstwGnmdmJks7NEHsVvnK9mfyIGADM7CZgF0mvpOOlgIslbZMh9i1Jb5kZZrawpEfMbPUygiR9MmMe3ck9pipTWVpU/J2O7mtV8J46YvU/A1woT2q82cxKleI3s8OST+W7kiZUNdHE8JxVbxfcgufmNCIAF8H9F6XLp5jZL3ErQiMZ82Az+w9JZfxAjez8Onx195q3OCjupG9r7LTL7AIH1M6iLqzQAlXSx1I0x2U5WtzMHsXDCV9Mx0sDd0kqddJMMqZIyunh0COZuXZ28yJo++MRHVvhbTEXkrR9CVm1rN6qNJUVZG6KZ0aviZfmGAS8UWbHa2b34Wadl3FFtJVSQUIze1glCt81vutmvpVczOynwC0q0XO7G5nNfptZ/wEzewhYW+nEmcxSUyWNzJtttdSxox5QOwszmyBpbKcQzTnXZc1QVNgCtcBM3AzR4HU8YSmHa81se6U2kxUx28xWUmqmk0wqWSsQSTulmyekH/0SuLO/DHWt3g6jw1T2yYapLFPm6cDuuKNzNLAPvootw3F48uEgvLVoQ1FsQfkIuIfN7AlgmM1d5DLbR4f3i74ynXzfJf8/CV5zaYPGKtrMNqQjRLcsj+J9vRsm5hXJbClrNVStrmNHPaCUheqJkQd4R5LMrLHaKF38rbACfhq4x8yuwk++OwK5fScOA75jZm9T3R/yWGBCit4C7xdxUN4059iXl6OjNtLyeEG8XiHpf5Ks1yT9d+68ClRmKisiaYaZDUp29vPMG3WVkXNtUtxDJL1cuGsSbpIoI3Nc8qPdiCeSVcnPcTPc1MaqvQIOBy4zs2fS8QqUfO9mdg3+P1wCV5r3puNNyCgbk6iyavUOuJ+r0d/7ODoCGw6TVLrW2IBSFuads7pE5Yv0NWuBenZJWQ1F9td0aXBVSXlzqEFJIumGZAdtdA77hjK7c5nZoXgC3lzd3SiZEyFptnmJ8iqVxUzzvIDfAzeZ2cvAMy2e04o3zXNWpiS/wrN07Ix6TXKMv9xpLKuYYAqyWC/Ncw38e3lU0js5cvHe29MqVBRImph2fI2aS4+ofM2lOhImG1RZ2PSH+H8RM/sMXrdrHG75+BVesr0UA8pnYWZ/o8PstBL+RzJgSeBJSaUzRq2jBSp4Q6HsFqhJbpXVQpvW8i/jlG2E+1kXbTHLONAKsuvo7lZ5uGdB9hZJ9g05J820E3gO91d8I8n8paQZuXOsEjPbHl8F/xX//6yCJ2NenyHzfLxm2/Vk9t2wentkNL6n1STdbN5Qa0FlJL2ZNz/6Gu7n3MC8sOkXJW1XQtYDktZLt8/FFflP03GWr2lA7SwaysDMfoXbca9Lx9uRV9EUPORvEVwZZSfnWT3VQo8q3B6Mh+VOxp3IveUIPAz3503uU0mZDZ4is55/ExoRMMUqpqXnaV61eKakRl7NCDyPo7SyUEeo9VtmdiqwYrspisTJeH2tGTDns/gDfqIvy9/SpYq+G7VViE2Wg4Pwboar4gEOv8JrjpWlysrNZmaL4akBnwJ+WbhvcPOn9FDwQNpZNDCzyeqUcWlmkySNLimvc5jrFnj/hdJhruZZnTvjSm39NDZN0tplZTZ5jRWBkySNq0pmDgV/zUjcdFBld7dKMbMpuBN6BG7DvxqPhut1xFZB5m24L2BBPMJqFnC7pKaRXC1krQy8opQFbWafBD6H265Pz9wB3SFp88KxpXmW7UJXlF3ZTroO0ve+MZ7Y2/hfTlVGZYWC7EXxplo5u5QD8KTR14DnlcoQmdn6wH8po1f4gNpZFHjBzP4fXqhOuBbPMXkchSdAzRXmiheuK40q6vLVDTOBUsqnqy1+g5Jb/YZPpfLubmb2I1wxFvNBjpBUtvnTv+XtL3cCfiHpNEvtajNYQtJrafFxnqTjO0Ud9YZL8Si9V81byF6GJ/2th682D8yY50PmSXSX4v+fXYCJjd9Eme++yp20dREm3SBz0fG2pHca/0vzMi9ZK+4qf5uSzjVvyrUsXvq8wT/I7I0yUJXFONyB2si8vCONlaWOMNfKq4Xa3DVzGmULHuj6Gd1Seb9sZWTr9oDtJH2n8FovJ9t7WWXxrnmZjn3p+CwWypzjgma2Al5QrldlSJqwiKSGw30v4FxJP0+hqVMyZQ/GfSuNCsizcLPMDpQ38/yC6vpl11mQ8nYz+w6wSPJTfpXMsudU/NuU9DQeTVkcy+peCQNUWaSop8PMa93/W9I/Wz2nBU3DXBsrnJIrmSq7fDWYVLj9HjBe0p1lBKnGftlWT1b4oBTi+naSuQiwcIa8/fHv6IeS/mZmq9BRUr0s38NNWhNSJM9H8CihMhS3pFsBx8CcsvRZk6zru69qJ13zouNo4Iu4X/JgvJVuVnVcqv9t1sKAVBZmtg5e235oOn4B2FfStJIiuwpz7fUKx8yWkvRyCj/Nrb0/F5IuSLuUj6WhR8vKSorwVUnndBo/FBgk6RflZ8qwhqKAOSutrMYt+In8FvOqocLDm0sVlDPP2/iOpDlOyBS//pOyk0syV1QhsU1effQLXT+rW/7PzC7Fw2+Xwv1ppJ1LVphr4TOcC+XVGKuyX/ZJeKG/X3Ua/wawvKRvl5D5ObyCwvN4WHzZ0PhmVPbbrJOB6uC+CzhW0q3peEvgR5JyasYshie4ZTnnzOx5fFt/F3An/gP9S47Mguwt8R/hE/jKc0VcSZYJnZ0GbNDZUWpeXnqi8iruTgZ20txZ4VfmhP0lOdviUW+GhzffmCHrRmCHCvILijJvVUWZt8npvBueiHZpMk00HJ3LZr73ogIbjPtGnlFe6e9l8J30nO8HTyLrtS/RvAr02pL+3Wl8ATxhrdd+OjO7HE8afJP0vwTuVMqMz6Wq36bVl0s2YJXFnFjk7sZ6KOur+Na0kTz1T+Cnkn7Z9bNayvwYHurZuAzDyyHcKalUEbgkdzKwh6RHC68zvnNkWA9ldRkBkhsdkv44Z9HR9Gdz4KDME9yiwL+SGWZ1PNrqepVM0jJPwtwAt7EX8zZyKoXWlgtSJ+kkfLMy6mJViZk9pC5qNXV3Xw9lj6DjfzkGz9eamBMFl+QWczc+iO/Oex0VZTXmkg1IMxTeMe67dLSz3IuOshI9JkVUfRzvM/F4GvsIcIqZDVXJ2i5pJ/EX4HzzGPbt8VIdn8bbL5ZloYaiaLyOeSexUpjZcpKe6zyWMb/GvCrPCseDGD6R/B8301H2oqyp75l0WYDqHKqV5YJYR/2zOUNUUwetGavhJ6Z24U0zW02duuKZtyzNqg0l6QkzG4znVC2C76wWyZFp8+ZufJiSuRuqMZdsoO4slsKdiWPxP88dwAmau4ZOT+Q8Cqwn6a1O44sAD0j6WPNndiuzuGpZES/6dne63Jdj9jDP6BQdSnJPPPu01w5LM9sHtysfQSqgCGyIK7MzlNFgJsmvulPeffLs2EPxSKGTrIIudNameQFm9nu8ntYVeHBAr+tqdSO7syL6B3CMpN9V9Ro5pBPjaXhtpUZnuNG4k/9wlSikmSKgxuC7/Efp+E8+qNQvI2O+leduWMW5ZABIikvJC55K39V9j5SU+W981bsHXjOmyvkuDHwTP4FciZeUWDhD3na4qehF4IV0e7sK5nkgHm3yMnArvhr8v0yZ9+N/9ruBkWlsaoa8McB0fGsPKX8hc47L4bkG16fjtfCyD2XlLYFHbd2YvpuvAkOr/E216wXPH7oAVxaT0+11MuQ9kv6XJ+A7/CUqnOs96fr+dN1o0Zsj80Y89HYEsDIein1jjswBtbMws6u7u1+9bEJvZrfgjvFbOo1vhTeI6bWz0ryqZ2N3sTH+w7kP+DPeLyG7P2+7YxV2yivI3ALfBd0p6afJXHi4SjplrYYMe/MaQefhwRfrmSd83a/M7ODkU9gNX23/SG2UCd/AzJbAT8SNDn6341UQqi77UprkPG78NzfFEwgfwINQzsuQexLwCl6S/lBcqU9XL1v+Npnr8bi/T7j15ESFg7tnmNksPFluPHAPc8eiI+n2Zs/rRt5IPEx2Ar56EX6S2wzYURVESiRn1wF4ueVVJA0qIWM1fGXxEl7X52z8T/lX4EBJE3PnWSVmNlHSRml7vomkt62ixk3muTVSRkmFJOceSZsUTVllgyQKMhvvuyiz9PtOJs1x+Hc9AbhEqSdzu2FmvwOm0REyujdu4u22UkB/kJT4hviJ+GBK/i8L8hbAczc+jZ+TbgR+rQpOzma2mPLzyICB5+BeHtga/wPtgdceGl/2pC7pITNbO8kaSYf/42B18mP0lLTCGkPHCmZ9YAaeJVoqgQ5frV4ILI4rycPxcMdP4A13Nikpty4qL/9tZqPxz2GIH9orwAGSJnf/zC6pPMMeb9azNMkfYN45r9TK2rxJ0SvAxbjz9L00XrqtZs2sKqkYkvu9tFhoC8xL3H8cXwiOBB7Cw2ePILOfhTzEd67cDTPbjPL/98ZC4ddUWYi0Krvb/HbB7ff74TkNh/b3fArzmoUrhmPwcgqLVCBzSuH2jK7uK/s5NhmrzC6ePoPPAh/IlPMg8InC8Vgy7MLAMsBFeNmL5/HEqqUz57ghfoJ4NV3/BVi3pKzbcH/PrXhC3q3F45Iyh3Z3yXzvfwbGFo43w82uOTLnmRO+Cygj6wrgSHwhl/VbLMgchC9cj8TzQsD7pd9F8l9kyL4HD5C5vzA2LUfmQNtZNJLG/hP/kkYAp5JRsrhqJA2rQWwxOem1bu4rwxVm9jmlfAXzDOFr8RNfacxsLB53fp6ZDcPDCUt3+QJeV8EEI2lCiuopPUVJVWfYT06+lUaznkdVMg9E0pZVzi3RMLU2jeEHSsfw46VTLkw7a5LsfTPkAVxjZttJeg3AzNbCix/22q+kesxh5+An9HuBU83s77gyOlrS73OFq+JCpANKWZjZBfgP5XrcYVq2vMf8xhrm1UsNWNU6Kpka3nAmh9/jrSu/gP/wr8ZXSqUxs+PxUMfVcdPRQvjKfbMSshpZ3/emRLrx+AlvN3z1XZa7UgLUJcDvVChPUhYzeyDJu0TSX1s9voWsjYCn5J3tGqHOjfaaJ6iEo1P19oN5Te7UXzy91mvm9bZy+BGuMP4T/y1dSMUldDIZje8c/51yN14APtr4zjKpvhBp2p4MCMzs33Rkxs6TsKRqE5XahpQd2iXqaLpTVv4hwLb4Tu1gSVk23GSrXh/PK2k4eh9UiRIiZnZrN3dLGVnHZrYxsDveJ2I6ns9Quphg+p52S5d/44rjUpXIkTCz+4D/kPSSefXWi/FIm1HAmpJ2zphn5TH81qSLW7PXKSH3c8C3cF/V59UpUa8/6fyem30GGbIrK58yR+ZAUhZVk0I8m32ADeVTuj5Su2Nz9wwwPHplKp7PgPLKXtwraeNCIt2iuP26LT/P9Mc8GdhTGVExnWSuBny3rEybu73mGcAsSSek46zIMvO6WH9i7n4wm6tEVeAUFj0ST+YsdnJcHDhKJUpz2Nyl+MEz4B/Ha6KhjBpWVWJmb+LBK5B2/ek4+/xhXkHipU5jq8gLXpZiQJmhauAzdQlOdvov4av1Od+T8ip7VknnEhdXdjFehkuTyWhJ81IIB5BZ5dPMjms2LunEZuM9kLc4HlG2O/4nvxLPi8nCvPbQrvjuYja+Ki7DIDNbUNJ7eNmIgwr35f7vi/1gGjH8ZfvBrI7/j5Zk7h4pr+O//zJM6nRcNuJtHlKU0gl4otuCdJzYy5hz16xqXk3o7K9ZE2+AVT4PKHYW7Yl5Zdw/4T/0OY4ptUlJhboxbywzJ+5c0k2Z8o4oHA7GT1APl1W+yV/xe9xM9OecuRVk3oP7Zy7D/RalEzDN7Fi8ptgLuDN6A0kys48CF0jqtf+nyWtUFsNvZmOq+hzrxMwewSsfdP5f5nTarJzkp/kWHswzx18jqXQ4ciiLDMxsgqSx1kXRthwfSFVJaHVjXrn2SObdAZXyBZj3dbhRUq7DtNXrLIw7aUs1VDIzU8V/HjNbQ9IjFcrbFC9R/kel+lXp+1pMGXkWxRh+SdXE8NdAxbuAhsx7JLVbXlJTqvbXhLJoU8zsB3gZgV4XPWsiqzbfSorg+RXzrrRKb/3Ny7LsrRpLPZgXKrxX0moln1+pkkwyF8YjljrLLGUqqwurodRJHdSxCzCzn+D5EVcAbxdktkWSY53+mvBZVIh5N7dildScSp+HAd8xs3eARqx92d1Kbb4V4D1JZ1Ys8y1gqnl71WJfh5zmOkWFOQivHppzEr4MV5K/JjN+vcBVeELeZAononak6hj+mnhV0vUVy2zsKoqRX6XKyNdEbf6aUBYVkEoB/Bz4EJ7NuzIe01y6yYqkyprO54bGtuAa8wZQVzL3Sqt0wTK8DMsfcifWiaLCfA94Ljl/y1KHkhwuaduKZdZBlS1Qv9nd/TlRdcCtZvYzKtwFqKJOhlDPjl/eOnkQ7pfaq+UTekEoi2r4Pl6F8mZJ65vZJykfHTKHpIQ2T4e3Sbq2pJzafCt0ZNkWwx5FRrKfMnthdMGCwEx5UcItgS+Y2YUqn0xXh5K8y8zWkTQ1Q0Zf8GU8hv/DwEw8hv+QkrIai6LV8SKcjcrQO+BRVjlUvgswzzBvVHOFvOq4tez4Jc02s2Fm9gFV2fY3fBb5NBKSkv1+fXlG5r2SSodSJtvoRnj9IXDlM1nS0RVMua2pyTE5BT9pjMCrel4NrK6S7TBTNFRncuc4HfgoXtbkbQZAvk4DM/sj8AWlasBmNgS4rN12WjafVMe1Gtr+xs6iGl4xs8XwldBFZvY8qcpnBtsDo5SazpuXKrkf7/edRRW+FTPbStL/mVnTP4mknHpb59DEMZnJvyW9l+b7C0mnmdn9ZYUpo5dxN2xXg8zKqSkHaCWguAp+J8nvNWa2l6T/7crElWnaqqw6bs07/srb/oayqIYdcafsN/DaM0uQ5zxtsCTeg4IkM4uKfStb4NVMd2hyn8grzliHY/JdMxuHN5hpzDmn//hCwFcomAmB/1HJwn/gvqUUhtpoAPQnSQ+UlVcjV+E5QDdTnTL/DV6/q5HotxOeG1CGRdN1ZX6/Av8ys7GSJsCcXXCpvt6Sxqbryucp6XtVywwzVIWkrN7iSqt8Vyo/sf0ELylt+EnpGEkXZ8h8ALfXzuVbkXRQi6f29nW+kJM8WEd4onnF0S/jZUPGmxep203ST0rK+zWubIrmiNmSDsyY42H4ir2haHcCzpJ0WlmZdVBXDpB50ceGorxDUumdX10kZX4hHYu3l4F9JT3Y9bN6LLuyaMq0+/sWvhAsyizvrwllkY+ZHYzvJP6FF4DLtrEnuSvgfgvD+/RmVaOsw7fSxes8KWmljOc3iv81fpyNz7NdwhPnqr3U3VgvZT4IjCkk0LVlTawqc4A6ye1cln4xlahlZGandnd/Tgh24TXmVMetQFbTHb9K1MUqyPwjXojySHyRtC9eH+zbZWWGGaoajgRGSnqhSqGSnqUjOqQK6vCtNMNaP6TJkzpszI2oL+HNoCaUOWl0kl2103y2ma2qVErcvKd3rknGOsmYTcnPsg4KtnXDc4DexnOAqqhYUFlZeirMLeiKKpREgTqiKZeWdI6ZHSZvF327mfWqbXRnQllUw1+BN/t7Ej2gLt9KZ8puV5vZblcGjjWzE3JMcFTvND8Kj+N/HD9ZrgzsnynzPOCeZLcHL31+TqbMyqjDtl5gJ1JZ+vRaz6SIqF5TU+h1nbwr6UUzW8DMFpB0q5n9NFdmun7WvE7UM8DwHIGhLKrhGDxG/h7mtrG3RSnkBgXzxuJ469bStEgoWq6MzK6ccmY2FHem5iiLSp3mkm4xLyPe6Gr3iKSsrGtJJ5vZbXjLVwP2b1O7/U54a9ZX0/GSwJbK6+72jiSZWaP/+KKtnvA+oo4d/w9STsgRwGl4yfdv5AgMn0UFmNm9wAS8n8OcNqVlVjjpxNglmU7zynwrVnNDpSavd79SHaKSz6/EaW5me+H/m990Gv8S8Iak35aY20bAMp2VWbJlP62MOlt10MzBXcH3cySwGrA18GO8LP1v28W531WIeIOcUPGkGN/C/4+NHf9FardKtqEs8jGzuyR9vCJZf6ObPsc58f1m9hjuQK3Ut1I3ZrYV8P+yIjkqcpqn3IzNG8ljhfHFgVtVorNb2k3sJ+mJTuMfxaOh2saxD+6I7+x0N7OpktbJlNsoSw9eKbd0WXrzkhdfl/TfOXMqyDsv3VwW+DgeNg7wSby6QnZSXhXRlHU698MMVQ23mtlBuGknq/SD6u1z3Na+lS5MW0Nxe+s+JWVW7TQf1FlRwJye0WXzNpburCiSzBlmtnRJmXUyycxOBs7AP89DqcapPBVYJMnMKnkiL3mxI1CJspC0P4CZXQuslYJPGhGLZ+TI7mrHT7mSOV/GM8wvxf83lQVIxM6iAqye0g919Dlen+REpQ19K01MWwJebPhaSso8vsnwUGAboNdOczN7GBjdeU7JGTtR0hol5jhD0kd7e19/kcwm32Xu/s4/yPyeDgSOw1fshid9nijp3AyZP8RNOpcwd8mLnHyduUqxm9kCwIPKKM9e5Y4/LS52wTstvoe/999JejlbdiiL9sQq7HNckFmZb2V+p+E0l7RBL593JN6m9CuN3YB5K9QzcHPEz0rM5VfAi7ipTYXx7wErqOKkyXbEzB4FPt6w06eT3l2SVs+QeWuT4ax8HTM7HfetjMf/l7sDMyQdmiHzBrw5UaW7fjP7MB6C+03g2539bL0lzFAZmNm3JJ2Ubu8i6bLCfT+S9J0M8VX2OW7wnqRuS0L3lBbRUGq3RLLOSHrJzHq9RZf0X2b2TzxufTH8M3gD+InKlyw/Au+LMcM66gyth/cmKJ0RXhdWQ3YwXr22aN57HXgqQ16l5cQLMr+WosEaZV7OknRld8/pAZVHU5pnw4/DAwaupwIzYewsMjCz+xor0+LtZscZr1Fln+MfAn+nAt9KX0dDVU1FTvPF8P/QPD6MkvI+QkedroeU0YO7TqzC7OCCT2kUsA5ed0p4TtC9kr6cMc/lgB8BH5K0nXnJlzGSsnJX0m9/NUk3m9kH6cKP1Qt5VUZTfo/UXx4PNb9BeX1bOmSHsihPMVywc+hgBaGElfc5rsO30u60cpqrwp7XA4WGP60YFWVmt0vaooSsZj6lOSijIJ6ZXY/76I6VtJ6ZLQjcnxO1lUKkDwKGSlo15dr8StKnMmRWGU35b7yNaqO4Yefov9I7/jBD5aEubjc77i3/jTthrwaQ9ICZbd79U7onJ+y2M1ZveeUq6dxgJttpHlSXHZyjDHrAMpIuNbNj0mu9Z2a52fuHABvjQSJIesy8AGAOlUVTAnWUzgdCWeSynpm9hp8gF0m3SceDu35az1BFfY7r8K2oxvLKVdLu5rD5lMqyg83sF5ION7NraLLAkvTZjHm+kRzljazwTfEe5zm8Lemdxv8y7VZyF4Z7pOtjCmOlQmfr/L2HsshA0qAaxVfW5xiP2Dgp3T4GuKxw37ZAjiMeAKuwvPL8QLJVHwGsJOlLyRyxukq2vk0yfyNp71Zj/U3hPb6KJ6Xl0IjQ+a9MOc34Jr4zX9XM7gSGATtnyrzdzL6DLw63Br5KZumcKnf8dbJAf08g6JIv41veRp/jUfgPswzWxe1mx70TbPbZFCf+N7wf8RN49MX7nfNwk8GYdDwT+EGmzLlKUqcs5F5nhNeNmQ03syvNbJaZPWdmvzOzsmaoyel9fknS7Z0vOfNM+RRb4BnXB+OVoXP7ThyNJ3VOTTKvk3RsGUFm9q3C7V063fejnEnWQSiL9mV1SXtKWk7SspL2AtYsKatO30qjvPJf0grpU8CdmTLnB1ZNpr13AST9i5KK18yOSb6fdc3stXR5He9tcFVlM66O8/AV+wr4YuaaNFYKSbOBYWkHXRnpBLyIpIfwCr6XpJDSHA6VdLakXSTtLOls86ZVZdi9cPuYNgmDyAAAF5RJREFUTve1Ve9xCDNUO3Ma3nC91VhPqNO3Ukd55fmBd8ysUZoCM1uVgnOyN0j6MfBjM/uxpM4njXZkmKSicjjfzA7PlPkEcKeZXc3c2dY5/bK/K+ky86ZK2+CmrjOBTTJk7guc0mlsvyZjPaHyHX+d+U+hLNoMMxuDb5uH2dwN5xfHq6b2mpp9K33VUKndOB64AVjRzC7Cm/TslyNQ0jFmthSeIVz0/9yRI7cGXjCvvjs+HY/DM9BzeCZdFqC63tmNgJD/BM6UdJWZnVBGkHmb4z2AVZJCazCE8u+9jh1/5+i/yog8izbDzLYAtsR9Fr8q3PU6cI2kx/pjXl1h80l55TpIkTab4u/9bmXW9jGvj3QYHoY6Jcn+c2ZmdOWY2UrA6bi/RsBdwGHtFnlmXvTvabyG1YZ47sG9KtH6NiXirYKXTz+6cNfreG2oXi+QUhjvG6QdPx1FPg0YLKlsYcpaCGXRppjZyu325+sOq6C88vyGee2dRptWIG8XkEwIG+GKZ5SZrQF8T9Ju2ZOtCKu49HdB7k3ALpJeScdLARcrrxbaB3Hb/9SUD7ECsI6kP2bI/GnnTPVmY/1FnflPYYZqMxpx58DplrqGFcmMO68cq7a88nxD8svsBjxER4mGRg2vsrwl6S0zw8wWlvSImZUupFcHqrj0d4FhDUWRXuflCpLdjsaLcT6TZD4LPJspc2ugs2LYrslYv1Bn/lMoi/ajzrjzOjgSD0mcrxoqVcDn8Ii1rFaqnZhp3qL098BNZvYy6UTXZtxpXn21stLfwGwzW6mRn5PMPrlmjydwf8qpaaX9J+AOSb2OMDOzr+Ch66uaWTH8dghtHP1XZf5TmKHmA9KWfMUKYsQrx2oqr9zupLpDu6iiIo9N5G+B+39ukPROHa9RFqun9Pe2wFl4rg54VdeDJN1YVmZB9vLArvjCZqkyq+6Usb4UTXwW7WhyNW/J+3PgQ3gI9srAw5JGdvvE7mSGsmhPzFttfhbf/U3BE4FuV0UlxqvC2ryhUl2Y2e/wMuK3UOH7Tj6B5ZjbD/K+zoZvYGbL0BEw8OcKAgZ+DawFPIfvKiYA95V0Ri8u74Y4tNn97aYwzOwBYCu8Z8v6ZvZJYJwyeqOEGap9WSL9OA8EzpN0fKftb7vwP3h3s7nKKw8Ark6XyjCzQ/GQ3OeY2w/SVr1BUhTY8cBYfH4T8K52pSPgzGwzYIqka1NY7nfM7JTMII+l8XDzV4CXgBfKKIrEb/Gw1Mn4ey7mQbSjj67y/KdQFu3Lgil6Y1egVDmBPqKyhkrzE5IuSBnHH0tDj0p6t7vn9IDDcD9Iu4cdX4w78r+QjvfE/Rc5PeLPxJNH1wOOAs4FLsTLdZRC0k4AZrYmnpR3q5kNktTr0iSSPpOu54s6TtSQ/xTKon05EbgRmCBponljnLbKsUhUWV55vsHMtgQuwJ2ohifn7ZuZQPcU+VVR+4Khkr5fOP6BmX0uU+Z7kpQirU6VdI6Z7Zsj0Mw+A3wC938she+A/5Q5T8xsXWAEc5sKr8iVWzE74vlP36Aj/+nEHIHhswiysAHYUAm8ARCwh6RH0/HHgPGSel34r5CpPxJYHfgDcyvenJIXlWNm/4W3fL00De2MR8R128iohczb8Yz4A/AT/CzcLJXTqOgMfGX9J0mVRJWZ2bm4WXCukGlJB1Qhv2qqzH8KZdFmWOo9YWan0by+//vacTy/YIUucd2N9VBWbd3i6iCFoS5Kx8lyATpCaEslfqWIpT2AiZL+lLLEt5R0YeZcl8MTHcGzt5/PlDdd0lo5MvqCrvKfchZxoSzaDDPbQdI1XW3BVaIvbx1YDQ2V5ifSClN05MXsCSwoaf8SshYBhnQ+kaUT3auS3sqd7/xAUhgb45/rREn/yJS3C56vdBt+svwEcJSkyzNkngP8XNL0nLnVjXnbgDFV5j+FsmgzzGxn4Np2P0GY2X2SNuh8u9nx+xEzWxjvNzIWPxHdAfyyTJKemZ2F51Nc0Wl8T2CspK9UMOVKMbPP0xEN9SdJv8+UdyBwHO5XMNyxfaKkczNkPgBs3VDCZjYMDyXtdW2ogszNcf/cP3BTYXY11zqoI/8plEWbYWZX4hVMb8Crev5RXu+/rTCz+yWt3/l2s+Oge7ozbZjZQzmJVHVgZr8EPkpH1dndgL9KOiRD5qPAxxuRYCk89y5JpcudmNnUos/DzBYAHsj0g8zAO/DNFSqeGeJbOXXkP0U0VJshaafklNoJb6V6jpldhTtP26lUdZ0NldqelBdwAvMWEixjE+6ud0E7NijbAlhbaaVpZhfgJ88cZuIVXBu8jkeH5XCDmd3I3ErtukyZT0qqNL+mJirPfwpl0YZIeg0Py7wgrbB2Bk4zs6GSVuzf2c2hzoZK8wPn4GGJk+nom1CW581sY0n3FgfNbCM8KqjdeBRYCWisplcESiWMFiLBngbuSQsj4aGf93b5xB4g6aiCucyAsyRdmSMTeMTMfsu8oeLtFjpbef5TKIs2JtWE+jy+IhoK/K5/Z9SB6m2oND/wqqSqeo0fBVxqZufjygdgNLAPc7febBeWBh42s8bJfCPgbktNgdS7ysiNOk1/TZcGWe1kU9mUGyX9B1DliXwRXEl8ujCmil+jCirPfwqfRZthZkPwiqbj8BaqV+MZs7cqvqx+xzp6OO+Kl5K4grn/jKUqr5pXBz0EWDsNPQScnhvqWQepyGGXSLq9u/v7iqS89pY0PyQ6Vkod+U+hLNoMM3sBz9y+GI+QyS0hEVSINa+42kBqs652fUHy3+yR6eC+leZ5RTmVbC/FCxPexNyl1Es7eVMlhVOSXAF/Bg6X1Ozk/L4izFDtx0pVhrsF1SLpk/09h3bAzEbhSXS7An8j30R6ZOH2YLzuVG4v9z+kS5X8FjgDD0ABNxNeDGxS8euUos78p9hZBEEvMLNN8L4Lq+KRJgdIerh/Z9U3pJImu+Mm0hfx4oFHSlq5pte7XVLpQoJJxgeANfBdwKPK7A1iZvdI2qTT2N2SNs2RWxV15j+1Y1heELQzZ+Cr4KWBk4FfVCXYzEY0Gdto3kf2G48AnwJ2kDRW0mnkR4IBYGZDC5dlzGwbYPlMmdvjTvNTgdOBGWa2Xc78cMfx0WY2wsxWNrNvUf3uJQfr4naz414RZqg2x8wWlfRG60cGfcQCkm5Kty8zs2MqlH1FKvfyNMxxJJ8OlE4iq5gv4DuLW1OG8MVknoAKFPtEvIebtr6YKfNk4JOSZgCY2ar4ib1MFFvnPhYHF+4T8P15ntE/1Jb/FMqiTTGzjwO/BhYDVjKv83/w/2/vzIPkqqo4/P0IS8KSBXeMGGSRRRE0LgghAYNsoTBlMAoEguBSIiBuZaGlSLmAoiUQBSESUQO4ICEBLRJUkggSCNkmQjSlgKCUMWWAIIwaPP5xbk9eOj3TM+/1pF93n6+qa+57/e7pMz3T7/Q99yxm9pHmatbxjEyx+zWPC8bbfwiYI+kkPBLuK8AJBeQ1lJSjcKukXfCIvQuBl0m6GrjVzOYXkD0YfSLWVQxF4s94i9EBM0j6DQaDlv8UexYlRdISPBlvbqasxmoze13fM4PBRNKsPp4uXKpa0mF49m03cKKZlTEpr4fkmjkFmFowcukUPPpvo6TP4cbyS3lCkTPG+xg8w/4n+LfqU/B9i08U0POMWueLVsdtBcJYlJTKRlpVDaaVRYqgBeVE0jy2dBEcCDwJbIABJ7m1JErl3SUdAXwVrxZ7UfVmcj9lDZpBl7cOqDAU38NZZmZT8spsFcINVV4eT64oSxEd5wMdEXXTgVzebAVKQGWj/ETgajO7TdLFeQTlKRM/ANnnZY8ljWBzmfq2JlYWJUXSi/Hkn4m4v3E+cIGVvz9zkIOq8hQdh6Tb8fpQE4E34U177i/7SlrSDsAqMzug2boMNrGyKCmpaclpzdYj2DaY2QuSnpM0ohPLU+DJfccBl5vZU5JegdfMKhVVLsMhwAFsbi/b1oSxKCmSrqxx+mlgqZkVKrIWNAZJr8P3F3qiTApudHYDXZIaVp6iVTCz5yStwyvErsXDZ9c2V6uaZF2Gm4DHzOyJZimzLQk3VEmRd0/bH6ik678bLy73KuDPZvaxZukWUOmbPQE3Fr8Ajgd+W2SjUyVvpTuYpPdzLPBaM9tP0h7AT83s8AIyd8I/N2PYsufIJQXVrTRnOhLvb/FgvevbgVhZlJd9gKPNbBNAimWfj4cDFm00ExRnCvAGYLmZnSXvlz2ziMBOMAp9MBk4FFgGYGZ/SxWYi3Abvhp/kExl4DykPZXPmNnq5CJbBiwF9pZ0rZk1LJO/rISxKC+vBHbB/9lJ4z2Sb7vQP37QEJ43s/9J2pQ6G64Dcpd/BpC0Lx42Wu3aKiS3RfiPmZmkSve9XRogc7SZHdcAOQB7mdnqND4LWGBmZySDdg8NLPtSVqI2VHn5GrBC0qzUFGc5cHn6EN3VVM0CgKWSRgLX4d9cl1GwsxveM/lq3Bd+FPADOiQsE2/+9F08I/4D+P94oZUacK+kRpVKybYKeAepPauZbaRBbUvLTuxZlJi03H0LHjp7v5n9rckqBTVIBQCHm1mu1qIZOQ+a2ZskdZnZ69O5xWY2rgFqlh5Jx+Ad6ISHES+oM6WevIdwd+4juBtKeFLewTlkzcPdwE8A1+MrjackDcODTg4qomsrEG6octONZ/IOBfaRtI+ZLWqyTkEVZvZog0R1S9oOWCvpo3jewUsbJLv0JOOwADzvRNJpZja7gMhcFWZ74WzgEjwPZKqZPZXOvw1fEbY9sbIoKZLOAS4ARgMr8H/K33ViJ7ZOIZUjfxgYiVcxHQF8zczua6pig0ja7zkX36ObixuLc/EcixVmdnIemWb2TKpbtRVWoA91JxPGoqRI6gLeDNxnZodI2h/4oplNbbJqQdAwJN2G18D6Hb4XMArYEa9WsCKnzNvNbJK8D3W2rDgU7EPdyYQbqrx0m1m3JCTtZGZrJL222Up1Or19W62Q51trjUKC1TLbuZDgazL7MzOB9Xhr4Y15BZrZpPSzVcqKtwRhLMrLEynaZg6wQNIGIDa4m0+2Cc6e+Ldi4a6jvwB5blCdXEiwJ8oohYU/UsRQBINHuKFagNQxbQRe779QD+GgMUi6Bu818ot0fDwwsWCvhKF49I4BfzKz7oYoW2IkvcDm0iYChgHPsTlyaXizdKuFpL2A89g6K7ydV39AGIvSkW4YH8ZvGl3A9ypZ3EF5qIS5Vp1bamZjc8jaHu+K937gMTz/aTQeZfNZM/tvH9ODbYiklcD38M9mT36FmS1smlLbiHBDlY8b8KX5Yjz070A8KiooF+tTR7cf4SuB04G85eO/DuyGx+5vhJ4oocvTI/7+OZB0Cf45ureBfey7zaxWkc+2J1YWJaMqIWt7PBnvjU1WK6gibXR/AS8mB7AIj1bLs8G9FtjPqj6MqcfFGjPbt6i+nYik9+NVbA8DNuKGY1GRqs2STgX2xRP0esru5Gn/2mqEsSgZkpZljUP1cdB+SPqjme030OeC/iHp5Xi/jE8Co8wsd4FCSV8FpgF/YrMbyjoh/yncUOXjDZKeSWMBw9JxKTf8OhVJLwE+DRzElkX/8tw0HpJ0RnUvDEmnA2sKKdrBpFDcA4G/46uKKaSqtgWYjIf7dlygSRiLkmFmQ5qtQ9AvZgM/BibhAQlnAv/IKetc4OfJbVIJzX0zHhk0ubiqHcuL8G52TwH/BNY3IFhkJR4mva6gnJYj3FBBkINM0b9VlcJ0khaa2fgCMo/GVyoCfm9mv2qQuh2NpAOAY4ELgSFmNrqArLuBg4EH2HLPou1DZ2NlEQT5qISzPinpRDxhMvdNCMDMfg38uqhigSNpEjAOD0IYhb+3iwuK/UJRvVqVWFkEQQ7SjWgx3ub2KmA4Hg01t6mKBT1I+jYepba4EeX9U3TanWY2sbByLUgYiyAI2hZJrwb2NbO7Uu+J7YuUE5E0F5hmZk/XvbjNCDdUEAwASVfRd9G/87ehOkEfpI57HwR2B/bG3YTX4NVt89INdElawOYyJR3xdw9jEQQDY2lm/EU62IfdApyLd5pcAmBmayUVbSZ1R3p0HOGGCoKcSFpuZoc2W4+gNpKWmNlbK3+nVBFhWZ62qlVydwQqiZJ/6JTaXbGyCIL8xDetcrNQ0kV4YusxwEeAeUUESpqA1297FA9xfpWkMzuh3XGsLIIgJ1GKpdykfuZnA+/Eb+x3AjOra3ANUOaDwKlm9od0vB9wU3UF4nYkjEUQDABJG9m8otgZ770AUY6lI8gmYfZ1rh0JYxEEQVsh6Sdm9p7Ux36rG1yRG7uk65PMH6ZTp+HhuGflldkqhLEIgqCtkPQKM3sy5VhshZk9VkD2TniU1RH4anIR8B0z+3efE9uAMBZBELQVkmYAN5rZvQ2UuaeZ/aVR8lqR7ZqtQBAEQYNZC3xD0qOSLpN0SANkzqkMJN3SAHktRxiLIAjaCjO7wswOA8bjpclnSXpY0udT9FIelBm/prCSLUi4oYIgaHskHQpcDxycp2dMNky6U0Omw1gEQdCWSNoBOA54L14PaiGeEzGnz4m1Zb2A14IS3pSq40Kmw1gEQdBWpGzt9wEnAvcDNwNzzOxffU4M+iSMRRAEbYWk3wA3AreY2T+brU+7EMYiCIIgqEtEQwVBEAR1CWMRBEEQ1CWMRdASSHq24Pwxkk7t4zmTdF7m3AxJ0+vInC5pj3689t2SxvYyf0Y/1K+ed4Wkv6aqqvWuvajquM+sZkljJV2ZxhMkvX2g+gXtSRiLoFMYA9Q0Fol1wAWpsU1/mQ7UNRaNJBmIycDjwJH9mLKFsTCzPm/+ZrY00yJ0AhDGIgDCWAQtjKSTJC2RtFzSXZJels6Pl7QiPZZL2g24FBiXzl1YQ9w/gF8BZ9Z4nUMk3SdplaRbJY2SNAUYC8xOMoelDOEHJK2WdK2kbNbv6ZLuTc+9pcZrvETSLWn+A5IO7+XXPgpYDVyNh4dW5u8qaZakrqTnuyVdijf+WSFpdrru2fTzx5JOyMz/fpozQdLtksYAHwYuTPPHSXok5S4gaXgqp7FDL3oG7YaZxSMepX8Az9Y4N4rNEX3nAN9I43nA4Wm8K94RcgJwey+yx+A34L2ANcAQYAYwPT2/ChifxpcA30rju4GxGTm7Z8Y/BE7KXHddGh8JrE7j6cCMNL4ROCKN9wQe7kXXmcA0YDjwV2CHdP6yil6V96bW+1Y5xlcnN6TxjvhKZVj2fQIuBj6ZmTsLeFcaf7DyfsejMx6xsghamdHAnalvwaeAg9L5e4BvSjofGGlmm/ojzMwewZO4etxVkkYkGQvTqRvo3f1zVFrpdAFHZ/QBuCm9xiJguKSRVXMnAjMkrQDmpmt2y16QXGQn4AlmzwBL8C5wlfnfzvwuG+r8ur8Ejk4lt48HFpnZ83XmzAQqfRvOwo1H0CFED+6glbkK+KaZzU29kS8GMLNLJd2B31jvkzRxADK/AvwM71PQbyQNBb6DrzQel3QxMDRzSXVCU/XxdsBhdW7YxwEjgK7k4ap06ruDVHaiv/qaWbeku4FjgakkY1Znzj0pGGA8MMTMVvf39YLWJ1YWQSszAnfFQGavQdLeZtZlZpcBS4H9gY3AbluL2BIzWwM8BExKx08DGySNS5dMw2sMUSWzYhjWS9oVmFIlemrS7Qjg6SQ3y3zgo5nfoVZZ7fcB55jZGDMbg7vN3ilp5xrzR6Xhf/vYV7gZXyGMw/tTV1PrPfsBblhiVdFhhLEIWoWdJT2ReXwcX0n8VNJiYH3m2o+ljeSVwPO4y2UVsEnSyl42uLN8GXdxVTgT+LqkVcAh+L4FwPeBa5Lr6N/AdUAX3vvggSqZG1LY6jXA2TVe83xgbNqcfgjfXO4hGYRj8VUEAOa1jn4LnAR8CRiV+b2PSpddC6yqbHBXMR93qd1lZv+p8fw8YHJlgzudm43vFdVdiQTtRZT7CIKg36QosJPNbFqzdQm2LbFnEQRBv5B0Fb4ZfkK9a4P2I1YWQRAEQV1izyIIgiCoSxiLIAiCoC5hLIIgCIK6hLEIgiAI6hLGIgiCIKjL/wGTXDbbIhkMOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(x='Last Notable Activity',hue='Converted',data=lead_data)\n",
    "\n",
    "chart.set_xticklabels(chart.get_xticklabels(),rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "    \n",
    "    no particular conlcusion is made out of this"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we can drop all the feilds which are not necessary as stated above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "lead_data = lead_data.drop(['I agree to pay the amount through cheque','Get updates on DM Content','Update me on Supply Chain Content','Receive More Updates About Our Courses'\n",
    ",'Through Recommendations','Digital Advertisement','Newspaper','X Education Forums','Newspaper Article',\n",
    "'Magazine','Search','What matters most to you in choosing a course','Country','Do Not Call','Do Not Email',\n",
    "'A free copy of Mastering The Interview','Lead Number'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
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       "      <td>2.0</td>\n",
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       "      <td>Unemployed</td>\n",
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       "      <td>3256f628-e534-4826-9d63-4a8b88782852</td>\n",
       "      <td>Landing Page Submission</td>\n",
       "      <td>Google</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1428</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Converted to Lead</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>Will revert after reading the email</td>\n",
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      "text/plain": [
       "                            Prospect ID              Lead Origin  \\\n",
       "0  7927b2df-8bba-4d29-b9a2-b6e0beafe620                      API   \n",
       "1  2a272436-5132-4136-86fa-dcc88c88f482                      API   \n",
       "2  8cc8c611-a219-4f35-ad23-fdfd2656bd8a  Landing Page Submission   \n",
       "3  0cc2df48-7cf4-4e39-9de9-19797f9b38cc  Landing Page Submission   \n",
       "4  3256f628-e534-4826-9d63-4a8b88782852  Landing Page Submission   \n",
       "\n",
       "      Lead Source  Converted  TotalVisits  Total Time Spent on Website  \\\n",
       "0      Olark Chat          0          0.0                            0   \n",
       "1  Organic Search          0          5.0                          674   \n",
       "2  Direct Traffic          1          2.0                         1532   \n",
       "3  Direct Traffic          0          1.0                          305   \n",
       "4          Google          1          2.0                         1428   \n",
       "\n",
       "   Page Views Per Visit            Last Activity           Specialization  \\\n",
       "0                   0.0  Page Visited on Website                  Unknown   \n",
       "1                   2.5             Email Opened                  Unknown   \n",
       "2                   2.0             Email Opened  Business Administration   \n",
       "3                   1.0              Unreachable    Media and Advertising   \n",
       "4                   1.0        Converted to Lead                  Unknown   \n",
       "\n",
       "  What is your current occupation                                 Tags  \\\n",
       "0                      Unemployed          Interested in other courses   \n",
       "1                      Unemployed                              Ringing   \n",
       "2                         Student  Will revert after reading the email   \n",
       "3                      Unemployed                              Ringing   \n",
       "4                      Unemployed  Will revert after reading the email   \n",
       "\n",
       "     City Last Notable Activity  \n",
       "0  Mumbai              Modified  \n",
       "1  Mumbai          Email Opened  \n",
       "2  Mumbai          Email Opened  \n",
       "3  Mumbai              Modified  \n",
       "4  Mumbai              Modified  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5 Data Preparation and Model Building \n",
    "\n",
    "we shall follow following steps \n",
    "1. Creating dummy variables\n",
    "2. Test and Train Split\n",
    "3. Feature Scaling\n",
    "4. Model Building\n",
    "5. Feature Selection Using RFE\n",
    "6. Assessing the Model with Stats Model\n",
    "7. probabilyt cut off analysis using ROC curve\n",
    "8. Model Testing \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Creating dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a dummy variable for some of the categorical variables and dropping the first one.\n",
    "dummy1 = pd.get_dummies(lead_data[['Lead Origin', 'Lead Source', 'Last Activity', 'Specialization','What is your current occupation'\n",
    "                                  ,'Tags','City','Last Notable Activity'\n",
    "                                  ]], drop_first=True)\n",
    "\n",
    "# Adding the results to the master dataframe\n",
    "lead_data = pd.concat([lead_data, dummy1], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
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       "      <td>2.0</td>\n",
       "      <td>1532</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Email Opened</td>\n",
       "      <td>Business Administration</td>\n",
       "      <td>Student</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>Landing Page Submission</td>\n",
       "      <td>Google</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>Converted to Lead</td>\n",
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       "      <td>Unemployed</td>\n",
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       "<p>5 rows × 120 columns</p>\n",
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       "                            Prospect ID              Lead Origin  \\\n",
       "0  7927b2df-8bba-4d29-b9a2-b6e0beafe620                      API   \n",
       "1  2a272436-5132-4136-86fa-dcc88c88f482                      API   \n",
       "2  8cc8c611-a219-4f35-ad23-fdfd2656bd8a  Landing Page Submission   \n",
       "3  0cc2df48-7cf4-4e39-9de9-19797f9b38cc  Landing Page Submission   \n",
       "4  3256f628-e534-4826-9d63-4a8b88782852  Landing Page Submission   \n",
       "\n",
       "      Lead Source  Converted  TotalVisits  Total Time Spent on Website  \\\n",
       "0      Olark Chat          0          0.0                            0   \n",
       "1  Organic Search          0          5.0                          674   \n",
       "2  Direct Traffic          1          2.0                         1532   \n",
       "3  Direct Traffic          0          1.0                          305   \n",
       "4          Google          1          2.0                         1428   \n",
       "\n",
       "   Page Views Per Visit            Last Activity           Specialization  \\\n",
       "0                   0.0  Page Visited on Website                  Unknown   \n",
       "1                   2.5             Email Opened                  Unknown   \n",
       "2                   2.0             Email Opened  Business Administration   \n",
       "3                   1.0              Unreachable    Media and Advertising   \n",
       "4                   1.0        Converted to Lead                  Unknown   \n",
       "\n",
       "  What is your current occupation  ...  \\\n",
       "0                      Unemployed  ...   \n",
       "1                      Unemployed  ...   \n",
       "2                         Student  ...   \n",
       "3                      Unemployed  ...   \n",
       "4                      Unemployed  ...   \n",
       "\n",
       "  Last Notable Activity_Form Submitted on Website  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               0   \n",
       "4                                               0   \n",
       "\n",
       "  Last Notable Activity_Had a Phone Conversation  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "\n",
       "  Last Notable Activity_Modified  \\\n",
       "0                              1   \n",
       "1                              0   \n",
       "2                              0   \n",
       "3                              1   \n",
       "4                              1   \n",
       "\n",
       "   Last Notable Activity_Olark Chat Conversation  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "\n",
       "   Last Notable Activity_Page Visited on Website  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "\n",
       "   Last Notable Activity_Resubscribed to emails  \\\n",
       "0                                             0   \n",
       "1                                             0   \n",
       "2                                             0   \n",
       "3                                             0   \n",
       "4                                             0   \n",
       "\n",
       "   Last Notable Activity_SMS Sent  Last Notable Activity_Unreachable  \\\n",
       "0                               0                                  0   \n",
       "1                               0                                  0   \n",
       "2                               0                                  0   \n",
       "3                               0                                  0   \n",
       "4                               0                                  0   \n",
       "\n",
       "   Last Notable Activity_Unsubscribed  \\\n",
       "0                                   0   \n",
       "1                                   0   \n",
       "2                                   0   \n",
       "3                                   0   \n",
       "4                                   0   \n",
       "\n",
       "   Last Notable Activity_View in browser link Clicked  \n",
       "0                                                  0   \n",
       "1                                                  0   \n",
       "2                                                  0   \n",
       "3                                                  0   \n",
       "4                                                  0   \n",
       "\n",
       "[5 rows x 120 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "lead_data=lead_data.drop(['Lead Origin', 'Lead Source', 'Last Activity', 'Specialization','What is your current occupation'\n",
    "                                  ,'Tags','City','Last Notable Activity'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
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       "      <td>2a272436-5132-4136-86fa-dcc88c88f482</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>674</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>8cc8c611-a219-4f35-ad23-fdfd2656bd8a</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1532</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0cc2df48-7cf4-4e39-9de9-19797f9b38cc</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>305</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3256f628-e534-4826-9d63-4a8b88782852</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1428</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 112 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Prospect ID  Converted  TotalVisits  \\\n",
       "0  7927b2df-8bba-4d29-b9a2-b6e0beafe620          0          0.0   \n",
       "1  2a272436-5132-4136-86fa-dcc88c88f482          0          5.0   \n",
       "2  8cc8c611-a219-4f35-ad23-fdfd2656bd8a          1          2.0   \n",
       "3  0cc2df48-7cf4-4e39-9de9-19797f9b38cc          0          1.0   \n",
       "4  3256f628-e534-4826-9d63-4a8b88782852          1          2.0   \n",
       "\n",
       "   Total Time Spent on Website  Page Views Per Visit  \\\n",
       "0                            0                   0.0   \n",
       "1                          674                   2.5   \n",
       "2                         1532                   2.0   \n",
       "3                          305                   1.0   \n",
       "4                         1428                   1.0   \n",
       "\n",
       "   Lead Origin_Landing Page Submission  Lead Origin_Lead Add Form  \\\n",
       "0                                    0                          0   \n",
       "1                                    0                          0   \n",
       "2                                    1                          0   \n",
       "3                                    1                          0   \n",
       "4                                    1                          0   \n",
       "\n",
       "   Lead Origin_Lead Import  Lead Source_Direct Traffic  Lead Source_Facebook  \\\n",
       "0                        0                           0                     0   \n",
       "1                        0                           0                     0   \n",
       "2                        0                           1                     0   \n",
       "3                        0                           1                     0   \n",
       "4                        0                           0                     0   \n",
       "\n",
       "   ...  Last Notable Activity_Form Submitted on Website  \\\n",
       "0  ...                                                0   \n",
       "1  ...                                                0   \n",
       "2  ...                                                0   \n",
       "3  ...                                                0   \n",
       "4  ...                                                0   \n",
       "\n",
       "   Last Notable Activity_Had a Phone Conversation  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               0   \n",
       "4                                               0   \n",
       "\n",
       "   Last Notable Activity_Modified  \\\n",
       "0                               1   \n",
       "1                               0   \n",
       "2                               0   \n",
       "3                               1   \n",
       "4                               1   \n",
       "\n",
       "   Last Notable Activity_Olark Chat Conversation  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "\n",
       "   Last Notable Activity_Page Visited on Website  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "\n",
       "   Last Notable Activity_Resubscribed to emails  \\\n",
       "0                                             0   \n",
       "1                                             0   \n",
       "2                                             0   \n",
       "3                                             0   \n",
       "4                                             0   \n",
       "\n",
       "   Last Notable Activity_SMS Sent  Last Notable Activity_Unreachable  \\\n",
       "0                               0                                  0   \n",
       "1                               0                                  0   \n",
       "2                               0                                  0   \n",
       "3                               0                                  0   \n",
       "4                               0                                  0   \n",
       "\n",
       "   Last Notable Activity_Unsubscribed  \\\n",
       "0                                   0   \n",
       "1                                   0   \n",
       "2                                   0   \n",
       "3                                   0   \n",
       "4                                   0   \n",
       "\n",
       "   Last Notable Activity_View in browser link Clicked  \n",
       "0                                                  0   \n",
       "1                                                  0   \n",
       "2                                                  0   \n",
       "3                                                  0   \n",
       "4                                                  0   \n",
       "\n",
       "[5 rows x 112 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test and Train Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "      <th>Lead Source_Facebook</th>\n",
       "      <th>Lead Source_Google</th>\n",
       "      <th>Lead Source_Live Chat</th>\n",
       "      <th>...</th>\n",
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       "      <th>Last Notable Activity_Olark Chat Conversation</th>\n",
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       "      <th>Last Notable Activity_SMS Sent</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>305</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1428</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 110 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TotalVisits  Total Time Spent on Website  Page Views Per Visit  \\\n",
       "0          0.0                            0                   0.0   \n",
       "1          5.0                          674                   2.5   \n",
       "2          2.0                         1532                   2.0   \n",
       "3          1.0                          305                   1.0   \n",
       "4          2.0                         1428                   1.0   \n",
       "\n",
       "   Lead Origin_Landing Page Submission  Lead Origin_Lead Add Form  \\\n",
       "0                                    0                          0   \n",
       "1                                    0                          0   \n",
       "2                                    1                          0   \n",
       "3                                    1                          0   \n",
       "4                                    1                          0   \n",
       "\n",
       "   Lead Origin_Lead Import  Lead Source_Direct Traffic  Lead Source_Facebook  \\\n",
       "0                        0                           0                     0   \n",
       "1                        0                           0                     0   \n",
       "2                        0                           1                     0   \n",
       "3                        0                           1                     0   \n",
       "4                        0                           0                     0   \n",
       "\n",
       "   Lead Source_Google  Lead Source_Live Chat  ...  \\\n",
       "0                   0                      0  ...   \n",
       "1                   0                      0  ...   \n",
       "2                   0                      0  ...   \n",
       "3                   0                      0  ...   \n",
       "4                   1                      0  ...   \n",
       "\n",
       "   Last Notable Activity_Form Submitted on Website  \\\n",
       "0                                                0   \n",
       "1                                                0   \n",
       "2                                                0   \n",
       "3                                                0   \n",
       "4                                                0   \n",
       "\n",
       "   Last Notable Activity_Had a Phone Conversation  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               0   \n",
       "4                                               0   \n",
       "\n",
       "   Last Notable Activity_Modified  \\\n",
       "0                               1   \n",
       "1                               0   \n",
       "2                               0   \n",
       "3                               1   \n",
       "4                               1   \n",
       "\n",
       "   Last Notable Activity_Olark Chat Conversation  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "\n",
       "   Last Notable Activity_Page Visited on Website  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "\n",
       "   Last Notable Activity_Resubscribed to emails  \\\n",
       "0                                             0   \n",
       "1                                             0   \n",
       "2                                             0   \n",
       "3                                             0   \n",
       "4                                             0   \n",
       "\n",
       "   Last Notable Activity_SMS Sent  Last Notable Activity_Unreachable  \\\n",
       "0                               0                                  0   \n",
       "1                               0                                  0   \n",
       "2                               0                                  0   \n",
       "3                               0                                  0   \n",
       "4                               0                                  0   \n",
       "\n",
       "   Last Notable Activity_Unsubscribed  \\\n",
       "0                                   0   \n",
       "1                                   0   \n",
       "2                                   0   \n",
       "3                                   0   \n",
       "4                                   0   \n",
       "\n",
       "   Last Notable Activity_View in browser link Clicked  \n",
       "0                                                  0   \n",
       "1                                                  0   \n",
       "2                                                  0   \n",
       "3                                                  0   \n",
       "4                                                  0   \n",
       "\n",
       "[5 rows x 110 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Putting feature variable to X\n",
    "X = lead_data.drop(['Prospect ID','Converted'], axis=1)\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "Name: Converted, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Putting response variable to y\n",
    "y = lead_data['Converted']\n",
    "\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, test_size=0.3, random_state=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Scaling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>Lead Source_Live Chat</th>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1012</td>\n",
       "      <td>-0.284595</td>\n",
       "      <td>-0.540048</td>\n",
       "      <td>-0.155018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9226</td>\n",
       "      <td>-0.671384</td>\n",
       "      <td>-0.888650</td>\n",
       "      <td>-1.265540</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4750</td>\n",
       "      <td>-0.284595</td>\n",
       "      <td>1.643304</td>\n",
       "      <td>-0.155018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7987</td>\n",
       "      <td>0.295588</td>\n",
       "      <td>2.017593</td>\n",
       "      <td>0.122613</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 110 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      TotalVisits  Total Time Spent on Website  Page Views Per Visit  \\\n",
       "3009    -0.284595                    -0.160255             -0.155018   \n",
       "1012    -0.284595                    -0.540048             -0.155018   \n",
       "9226    -0.671384                    -0.888650             -1.265540   \n",
       "4750    -0.284595                     1.643304             -0.155018   \n",
       "7987     0.295588                     2.017593              0.122613   \n",
       "\n",
       "      Lead Origin_Landing Page Submission  Lead Origin_Lead Add Form  \\\n",
       "3009                                    1                          0   \n",
       "1012                                    1                          0   \n",
       "9226                                    0                          0   \n",
       "4750                                    1                          0   \n",
       "7987                                    1                          0   \n",
       "\n",
       "      Lead Origin_Lead Import  Lead Source_Direct Traffic  \\\n",
       "3009                        0                           1   \n",
       "1012                        0                           1   \n",
       "9226                        0                           0   \n",
       "4750                        0                           1   \n",
       "7987                        0                           1   \n",
       "\n",
       "      Lead Source_Facebook  Lead Source_Google  Lead Source_Live Chat  ...  \\\n",
       "3009                     0                   0                      0  ...   \n",
       "1012                     0                   0                      0  ...   \n",
       "9226                     0                   0                      0  ...   \n",
       "4750                     0                   0                      0  ...   \n",
       "7987                     0                   0                      0  ...   \n",
       "\n",
       "      Last Notable Activity_Form Submitted on Website  \\\n",
       "3009                                                0   \n",
       "1012                                                0   \n",
       "9226                                                0   \n",
       "4750                                                0   \n",
       "7987                                                0   \n",
       "\n",
       "      Last Notable Activity_Had a Phone Conversation  \\\n",
       "3009                                               0   \n",
       "1012                                               0   \n",
       "9226                                               0   \n",
       "4750                                               0   \n",
       "7987                                               0   \n",
       "\n",
       "      Last Notable Activity_Modified  \\\n",
       "3009                               0   \n",
       "1012                               0   \n",
       "9226                               1   \n",
       "4750                               0   \n",
       "7987                               1   \n",
       "\n",
       "      Last Notable Activity_Olark Chat Conversation  \\\n",
       "3009                                              0   \n",
       "1012                                              0   \n",
       "9226                                              0   \n",
       "4750                                              0   \n",
       "7987                                              0   \n",
       "\n",
       "      Last Notable Activity_Page Visited on Website  \\\n",
       "3009                                              0   \n",
       "1012                                              0   \n",
       "9226                                              0   \n",
       "4750                                              0   \n",
       "7987                                              0   \n",
       "\n",
       "      Last Notable Activity_Resubscribed to emails  \\\n",
       "3009                                             0   \n",
       "1012                                             0   \n",
       "9226                                             0   \n",
       "4750                                             0   \n",
       "7987                                             0   \n",
       "\n",
       "      Last Notable Activity_SMS Sent  Last Notable Activity_Unreachable  \\\n",
       "3009                               0                                  0   \n",
       "1012                               0                                  0   \n",
       "9226                               0                                  0   \n",
       "4750                               1                                  0   \n",
       "7987                               0                                  0   \n",
       "\n",
       "      Last Notable Activity_Unsubscribed  \\\n",
       "3009                                   0   \n",
       "1012                                   0   \n",
       "9226                                   0   \n",
       "4750                                   0   \n",
       "7987                                   0   \n",
       "\n",
       "      Last Notable Activity_View in browser link Clicked  \n",
       "3009                                                  0   \n",
       "1012                                                  0   \n",
       "9226                                                  0   \n",
       "4750                                                  0   \n",
       "7987                                                  0   \n",
       "\n",
       "[5 rows x 110 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "X_train[['TotalVisits','Total Time Spent on Website','Page Views Per Visit']] = scaler.fit_transform(X_train[['TotalVisits','Total Time Spent on Website','Page Views Per Visit']])\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Converted</td>    <th>  No. Observations:  </th>  <td>  6351</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  6243</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>   107</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>         <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -1553.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Mon, 20 Apr 2020</td> <th>  Deviance:          </th> <td>  3106.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>16:24:10</td>     <th>  Pearson chi2:      </th> <td>1.22e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>         <td>24</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                             <td></td>                               <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                                  <td>   27.2941</td> <td> 2.19e+05</td> <td>    0.000</td> <td> 1.000</td> <td> -4.3e+05</td> <td>  4.3e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TotalVisits</th>                                            <td>    0.1300</td> <td>    0.036</td> <td>    3.621</td> <td> 0.000</td> <td>    0.060</td> <td>    0.200</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Time Spent on Website</th>                            <td>    1.1841</td> <td>    0.057</td> <td>   20.832</td> <td> 0.000</td> <td>    1.073</td> <td>    1.295</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Page Views Per Visit</th>                                   <td>   -0.2027</td> <td>    0.074</td> <td>   -2.740</td> <td> 0.006</td> <td>   -0.348</td> <td>   -0.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Landing Page Submission</th>                    <td>   -1.2614</td> <td>    0.189</td> <td>   -6.680</td> <td> 0.000</td> <td>   -1.632</td> <td>   -0.891</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Lead Add Form</th>                              <td>   -3.7021</td> <td>    3.079</td> <td>   -1.202</td> <td> 0.229</td> <td>   -9.738</td> <td>    2.333</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Lead Import</th>                                <td>   28.1785</td> <td> 2.13e+05</td> <td>    0.000</td> <td> 1.000</td> <td>-4.17e+05</td> <td> 4.17e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Direct Traffic</th>                             <td>   -6.1538</td> <td>    3.647</td> <td>   -1.687</td> <td> 0.092</td> <td>  -13.301</td> <td>    0.994</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Facebook</th>                                   <td>  -32.9437</td> <td> 2.13e+05</td> <td>   -0.000</td> <td> 1.000</td> <td>-4.17e+05</td> <td> 4.17e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Google</th>                                     <td>   -5.8980</td> <td>    3.646</td> <td>   -1.618</td> <td> 0.106</td> <td>  -13.043</td> <td>    1.247</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Live Chat</th>                                  <td>   22.5988</td> <td> 1.54e+05</td> <td>    0.000</td> <td> 1.000</td> <td>-3.02e+05</td> <td> 3.02e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_NC_EDM</th>                                     <td>   19.0210</td> <td> 2.17e+05</td> <td> 8.78e-05</td> <td> 1.000</td> <td>-4.25e+05</td> <td> 4.25e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Olark Chat</th>                                 <td>   -5.2498</td> <td>    3.639</td> <td>   -1.443</td> <td> 0.149</td> <td>  -12.382</td> <td>    1.882</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Organic Search</th>                             <td>   -5.8762</td> <td>    3.649</td> <td>   -1.611</td> <td> 0.107</td> <td>  -13.027</td> <td>    1.275</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Pay per Click Ads</th>                          <td>  -25.1315</td> <td> 2.18e+05</td> <td>   -0.000</td> <td> 1.000</td> <td>-4.26e+05</td> <td> 4.26e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Press_Release</th>                              <td>  -31.0703</td> <td> 2.17e+05</td> <td>   -0.000</td> <td> 1.000</td> <td>-4.25e+05</td> <td> 4.25e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Reference</th>                                  <td>    0.3456</td> <td>    1.959</td> <td>    0.176</td> <td> 0.860</td> <td>   -3.493</td> <td>    4.184</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Referral Sites</th>                             <td>   -6.0925</td> <td>    3.668</td> <td>   -1.661</td> <td> 0.097</td> <td>  -13.281</td> <td>    1.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Social Media</th>                               <td>   -2.8463</td> <td>    4.807</td> <td>   -0.592</td> <td> 0.554</td> <td>  -12.267</td> <td>    6.574</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_WeLearn</th>                                    <td>-2.497e-09</td> <td> 7.39e-05</td> <td>-3.38e-05</td> <td> 1.000</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Welingak Website</th>                           <td>    2.3330</td> <td>    2.076</td> <td>    1.124</td> <td> 0.261</td> <td>   -1.737</td> <td>    6.403</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_bing</th>                                       <td>   -5.4429</td> <td>    4.116</td> <td>   -1.322</td> <td> 0.186</td> <td>  -13.510</td> <td>    2.624</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_blog</th>                                       <td>  -31.2462</td> <td> 2.15e+05</td> <td>   -0.000</td> <td> 1.000</td> <td>-4.21e+05</td> <td> 4.21e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_google</th>                                     <td>  -29.5631</td> <td> 1.02e+05</td> <td>   -0.000</td> <td> 1.000</td> <td>   -2e+05</td> <td>    2e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_testone</th>                                    <td>  -24.9513</td> <td> 2.19e+05</td> <td>   -0.000</td> <td> 1.000</td> <td>-4.29e+05</td> <td> 4.29e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_welearnblog_Home</th>                           <td>  -31.1866</td> <td> 2.14e+05</td> <td>   -0.000</td> <td> 1.000</td> <td>-4.19e+05</td> <td> 4.19e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_youtubechannel</th>                             <td>  -31.4053</td> <td>  2.1e+05</td> <td>   -0.000</td> <td> 1.000</td> <td>-4.12e+05</td> <td> 4.11e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Converted to Lead</th>                        <td>  -21.7437</td> <td> 9.87e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.93e+05</td> <td> 1.93e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Bounced</th>                            <td>  -24.1079</td> <td> 9.87e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.93e+05</td> <td> 1.93e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Link Clicked</th>                       <td>  -20.8466</td> <td> 9.87e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.93e+05</td> <td> 1.93e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Marked Spam</th>                        <td>   -0.5627</td> <td> 1.33e+05</td> <td>-4.23e-06</td> <td> 1.000</td> <td>-2.61e+05</td> <td> 2.61e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Opened</th>                             <td>  -21.7299</td> <td> 9.87e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.93e+05</td> <td> 1.93e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Received</th>                           <td>    0.7287</td> <td> 2.39e+05</td> <td> 3.05e-06</td> <td> 1.000</td> <td>-4.69e+05</td> <td> 4.69e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Form Submitted on Website</th>                <td>  -21.6627</td> <td> 9.87e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.93e+05</td> <td> 1.93e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Had a Phone Conversation</th>                 <td>  -18.2643</td> <td> 9.87e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.93e+05</td> <td> 1.93e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Olark Chat Conversation</th>                  <td>  -22.2994</td> <td> 9.87e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.93e+05</td> <td> 1.93e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Page Visited on Website</th>                  <td>  -21.5368</td> <td> 9.87e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.93e+05</td> <td> 1.93e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Resubscribed to emails</th>                   <td>   -1.3399</td> <td> 1.55e+05</td> <td>-8.62e-06</td> <td> 1.000</td> <td>-3.05e+05</td> <td> 3.05e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_SMS Sent</th>                                 <td>  -20.8654</td> <td> 9.87e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.93e+05</td> <td> 1.93e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Unreachable</th>                              <td>  -20.7475</td> <td> 9.87e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.93e+05</td> <td> 1.93e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Unsubscribed</th>                             <td>  -20.7081</td> <td> 9.87e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.93e+05</td> <td> 1.93e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_View in browser link Clicked</th>             <td>  -21.1658</td> <td> 9.87e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.93e+05</td> <td> 1.93e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Visited Booth in Tradeshow</th>               <td>  -18.1075</td> <td> 2.45e+05</td> <td> -7.4e-05</td> <td> 1.000</td> <td> -4.8e+05</td> <td>  4.8e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Business Administration</th>                 <td>   -0.2392</td> <td>    0.333</td> <td>   -0.719</td> <td> 0.472</td> <td>   -0.892</td> <td>    0.413</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_E-Business</th>                              <td>   -0.5975</td> <td>    0.645</td> <td>   -0.926</td> <td> 0.354</td> <td>   -1.861</td> <td>    0.667</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_E-COMMERCE</th>                              <td>   -0.0742</td> <td>    0.507</td> <td>   -0.146</td> <td> 0.884</td> <td>   -1.069</td> <td>    0.920</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Finance Management</th>                      <td>   -0.3503</td> <td>    0.290</td> <td>   -1.210</td> <td> 0.226</td> <td>   -0.918</td> <td>    0.217</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Healthcare Management</th>                   <td>   -0.4505</td> <td>    0.435</td> <td>   -1.036</td> <td> 0.300</td> <td>   -1.303</td> <td>    0.402</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Hospitality Management</th>                  <td>   -0.6476</td> <td>    0.508</td> <td>   -1.275</td> <td> 0.202</td> <td>   -1.643</td> <td>    0.348</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Human Resource Management</th>               <td>   -0.2602</td> <td>    0.294</td> <td>   -0.886</td> <td> 0.375</td> <td>   -0.836</td> <td>    0.315</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_IT Projects Management</th>                  <td>   -0.1468</td> <td>    0.348</td> <td>   -0.422</td> <td> 0.673</td> <td>   -0.829</td> <td>    0.536</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_International Business</th>                  <td>   -0.8973</td> <td>    0.386</td> <td>   -2.327</td> <td> 0.020</td> <td>   -1.653</td> <td>   -0.141</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Marketing Management</th>                    <td>    0.0752</td> <td>    0.293</td> <td>    0.257</td> <td> 0.797</td> <td>   -0.499</td> <td>    0.649</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Media and Advertising</th>                   <td>   -0.6600</td> <td>    0.400</td> <td>   -1.648</td> <td> 0.099</td> <td>   -1.445</td> <td>    0.125</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Operations Management</th>                   <td>   -0.2552</td> <td>    0.329</td> <td>   -0.777</td> <td> 0.437</td> <td>   -0.899</td> <td>    0.389</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Retail Management</th>                       <td>   -0.4487</td> <td>    0.508</td> <td>   -0.884</td> <td> 0.377</td> <td>   -1.444</td> <td>    0.546</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Rural and Agribusiness</th>                  <td>   -0.6017</td> <td>    0.571</td> <td>   -1.055</td> <td> 0.292</td> <td>   -1.720</td> <td>    0.517</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Services Excellence</th>                     <td>   -0.4451</td> <td>    0.894</td> <td>   -0.498</td> <td> 0.618</td> <td>   -2.197</td> <td>    1.306</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Supply Chain Management</th>                 <td>   -0.3910</td> <td>    0.348</td> <td>   -1.122</td> <td> 0.262</td> <td>   -1.074</td> <td>    0.292</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Travel and Tourism</th>                      <td>   -0.5720</td> <td>    0.429</td> <td>   -1.334</td> <td> 0.182</td> <td>   -1.412</td> <td>    0.268</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Unknown</th>                                 <td>   -1.6779</td> <td>    0.301</td> <td>   -5.576</td> <td> 0.000</td> <td>   -2.268</td> <td>   -1.088</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What is your current occupation_Housewife</th>              <td>   23.4472</td> <td> 6.78e+04</td> <td>    0.000</td> <td> 1.000</td> <td>-1.33e+05</td> <td> 1.33e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What is your current occupation_Other</th>                  <td>    0.1553</td> <td>    1.929</td> <td>    0.081</td> <td> 0.936</td> <td>   -3.626</td> <td>    3.936</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What is your current occupation_Student</th>                <td>    0.7624</td> <td>    1.709</td> <td>    0.446</td> <td> 0.656</td> <td>   -2.587</td> <td>    4.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What is your current occupation_Unemployed</th>             <td>   -1.3148</td> <td>    1.647</td> <td>   -0.798</td> <td> 0.425</td> <td>   -4.543</td> <td>    1.913</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What is your current occupation_Working Professional</th>   <td>    1.5337</td> <td>    1.671</td> <td>    0.918</td> <td> 0.359</td> <td>   -1.741</td> <td>    4.808</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Busy</th>                                              <td>    4.4803</td> <td>    0.805</td> <td>    5.567</td> <td> 0.000</td> <td>    2.903</td> <td>    6.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Closed by Horizzon</th>                                <td>   10.3915</td> <td>    1.072</td> <td>    9.689</td> <td> 0.000</td> <td>    8.289</td> <td>   12.493</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Diploma holder (Not Eligible)</th>                     <td>    0.1074</td> <td>    1.471</td> <td>    0.073</td> <td> 0.942</td> <td>   -2.776</td> <td>    2.991</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Graduation in progress</th>                            <td>    3.2266</td> <td>    0.927</td> <td>    3.480</td> <td> 0.001</td> <td>    1.409</td> <td>    5.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_In confusion whether part time or DLP</th>             <td>    3.5708</td> <td>    1.848</td> <td>    1.933</td> <td> 0.053</td> <td>   -0.050</td> <td>    7.192</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Interested  in full time MBA</th>                      <td>    1.2225</td> <td>    1.115</td> <td>    1.096</td> <td> 0.273</td> <td>   -0.964</td> <td>    3.409</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Interested in Next batch</th>                          <td>   27.0825</td> <td> 9.93e+04</td> <td>    0.000</td> <td> 1.000</td> <td>-1.95e+05</td> <td> 1.95e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Interested in other courses</th>                       <td>    1.7650</td> <td>    0.843</td> <td>    2.094</td> <td> 0.036</td> <td>    0.113</td> <td>    3.417</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Lateral student</th>                                   <td>   30.4358</td> <td>  1.3e+05</td> <td>    0.000</td> <td> 1.000</td> <td>-2.54e+05</td> <td> 2.54e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Lost to EINS</th>                                      <td>   10.1158</td> <td>    1.064</td> <td>    9.505</td> <td> 0.000</td> <td>    8.030</td> <td>   12.202</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Lost to Others</th>                                    <td>  -22.9294</td> <td> 6.11e+04</td> <td>   -0.000</td> <td> 1.000</td> <td> -1.2e+05</td> <td>  1.2e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Not doing further education</th>                       <td>    0.4846</td> <td>    1.534</td> <td>    0.316</td> <td> 0.752</td> <td>   -2.523</td> <td>    3.492</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Recognition issue (DEC approval)</th>                  <td>  -21.9496</td> <td> 2.17e+05</td> <td>   -0.000</td> <td> 1.000</td> <td>-4.26e+05</td> <td> 4.26e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Ringing</th>                                           <td>    0.0810</td> <td>    0.809</td> <td>    0.100</td> <td> 0.920</td> <td>   -1.505</td> <td>    1.667</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Shall take in the next coming month</th>               <td>  -21.3750</td> <td> 2.16e+05</td> <td>-9.91e-05</td> <td> 1.000</td> <td>-4.23e+05</td> <td> 4.23e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Still Thinking</th>                                    <td>    2.3959</td> <td>    2.203</td> <td>    1.088</td> <td> 0.277</td> <td>   -1.922</td> <td>    6.713</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_University not recognized</th>                         <td>  -19.5605</td> <td> 1.47e+05</td> <td>   -0.000</td> <td> 1.000</td> <td>-2.88e+05</td> <td> 2.88e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Want to take admission but has financial problems</th> <td>    2.7964</td> <td>    1.477</td> <td>    1.893</td> <td> 0.058</td> <td>   -0.099</td> <td>    5.692</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Will revert after reading the email</th>               <td>    5.2004</td> <td>    0.766</td> <td>    6.786</td> <td> 0.000</td> <td>    3.698</td> <td>    6.702</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_in touch with EINS</th>                                <td>    4.1856</td> <td>    1.355</td> <td>    3.089</td> <td> 0.002</td> <td>    1.530</td> <td>    6.842</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_invalid number</th>                                    <td>  -20.5167</td> <td> 2.44e+04</td> <td>   -0.001</td> <td> 0.999</td> <td>-4.79e+04</td> <td> 4.78e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_number not provided</th>                               <td>  -21.6186</td> <td> 3.95e+04</td> <td>   -0.001</td> <td> 1.000</td> <td>-7.75e+04</td> <td> 7.74e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_opp hangup</th>                                        <td>    2.4956</td> <td>    1.090</td> <td>    2.290</td> <td> 0.022</td> <td>    0.360</td> <td>    4.631</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_switched off</th>                                      <td>    0.2283</td> <td>    0.951</td> <td>    0.240</td> <td> 0.810</td> <td>   -1.635</td> <td>    2.092</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_wrong number given</th>                                <td>  -21.2667</td> <td> 3.18e+04</td> <td>   -0.001</td> <td> 0.999</td> <td>-6.24e+04</td> <td> 6.24e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>City_Other Cities</th>                                      <td>    0.1855</td> <td>    0.189</td> <td>    0.983</td> <td> 0.326</td> <td>   -0.184</td> <td>    0.555</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>City_Other Cities of Maharashtra</th>                       <td>    0.1307</td> <td>    0.223</td> <td>    0.585</td> <td> 0.558</td> <td>   -0.307</td> <td>    0.568</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>City_Other Metro Cities</th>                                <td>    0.1137</td> <td>    0.234</td> <td>    0.486</td> <td> 0.627</td> <td>   -0.344</td> <td>    0.572</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>City_Thane & Outskirts</th>                                 <td>    0.1061</td> <td>    0.182</td> <td>    0.582</td> <td> 0.560</td> <td>   -0.251</td> <td>    0.463</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>City_Tier II Cities</th>                                    <td>    0.6986</td> <td>    0.617</td> <td>    1.133</td> <td> 0.257</td> <td>   -0.510</td> <td>    1.907</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Email Bounced</th>                    <td>   -1.1093</td> <td> 2.41e+05</td> <td>-4.61e-06</td> <td> 1.000</td> <td>-4.72e+05</td> <td> 4.72e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Email Link Clicked</th>               <td>   -4.2112</td> <td> 2.41e+05</td> <td>-1.75e-05</td> <td> 1.000</td> <td>-4.72e+05</td> <td> 4.72e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Email Marked Spam</th>                <td>   -0.5627</td> <td> 1.33e+05</td> <td>-4.23e-06</td> <td> 1.000</td> <td>-2.61e+05</td> <td> 2.61e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Email Opened</th>                     <td>   -2.4954</td> <td> 2.41e+05</td> <td>-1.04e-05</td> <td> 1.000</td> <td>-4.72e+05</td> <td> 4.72e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Email Received</th>                   <td>   -2.3254</td> <td> 3.92e+05</td> <td>-5.94e-06</td> <td> 1.000</td> <td>-7.68e+05</td> <td> 7.68e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Form Submitted on Website</th>        <td>  -27.3498</td> <td> 3.24e+05</td> <td>-8.44e-05</td> <td> 1.000</td> <td>-6.35e+05</td> <td> 6.35e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Had a Phone Conversation</th>         <td>   -3.5566</td> <td> 2.41e+05</td> <td>-1.48e-05</td> <td> 1.000</td> <td>-4.72e+05</td> <td> 4.72e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Modified</th>                         <td>   -3.7780</td> <td> 2.41e+05</td> <td>-1.57e-05</td> <td> 1.000</td> <td>-4.72e+05</td> <td> 4.72e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Olark Chat Conversation</th>          <td>   -3.9974</td> <td> 2.41e+05</td> <td>-1.66e-05</td> <td> 1.000</td> <td>-4.72e+05</td> <td> 4.72e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Page Visited on Website</th>          <td>   -3.7748</td> <td> 2.41e+05</td> <td>-1.57e-05</td> <td> 1.000</td> <td>-4.72e+05</td> <td> 4.72e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Resubscribed to emails</th>           <td>   -1.3399</td> <td> 1.55e+05</td> <td>-8.62e-06</td> <td> 1.000</td> <td>-3.05e+05</td> <td> 3.05e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_SMS Sent</th>                         <td>   -1.0651</td> <td> 2.41e+05</td> <td>-4.43e-06</td> <td> 1.000</td> <td>-4.72e+05</td> <td> 4.72e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Unreachable</th>                      <td>   -2.4979</td> <td> 2.41e+05</td> <td>-1.04e-05</td> <td> 1.000</td> <td>-4.72e+05</td> <td> 4.72e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Unsubscribed</th>                     <td>   -3.1256</td> <td> 2.41e+05</td> <td> -1.3e-05</td> <td> 1.000</td> <td>-4.72e+05</td> <td> 4.72e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_View in browser link Clicked</th>     <td>  -23.4812</td> <td> 3.24e+05</td> <td>-7.25e-05</td> <td> 1.000</td> <td>-6.35e+05</td> <td> 6.35e+05</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:              Converted   No. Observations:                 6351\n",
       "Model:                            GLM   Df Residuals:                     6243\n",
       "Model Family:                Binomial   Df Model:                          107\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -1553.2\n",
       "Date:                Mon, 20 Apr 2020   Deviance:                       3106.3\n",
       "Time:                        16:24:10   Pearson chi2:                 1.22e+04\n",
       "No. Iterations:                    24                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==========================================================================================================================\n",
       "                                                             coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------------------------------------\n",
       "const                                                     27.2941   2.19e+05      0.000      1.000    -4.3e+05     4.3e+05\n",
       "TotalVisits                                                0.1300      0.036      3.621      0.000       0.060       0.200\n",
       "Total Time Spent on Website                                1.1841      0.057     20.832      0.000       1.073       1.295\n",
       "Page Views Per Visit                                      -0.2027      0.074     -2.740      0.006      -0.348      -0.058\n",
       "Lead Origin_Landing Page Submission                       -1.2614      0.189     -6.680      0.000      -1.632      -0.891\n",
       "Lead Origin_Lead Add Form                                 -3.7021      3.079     -1.202      0.229      -9.738       2.333\n",
       "Lead Origin_Lead Import                                   28.1785   2.13e+05      0.000      1.000   -4.17e+05    4.17e+05\n",
       "Lead Source_Direct Traffic                                -6.1538      3.647     -1.687      0.092     -13.301       0.994\n",
       "Lead Source_Facebook                                     -32.9437   2.13e+05     -0.000      1.000   -4.17e+05    4.17e+05\n",
       "Lead Source_Google                                        -5.8980      3.646     -1.618      0.106     -13.043       1.247\n",
       "Lead Source_Live Chat                                     22.5988   1.54e+05      0.000      1.000   -3.02e+05    3.02e+05\n",
       "Lead Source_NC_EDM                                        19.0210   2.17e+05   8.78e-05      1.000   -4.25e+05    4.25e+05\n",
       "Lead Source_Olark Chat                                    -5.2498      3.639     -1.443      0.149     -12.382       1.882\n",
       "Lead Source_Organic Search                                -5.8762      3.649     -1.611      0.107     -13.027       1.275\n",
       "Lead Source_Pay per Click Ads                            -25.1315   2.18e+05     -0.000      1.000   -4.26e+05    4.26e+05\n",
       "Lead Source_Press_Release                                -31.0703   2.17e+05     -0.000      1.000   -4.25e+05    4.25e+05\n",
       "Lead Source_Reference                                      0.3456      1.959      0.176      0.860      -3.493       4.184\n",
       "Lead Source_Referral Sites                                -6.0925      3.668     -1.661      0.097     -13.281       1.096\n",
       "Lead Source_Social Media                                  -2.8463      4.807     -0.592      0.554     -12.267       6.574\n",
       "Lead Source_WeLearn                                    -2.497e-09   7.39e-05  -3.38e-05      1.000      -0.000       0.000\n",
       "Lead Source_Welingak Website                               2.3330      2.076      1.124      0.261      -1.737       6.403\n",
       "Lead Source_bing                                          -5.4429      4.116     -1.322      0.186     -13.510       2.624\n",
       "Lead Source_blog                                         -31.2462   2.15e+05     -0.000      1.000   -4.21e+05    4.21e+05\n",
       "Lead Source_google                                       -29.5631   1.02e+05     -0.000      1.000      -2e+05       2e+05\n",
       "Lead Source_testone                                      -24.9513   2.19e+05     -0.000      1.000   -4.29e+05    4.29e+05\n",
       "Lead Source_welearnblog_Home                             -31.1866   2.14e+05     -0.000      1.000   -4.19e+05    4.19e+05\n",
       "Lead Source_youtubechannel                               -31.4053    2.1e+05     -0.000      1.000   -4.12e+05    4.11e+05\n",
       "Last Activity_Converted to Lead                          -21.7437   9.87e+04     -0.000      1.000   -1.93e+05    1.93e+05\n",
       "Last Activity_Email Bounced                              -24.1079   9.87e+04     -0.000      1.000   -1.93e+05    1.93e+05\n",
       "Last Activity_Email Link Clicked                         -20.8466   9.87e+04     -0.000      1.000   -1.93e+05    1.93e+05\n",
       "Last Activity_Email Marked Spam                           -0.5627   1.33e+05  -4.23e-06      1.000   -2.61e+05    2.61e+05\n",
       "Last Activity_Email Opened                               -21.7299   9.87e+04     -0.000      1.000   -1.93e+05    1.93e+05\n",
       "Last Activity_Email Received                               0.7287   2.39e+05   3.05e-06      1.000   -4.69e+05    4.69e+05\n",
       "Last Activity_Form Submitted on Website                  -21.6627   9.87e+04     -0.000      1.000   -1.93e+05    1.93e+05\n",
       "Last Activity_Had a Phone Conversation                   -18.2643   9.87e+04     -0.000      1.000   -1.93e+05    1.93e+05\n",
       "Last Activity_Olark Chat Conversation                    -22.2994   9.87e+04     -0.000      1.000   -1.93e+05    1.93e+05\n",
       "Last Activity_Page Visited on Website                    -21.5368   9.87e+04     -0.000      1.000   -1.93e+05    1.93e+05\n",
       "Last Activity_Resubscribed to emails                      -1.3399   1.55e+05  -8.62e-06      1.000   -3.05e+05    3.05e+05\n",
       "Last Activity_SMS Sent                                   -20.8654   9.87e+04     -0.000      1.000   -1.93e+05    1.93e+05\n",
       "Last Activity_Unreachable                                -20.7475   9.87e+04     -0.000      1.000   -1.93e+05    1.93e+05\n",
       "Last Activity_Unsubscribed                               -20.7081   9.87e+04     -0.000      1.000   -1.93e+05    1.93e+05\n",
       "Last Activity_View in browser link Clicked               -21.1658   9.87e+04     -0.000      1.000   -1.93e+05    1.93e+05\n",
       "Last Activity_Visited Booth in Tradeshow                 -18.1075   2.45e+05   -7.4e-05      1.000    -4.8e+05     4.8e+05\n",
       "Specialization_Business Administration                    -0.2392      0.333     -0.719      0.472      -0.892       0.413\n",
       "Specialization_E-Business                                 -0.5975      0.645     -0.926      0.354      -1.861       0.667\n",
       "Specialization_E-COMMERCE                                 -0.0742      0.507     -0.146      0.884      -1.069       0.920\n",
       "Specialization_Finance Management                         -0.3503      0.290     -1.210      0.226      -0.918       0.217\n",
       "Specialization_Healthcare Management                      -0.4505      0.435     -1.036      0.300      -1.303       0.402\n",
       "Specialization_Hospitality Management                     -0.6476      0.508     -1.275      0.202      -1.643       0.348\n",
       "Specialization_Human Resource Management                  -0.2602      0.294     -0.886      0.375      -0.836       0.315\n",
       "Specialization_IT Projects Management                     -0.1468      0.348     -0.422      0.673      -0.829       0.536\n",
       "Specialization_International Business                     -0.8973      0.386     -2.327      0.020      -1.653      -0.141\n",
       "Specialization_Marketing Management                        0.0752      0.293      0.257      0.797      -0.499       0.649\n",
       "Specialization_Media and Advertising                      -0.6600      0.400     -1.648      0.099      -1.445       0.125\n",
       "Specialization_Operations Management                      -0.2552      0.329     -0.777      0.437      -0.899       0.389\n",
       "Specialization_Retail Management                          -0.4487      0.508     -0.884      0.377      -1.444       0.546\n",
       "Specialization_Rural and Agribusiness                     -0.6017      0.571     -1.055      0.292      -1.720       0.517\n",
       "Specialization_Services Excellence                        -0.4451      0.894     -0.498      0.618      -2.197       1.306\n",
       "Specialization_Supply Chain Management                    -0.3910      0.348     -1.122      0.262      -1.074       0.292\n",
       "Specialization_Travel and Tourism                         -0.5720      0.429     -1.334      0.182      -1.412       0.268\n",
       "Specialization_Unknown                                    -1.6779      0.301     -5.576      0.000      -2.268      -1.088\n",
       "What is your current occupation_Housewife                 23.4472   6.78e+04      0.000      1.000   -1.33e+05    1.33e+05\n",
       "What is your current occupation_Other                      0.1553      1.929      0.081      0.936      -3.626       3.936\n",
       "What is your current occupation_Student                    0.7624      1.709      0.446      0.656      -2.587       4.112\n",
       "What is your current occupation_Unemployed                -1.3148      1.647     -0.798      0.425      -4.543       1.913\n",
       "What is your current occupation_Working Professional       1.5337      1.671      0.918      0.359      -1.741       4.808\n",
       "Tags_Busy                                                  4.4803      0.805      5.567      0.000       2.903       6.058\n",
       "Tags_Closed by Horizzon                                   10.3915      1.072      9.689      0.000       8.289      12.493\n",
       "Tags_Diploma holder (Not Eligible)                         0.1074      1.471      0.073      0.942      -2.776       2.991\n",
       "Tags_Graduation in progress                                3.2266      0.927      3.480      0.001       1.409       5.044\n",
       "Tags_In confusion whether part time or DLP                 3.5708      1.848      1.933      0.053      -0.050       7.192\n",
       "Tags_Interested  in full time MBA                          1.2225      1.115      1.096      0.273      -0.964       3.409\n",
       "Tags_Interested in Next batch                             27.0825   9.93e+04      0.000      1.000   -1.95e+05    1.95e+05\n",
       "Tags_Interested in other courses                           1.7650      0.843      2.094      0.036       0.113       3.417\n",
       "Tags_Lateral student                                      30.4358    1.3e+05      0.000      1.000   -2.54e+05    2.54e+05\n",
       "Tags_Lost to EINS                                         10.1158      1.064      9.505      0.000       8.030      12.202\n",
       "Tags_Lost to Others                                      -22.9294   6.11e+04     -0.000      1.000    -1.2e+05     1.2e+05\n",
       "Tags_Not doing further education                           0.4846      1.534      0.316      0.752      -2.523       3.492\n",
       "Tags_Recognition issue (DEC approval)                    -21.9496   2.17e+05     -0.000      1.000   -4.26e+05    4.26e+05\n",
       "Tags_Ringing                                               0.0810      0.809      0.100      0.920      -1.505       1.667\n",
       "Tags_Shall take in the next coming month                 -21.3750   2.16e+05  -9.91e-05      1.000   -4.23e+05    4.23e+05\n",
       "Tags_Still Thinking                                        2.3959      2.203      1.088      0.277      -1.922       6.713\n",
       "Tags_University not recognized                           -19.5605   1.47e+05     -0.000      1.000   -2.88e+05    2.88e+05\n",
       "Tags_Want to take admission but has financial problems     2.7964      1.477      1.893      0.058      -0.099       5.692\n",
       "Tags_Will revert after reading the email                   5.2004      0.766      6.786      0.000       3.698       6.702\n",
       "Tags_in touch with EINS                                    4.1856      1.355      3.089      0.002       1.530       6.842\n",
       "Tags_invalid number                                      -20.5167   2.44e+04     -0.001      0.999   -4.79e+04    4.78e+04\n",
       "Tags_number not provided                                 -21.6186   3.95e+04     -0.001      1.000   -7.75e+04    7.74e+04\n",
       "Tags_opp hangup                                            2.4956      1.090      2.290      0.022       0.360       4.631\n",
       "Tags_switched off                                          0.2283      0.951      0.240      0.810      -1.635       2.092\n",
       "Tags_wrong number given                                  -21.2667   3.18e+04     -0.001      0.999   -6.24e+04    6.24e+04\n",
       "City_Other Cities                                          0.1855      0.189      0.983      0.326      -0.184       0.555\n",
       "City_Other Cities of Maharashtra                           0.1307      0.223      0.585      0.558      -0.307       0.568\n",
       "City_Other Metro Cities                                    0.1137      0.234      0.486      0.627      -0.344       0.572\n",
       "City_Thane & Outskirts                                     0.1061      0.182      0.582      0.560      -0.251       0.463\n",
       "City_Tier II Cities                                        0.6986      0.617      1.133      0.257      -0.510       1.907\n",
       "Last Notable Activity_Email Bounced                       -1.1093   2.41e+05  -4.61e-06      1.000   -4.72e+05    4.72e+05\n",
       "Last Notable Activity_Email Link Clicked                  -4.2112   2.41e+05  -1.75e-05      1.000   -4.72e+05    4.72e+05\n",
       "Last Notable Activity_Email Marked Spam                   -0.5627   1.33e+05  -4.23e-06      1.000   -2.61e+05    2.61e+05\n",
       "Last Notable Activity_Email Opened                        -2.4954   2.41e+05  -1.04e-05      1.000   -4.72e+05    4.72e+05\n",
       "Last Notable Activity_Email Received                      -2.3254   3.92e+05  -5.94e-06      1.000   -7.68e+05    7.68e+05\n",
       "Last Notable Activity_Form Submitted on Website          -27.3498   3.24e+05  -8.44e-05      1.000   -6.35e+05    6.35e+05\n",
       "Last Notable Activity_Had a Phone Conversation            -3.5566   2.41e+05  -1.48e-05      1.000   -4.72e+05    4.72e+05\n",
       "Last Notable Activity_Modified                            -3.7780   2.41e+05  -1.57e-05      1.000   -4.72e+05    4.72e+05\n",
       "Last Notable Activity_Olark Chat Conversation             -3.9974   2.41e+05  -1.66e-05      1.000   -4.72e+05    4.72e+05\n",
       "Last Notable Activity_Page Visited on Website             -3.7748   2.41e+05  -1.57e-05      1.000   -4.72e+05    4.72e+05\n",
       "Last Notable Activity_Resubscribed to emails              -1.3399   1.55e+05  -8.62e-06      1.000   -3.05e+05    3.05e+05\n",
       "Last Notable Activity_SMS Sent                            -1.0651   2.41e+05  -4.43e-06      1.000   -4.72e+05    4.72e+05\n",
       "Last Notable Activity_Unreachable                         -2.4979   2.41e+05  -1.04e-05      1.000   -4.72e+05    4.72e+05\n",
       "Last Notable Activity_Unsubscribed                        -3.1256   2.41e+05   -1.3e-05      1.000   -4.72e+05    4.72e+05\n",
       "Last Notable Activity_View in browser link Clicked       -23.4812   3.24e+05  -7.25e-05      1.000   -6.35e+05    6.35e+05\n",
       "==========================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic regression model\n",
    "log1 = sm.GLM(y_train,(sm.add_constant(X_train)), family = sm.families.Binomial())\n",
    "log1.fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Selection Using RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log_reg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "rfe_1 = RFE(log_reg, 15)             # running RFE with 15 variables as output\n",
    "rfe_1 = rfe_1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False,  True,  True, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False,  True, False, False, False, False, False, False, False,\n",
       "        True, False, False, False, False, False,  True, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False,  True, False, False, False,\n",
       "        True, False,  True,  True, False, False, False, False, False,\n",
       "       False, False,  True, False, False, False,  True, False, False,\n",
       "       False, False,  True, False,  True, False, False,  True, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False,  True, False,\n",
       "       False, False])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfe_1.support_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('TotalVisits', False, 65),\n",
       " ('Total Time Spent on Website', False, 6),\n",
       " ('Page Views Per Visit', False, 55),\n",
       " ('Lead Origin_Landing Page Submission', True, 1),\n",
       " ('Lead Origin_Lead Add Form', True, 1),\n",
       " ('Lead Origin_Lead Import', False, 33),\n",
       " ('Lead Source_Direct Traffic', False, 22),\n",
       " ('Lead Source_Facebook', False, 81),\n",
       " ('Lead Source_Google', False, 24),\n",
       " ('Lead Source_Live Chat', False, 67),\n",
       " ('Lead Source_NC_EDM', False, 46),\n",
       " ('Lead Source_Olark Chat', False, 38),\n",
       " ('Lead Source_Organic Search', False, 23),\n",
       " ('Lead Source_Pay per Click Ads', False, 94),\n",
       " ('Lead Source_Press_Release', False, 28),\n",
       " ('Lead Source_Reference', False, 32),\n",
       " ('Lead Source_Referral Sites', False, 21),\n",
       " ('Lead Source_Social Media', False, 39),\n",
       " ('Lead Source_WeLearn', False, 96),\n",
       " ('Lead Source_Welingak Website', True, 1),\n",
       " ('Lead Source_bing', False, 92),\n",
       " ('Lead Source_blog', False, 29),\n",
       " ('Lead Source_google', False, 26),\n",
       " ('Lead Source_testone', False, 93),\n",
       " ('Lead Source_welearnblog_Home', False, 40),\n",
       " ('Lead Source_youtubechannel', False, 31),\n",
       " ('Last Activity_Converted to Lead', False, 54),\n",
       " ('Last Activity_Email Bounced', True, 1),\n",
       " ('Last Activity_Email Link Clicked', False, 35),\n",
       " ('Last Activity_Email Marked Spam', False, 53),\n",
       " ('Last Activity_Email Opened', False, 60),\n",
       " ('Last Activity_Email Received', False, 72),\n",
       " ('Last Activity_Form Submitted on Website', False, 58),\n",
       " ('Last Activity_Had a Phone Conversation', True, 1),\n",
       " ('Last Activity_Olark Chat Conversation', False, 20),\n",
       " ('Last Activity_Page Visited on Website', False, 59),\n",
       " ('Last Activity_Resubscribed to emails', False, 76),\n",
       " ('Last Activity_SMS Sent', False, 10),\n",
       " ('Last Activity_Unreachable', False, 12),\n",
       " ('Last Activity_Unsubscribed', False, 75),\n",
       " ('Last Activity_View in browser link Clicked', False, 88),\n",
       " ('Last Activity_Visited Booth in Tradeshow', False, 95),\n",
       " ('Specialization_Business Administration', False, 83),\n",
       " ('Specialization_E-Business', False, 70),\n",
       " ('Specialization_E-COMMERCE', False, 51),\n",
       " ('Specialization_Finance Management', False, 74),\n",
       " ('Specialization_Healthcare Management', False, 57),\n",
       " ('Specialization_Hospitality Management', False, 42),\n",
       " ('Specialization_Human Resource Management', False, 89),\n",
       " ('Specialization_IT Projects Management', False, 90),\n",
       " ('Specialization_International Business', False, 25),\n",
       " ('Specialization_Marketing Management', False, 45),\n",
       " ('Specialization_Media and Advertising', False, 37),\n",
       " ('Specialization_Operations Management', False, 82),\n",
       " ('Specialization_Retail Management', False, 56),\n",
       " ('Specialization_Rural and Agribusiness', False, 64),\n",
       " ('Specialization_Services Excellence', False, 52),\n",
       " ('Specialization_Supply Chain Management', False, 73),\n",
       " ('Specialization_Travel and Tourism', False, 41),\n",
       " ('Specialization_Unknown', True, 1),\n",
       " ('What is your current occupation_Housewife', False, 30),\n",
       " ('What is your current occupation_Other', False, 91),\n",
       " ('What is your current occupation_Student', False, 49),\n",
       " ('What is your current occupation_Unemployed', True, 1),\n",
       " ('What is your current occupation_Working Professional', False, 4),\n",
       " ('Tags_Busy', True, 1),\n",
       " ('Tags_Closed by Horizzon', True, 1),\n",
       " ('Tags_Diploma holder (Not Eligible)', False, 9),\n",
       " ('Tags_Graduation in progress', False, 15),\n",
       " ('Tags_In confusion whether part time or DLP', False, 43),\n",
       " ('Tags_Interested  in full time MBA', False, 27),\n",
       " ('Tags_Interested in Next batch', False, 14),\n",
       " ('Tags_Interested in other courses', False, 34),\n",
       " ('Tags_Lateral student', False, 2),\n",
       " ('Tags_Lost to EINS', True, 1),\n",
       " ('Tags_Lost to Others', False, 16),\n",
       " ('Tags_Not doing further education', False, 17),\n",
       " ('Tags_Recognition issue (DEC approval)', False, 61),\n",
       " ('Tags_Ringing', True, 1),\n",
       " ('Tags_Shall take in the next coming month', False, 66),\n",
       " ('Tags_Still Thinking', False, 69),\n",
       " ('Tags_University not recognized', False, 87),\n",
       " ('Tags_Want to take admission but has financial problems', False, 48),\n",
       " ('Tags_Will revert after reading the email', True, 1),\n",
       " ('Tags_in touch with EINS', False, 13),\n",
       " ('Tags_invalid number', True, 1),\n",
       " ('Tags_number not provided', False, 7),\n",
       " ('Tags_opp hangup', False, 63),\n",
       " ('Tags_switched off', True, 1),\n",
       " ('Tags_wrong number given', False, 3),\n",
       " ('City_Other Cities', False, 68),\n",
       " ('City_Other Cities of Maharashtra', False, 80),\n",
       " ('City_Other Metro Cities', False, 79),\n",
       " ('City_Thane & Outskirts', False, 78),\n",
       " ('City_Tier II Cities', False, 44),\n",
       " ('Last Notable Activity_Email Bounced', False, 36),\n",
       " ('Last Notable Activity_Email Link Clicked', False, 19),\n",
       " ('Last Notable Activity_Email Marked Spam', False, 62),\n",
       " ('Last Notable Activity_Email Opened', False, 71),\n",
       " ('Last Notable Activity_Email Received', False, 85),\n",
       " ('Last Notable Activity_Form Submitted on Website', False, 50),\n",
       " ('Last Notable Activity_Had a Phone Conversation', False, 18),\n",
       " ('Last Notable Activity_Modified', False, 8),\n",
       " ('Last Notable Activity_Olark Chat Conversation', False, 5),\n",
       " ('Last Notable Activity_Page Visited on Website', False, 11),\n",
       " ('Last Notable Activity_Resubscribed to emails', False, 77),\n",
       " ('Last Notable Activity_SMS Sent', True, 1),\n",
       " ('Last Notable Activity_Unreachable', False, 47),\n",
       " ('Last Notable Activity_Unsubscribed', False, 86),\n",
       " ('Last Notable Activity_View in browser link Clicked', False, 84)]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns, rfe_1.support_, rfe_1.ranking_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = X_train.columns[rfe_1.support_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['TotalVisits', 'Total Time Spent on Website', 'Page Views Per Visit',\n",
       "       'Lead Origin_Lead Import', 'Lead Source_Direct Traffic',\n",
       "       'Lead Source_Facebook', 'Lead Source_Google', 'Lead Source_Live Chat',\n",
       "       'Lead Source_NC_EDM', 'Lead Source_Olark Chat',\n",
       "       'Lead Source_Organic Search', 'Lead Source_Pay per Click Ads',\n",
       "       'Lead Source_Press_Release', 'Lead Source_Reference',\n",
       "       'Lead Source_Referral Sites', 'Lead Source_Social Media',\n",
       "       'Lead Source_WeLearn', 'Lead Source_bing', 'Lead Source_blog',\n",
       "       'Lead Source_google', 'Lead Source_testone',\n",
       "       'Lead Source_welearnblog_Home', 'Lead Source_youtubechannel',\n",
       "       'Last Activity_Converted to Lead', 'Last Activity_Email Link Clicked',\n",
       "       'Last Activity_Email Marked Spam', 'Last Activity_Email Opened',\n",
       "       'Last Activity_Email Received',\n",
       "       'Last Activity_Form Submitted on Website',\n",
       "       'Last Activity_Olark Chat Conversation',\n",
       "       'Last Activity_Page Visited on Website',\n",
       "       'Last Activity_Resubscribed to emails', 'Last Activity_SMS Sent',\n",
       "       'Last Activity_Unreachable', 'Last Activity_Unsubscribed',\n",
       "       'Last Activity_View in browser link Clicked',\n",
       "       'Last Activity_Visited Booth in Tradeshow',\n",
       "       'Specialization_Business Administration', 'Specialization_E-Business',\n",
       "       'Specialization_E-COMMERCE', 'Specialization_Finance Management',\n",
       "       'Specialization_Healthcare Management',\n",
       "       'Specialization_Hospitality Management',\n",
       "       'Specialization_Human Resource Management',\n",
       "       'Specialization_IT Projects Management',\n",
       "       'Specialization_International Business',\n",
       "       'Specialization_Marketing Management',\n",
       "       'Specialization_Media and Advertising',\n",
       "       'Specialization_Operations Management',\n",
       "       'Specialization_Retail Management',\n",
       "       'Specialization_Rural and Agribusiness',\n",
       "       'Specialization_Services Excellence',\n",
       "       'Specialization_Supply Chain Management',\n",
       "       'Specialization_Travel and Tourism',\n",
       "       'What is your current occupation_Housewife',\n",
       "       'What is your current occupation_Other',\n",
       "       'What is your current occupation_Student',\n",
       "       'What is your current occupation_Working Professional',\n",
       "       'Tags_Diploma holder (Not Eligible)', 'Tags_Graduation in progress',\n",
       "       'Tags_In confusion whether part time or DLP',\n",
       "       'Tags_Interested  in full time MBA', 'Tags_Interested in Next batch',\n",
       "       'Tags_Interested in other courses', 'Tags_Lateral student',\n",
       "       'Tags_Lost to Others', 'Tags_Not doing further education',\n",
       "       'Tags_Recognition issue (DEC approval)',\n",
       "       'Tags_Shall take in the next coming month', 'Tags_Still Thinking',\n",
       "       'Tags_University not recognized',\n",
       "       'Tags_Want to take admission but has financial problems',\n",
       "       'Tags_in touch with EINS', 'Tags_number not provided',\n",
       "       'Tags_opp hangup', 'Tags_wrong number given', 'City_Other Cities',\n",
       "       'City_Other Cities of Maharashtra', 'City_Other Metro Cities',\n",
       "       'City_Thane & Outskirts', 'City_Tier II Cities',\n",
       "       'Last Notable Activity_Email Bounced',\n",
       "       'Last Notable Activity_Email Link Clicked',\n",
       "       'Last Notable Activity_Email Marked Spam',\n",
       "       'Last Notable Activity_Email Opened',\n",
       "       'Last Notable Activity_Email Received',\n",
       "       'Last Notable Activity_Form Submitted on Website',\n",
       "       'Last Notable Activity_Had a Phone Conversation',\n",
       "       'Last Notable Activity_Modified',\n",
       "       'Last Notable Activity_Olark Chat Conversation',\n",
       "       'Last Notable Activity_Page Visited on Website',\n",
       "       'Last Notable Activity_Resubscribed to emails',\n",
       "       'Last Notable Activity_Unreachable',\n",
       "       'Last Notable Activity_Unsubscribed',\n",
       "       'Last Notable Activity_View in browser link Clicked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns[~rfe_1.support_]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assessing the model with StatsModels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Converted</td>    <th>  No. Observations:  </th>  <td>  6351</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  6335</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>    15</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>         <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -1984.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Mon, 20 Apr 2020</td> <th>  Deviance:          </th> <td>  3969.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>16:24:14</td>     <th>  Pearson chi2:      </th> <td>1.16e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>         <td>23</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                       <td></td>                         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                      <td>   -0.5824</td> <td>    0.245</td> <td>   -2.379</td> <td> 0.017</td> <td>   -1.062</td> <td>   -0.103</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Landing Page Submission</th>        <td>   -1.4546</td> <td>    0.147</td> <td>   -9.862</td> <td> 0.000</td> <td>   -1.744</td> <td>   -1.166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Lead Add Form</th>                  <td>    1.6761</td> <td>    0.317</td> <td>    5.294</td> <td> 0.000</td> <td>    1.056</td> <td>    2.297</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Welingak Website</th>               <td>    2.5187</td> <td>    0.804</td> <td>    3.133</td> <td> 0.002</td> <td>    0.943</td> <td>    4.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Bounced</th>                <td>   -2.0760</td> <td>    0.389</td> <td>   -5.338</td> <td> 0.000</td> <td>   -2.838</td> <td>   -1.314</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Had a Phone Conversation</th>     <td>    2.7778</td> <td>    0.972</td> <td>    2.857</td> <td> 0.004</td> <td>    0.872</td> <td>    4.683</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Unknown</th>                     <td>   -2.1169</td> <td>    0.150</td> <td>  -14.093</td> <td> 0.000</td> <td>   -2.411</td> <td>   -1.822</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What is your current occupation_Unemployed</th> <td>   -2.3111</td> <td>    0.187</td> <td>  -12.342</td> <td> 0.000</td> <td>   -2.678</td> <td>   -1.944</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Busy</th>                                  <td>    3.0114</td> <td>    0.307</td> <td>    9.800</td> <td> 0.000</td> <td>    2.409</td> <td>    3.614</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Closed by Horizzon</th>                    <td>    8.4197</td> <td>    0.743</td> <td>   11.335</td> <td> 0.000</td> <td>    6.964</td> <td>    9.876</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Lost to EINS</th>                          <td>    8.3874</td> <td>    0.746</td> <td>   11.236</td> <td> 0.000</td> <td>    6.924</td> <td>    9.850</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Ringing</th>                               <td>   -1.1915</td> <td>    0.317</td> <td>   -3.760</td> <td> 0.000</td> <td>   -1.812</td> <td>   -0.570</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Will revert after reading the email</th>   <td>    3.7549</td> <td>    0.205</td> <td>   18.352</td> <td> 0.000</td> <td>    3.354</td> <td>    4.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_invalid number</th>                        <td>  -21.6538</td> <td> 1.47e+04</td> <td>   -0.001</td> <td> 0.999</td> <td>-2.88e+04</td> <td> 2.88e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_switched off</th>                          <td>   -1.2049</td> <td>    0.560</td> <td>   -2.153</td> <td> 0.031</td> <td>   -2.302</td> <td>   -0.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_SMS Sent</th>             <td>    2.8209</td> <td>    0.114</td> <td>   24.766</td> <td> 0.000</td> <td>    2.598</td> <td>    3.044</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:              Converted   No. Observations:                 6351\n",
       "Model:                            GLM   Df Residuals:                     6335\n",
       "Model Family:                Binomial   Df Model:                           15\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -1984.7\n",
       "Date:                Mon, 20 Apr 2020   Deviance:                       3969.3\n",
       "Time:                        16:24:14   Pearson chi2:                 1.16e+04\n",
       "No. Iterations:                    23                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================================================\n",
       "                                                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------------------------\n",
       "const                                         -0.5824      0.245     -2.379      0.017      -1.062      -0.103\n",
       "Lead Origin_Landing Page Submission           -1.4546      0.147     -9.862      0.000      -1.744      -1.166\n",
       "Lead Origin_Lead Add Form                      1.6761      0.317      5.294      0.000       1.056       2.297\n",
       "Lead Source_Welingak Website                   2.5187      0.804      3.133      0.002       0.943       4.094\n",
       "Last Activity_Email Bounced                   -2.0760      0.389     -5.338      0.000      -2.838      -1.314\n",
       "Last Activity_Had a Phone Conversation         2.7778      0.972      2.857      0.004       0.872       4.683\n",
       "Specialization_Unknown                        -2.1169      0.150    -14.093      0.000      -2.411      -1.822\n",
       "What is your current occupation_Unemployed    -2.3111      0.187    -12.342      0.000      -2.678      -1.944\n",
       "Tags_Busy                                      3.0114      0.307      9.800      0.000       2.409       3.614\n",
       "Tags_Closed by Horizzon                        8.4197      0.743     11.335      0.000       6.964       9.876\n",
       "Tags_Lost to EINS                              8.3874      0.746     11.236      0.000       6.924       9.850\n",
       "Tags_Ringing                                  -1.1915      0.317     -3.760      0.000      -1.812      -0.570\n",
       "Tags_Will revert after reading the email       3.7549      0.205     18.352      0.000       3.354       4.156\n",
       "Tags_invalid number                          -21.6538   1.47e+04     -0.001      0.999   -2.88e+04    2.88e+04\n",
       "Tags_switched off                             -1.2049      0.560     -2.153      0.031      -2.302      -0.108\n",
       "Last Notable Activity_SMS Sent                 2.8209      0.114     24.766      0.000       2.598       3.044\n",
       "==============================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_const = sm.add_constant(X_train[col])\n",
    "log2 = sm.GLM(y_train,X_train_const, family = sm.families.Binomial())\n",
    "res_mod = log2.fit()\n",
    "res_mod.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "col1 = col.drop('Tags_invalid number',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Lead Origin_Landing Page Submission', 'Lead Origin_Lead Add Form',\n",
       "       'Lead Source_Welingak Website', 'Last Activity_Email Bounced',\n",
       "       'Last Activity_Had a Phone Conversation', 'Specialization_Unknown',\n",
       "       'What is your current occupation_Unemployed', 'Tags_Busy',\n",
       "       'Tags_Closed by Horizzon', 'Tags_Lost to EINS', 'Tags_Ringing',\n",
       "       'Tags_Will revert after reading the email', 'Tags_switched off',\n",
       "       'Last Notable Activity_SMS Sent'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " col1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Converted</td>    <th>  No. Observations:  </th>  <td>  6351</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  6335</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>    15</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>         <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -1984.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Mon, 20 Apr 2020</td> <th>  Deviance:          </th> <td>  3969.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>16:24:14</td>     <th>  Pearson chi2:      </th> <td>1.16e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>         <td>23</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                       <td></td>                         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                      <td>   -0.5824</td> <td>    0.245</td> <td>   -2.379</td> <td> 0.017</td> <td>   -1.062</td> <td>   -0.103</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Landing Page Submission</th>        <td>   -1.4546</td> <td>    0.147</td> <td>   -9.862</td> <td> 0.000</td> <td>   -1.744</td> <td>   -1.166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Lead Add Form</th>                  <td>    1.6761</td> <td>    0.317</td> <td>    5.294</td> <td> 0.000</td> <td>    1.056</td> <td>    2.297</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Welingak Website</th>               <td>    2.5187</td> <td>    0.804</td> <td>    3.133</td> <td> 0.002</td> <td>    0.943</td> <td>    4.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Bounced</th>                <td>   -2.0760</td> <td>    0.389</td> <td>   -5.338</td> <td> 0.000</td> <td>   -2.838</td> <td>   -1.314</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Had a Phone Conversation</th>     <td>    2.7778</td> <td>    0.972</td> <td>    2.857</td> <td> 0.004</td> <td>    0.872</td> <td>    4.683</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Unknown</th>                     <td>   -2.1169</td> <td>    0.150</td> <td>  -14.093</td> <td> 0.000</td> <td>   -2.411</td> <td>   -1.822</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What is your current occupation_Unemployed</th> <td>   -2.3111</td> <td>    0.187</td> <td>  -12.342</td> <td> 0.000</td> <td>   -2.678</td> <td>   -1.944</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Busy</th>                                  <td>    3.0114</td> <td>    0.307</td> <td>    9.800</td> <td> 0.000</td> <td>    2.409</td> <td>    3.614</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Closed by Horizzon</th>                    <td>    8.4197</td> <td>    0.743</td> <td>   11.335</td> <td> 0.000</td> <td>    6.964</td> <td>    9.876</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Lost to EINS</th>                          <td>    8.3874</td> <td>    0.746</td> <td>   11.236</td> <td> 0.000</td> <td>    6.924</td> <td>    9.850</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Ringing</th>                               <td>   -1.1915</td> <td>    0.317</td> <td>   -3.760</td> <td> 0.000</td> <td>   -1.812</td> <td>   -0.570</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Will revert after reading the email</th>   <td>    3.7549</td> <td>    0.205</td> <td>   18.352</td> <td> 0.000</td> <td>    3.354</td> <td>    4.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_invalid number</th>                        <td>  -21.6538</td> <td> 1.47e+04</td> <td>   -0.001</td> <td> 0.999</td> <td>-2.88e+04</td> <td> 2.88e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_switched off</th>                          <td>   -1.2049</td> <td>    0.560</td> <td>   -2.153</td> <td> 0.031</td> <td>   -2.302</td> <td>   -0.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_SMS Sent</th>             <td>    2.8209</td> <td>    0.114</td> <td>   24.766</td> <td> 0.000</td> <td>    2.598</td> <td>    3.044</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:              Converted   No. Observations:                 6351\n",
       "Model:                            GLM   Df Residuals:                     6335\n",
       "Model Family:                Binomial   Df Model:                           15\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -1984.7\n",
       "Date:                Mon, 20 Apr 2020   Deviance:                       3969.3\n",
       "Time:                        16:24:14   Pearson chi2:                 1.16e+04\n",
       "No. Iterations:                    23                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================================================\n",
       "                                                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------------------------\n",
       "const                                         -0.5824      0.245     -2.379      0.017      -1.062      -0.103\n",
       "Lead Origin_Landing Page Submission           -1.4546      0.147     -9.862      0.000      -1.744      -1.166\n",
       "Lead Origin_Lead Add Form                      1.6761      0.317      5.294      0.000       1.056       2.297\n",
       "Lead Source_Welingak Website                   2.5187      0.804      3.133      0.002       0.943       4.094\n",
       "Last Activity_Email Bounced                   -2.0760      0.389     -5.338      0.000      -2.838      -1.314\n",
       "Last Activity_Had a Phone Conversation         2.7778      0.972      2.857      0.004       0.872       4.683\n",
       "Specialization_Unknown                        -2.1169      0.150    -14.093      0.000      -2.411      -1.822\n",
       "What is your current occupation_Unemployed    -2.3111      0.187    -12.342      0.000      -2.678      -1.944\n",
       "Tags_Busy                                      3.0114      0.307      9.800      0.000       2.409       3.614\n",
       "Tags_Closed by Horizzon                        8.4197      0.743     11.335      0.000       6.964       9.876\n",
       "Tags_Lost to EINS                              8.3874      0.746     11.236      0.000       6.924       9.850\n",
       "Tags_Ringing                                  -1.1915      0.317     -3.760      0.000      -1.812      -0.570\n",
       "Tags_Will revert after reading the email       3.7549      0.205     18.352      0.000       3.354       4.156\n",
       "Tags_invalid number                          -21.6538   1.47e+04     -0.001      0.999   -2.88e+04    2.88e+04\n",
       "Tags_switched off                             -1.2049      0.560     -2.153      0.031      -2.302      -0.108\n",
       "Last Notable Activity_SMS Sent                 2.8209      0.114     24.766      0.000       2.598       3.044\n",
       "==============================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_const = sm.add_constant(X_train[col1])\n",
    "log3 = sm.GLM(y_train,X_train_const, family = sm.families.Binomial())\n",
    "res_mod1 = log2.fit()\n",
    "res_mod1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "col2 = col1.drop('Tags_switched off',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Converted</td>    <th>  No. Observations:  </th>  <td>  6351</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  6337</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>    13</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>         <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -1990.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Mon, 20 Apr 2020</td> <th>  Deviance:          </th> <td>  3981.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>16:24:15</td>     <th>  Pearson chi2:      </th> <td>1.09e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>          <td>8</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                       <td></td>                         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                      <td>   -0.7700</td> <td>    0.249</td> <td>   -3.089</td> <td> 0.002</td> <td>   -1.259</td> <td>   -0.282</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Landing Page Submission</th>        <td>   -1.4894</td> <td>    0.149</td> <td>  -10.012</td> <td> 0.000</td> <td>   -1.781</td> <td>   -1.198</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Lead Add Form</th>                  <td>    1.7037</td> <td>    0.319</td> <td>    5.334</td> <td> 0.000</td> <td>    1.078</td> <td>    2.330</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Welingak Website</th>               <td>    2.4855</td> <td>    0.805</td> <td>    3.088</td> <td> 0.002</td> <td>    0.908</td> <td>    4.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Bounced</th>                <td>   -2.0792</td> <td>    0.390</td> <td>   -5.331</td> <td> 0.000</td> <td>   -2.844</td> <td>   -1.315</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Had a Phone Conversation</th>     <td>    2.7710</td> <td>    0.970</td> <td>    2.857</td> <td> 0.004</td> <td>    0.870</td> <td>    4.672</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Unknown</th>                     <td>   -2.1375</td> <td>    0.152</td> <td>  -14.100</td> <td> 0.000</td> <td>   -2.435</td> <td>   -1.840</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What is your current occupation_Unemployed</th> <td>   -2.4096</td> <td>    0.192</td> <td>  -12.574</td> <td> 0.000</td> <td>   -2.785</td> <td>   -2.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Busy</th>                                  <td>    3.3570</td> <td>    0.293</td> <td>   11.471</td> <td> 0.000</td> <td>    2.783</td> <td>    3.931</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Closed by Horizzon</th>                    <td>    8.7308</td> <td>    0.739</td> <td>   11.807</td> <td> 0.000</td> <td>    7.282</td> <td>   10.180</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Lost to EINS</th>                          <td>    8.7022</td> <td>    0.743</td> <td>   11.712</td> <td> 0.000</td> <td>    7.246</td> <td>   10.158</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Ringing</th>                               <td>   -0.8313</td> <td>    0.301</td> <td>   -2.761</td> <td> 0.006</td> <td>   -1.421</td> <td>   -0.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Will revert after reading the email</th>   <td>    4.0717</td> <td>    0.191</td> <td>   21.358</td> <td> 0.000</td> <td>    3.698</td> <td>    4.445</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_SMS Sent</th>             <td>    2.7642</td> <td>    0.111</td> <td>   24.968</td> <td> 0.000</td> <td>    2.547</td> <td>    2.981</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:              Converted   No. Observations:                 6351\n",
       "Model:                            GLM   Df Residuals:                     6337\n",
       "Model Family:                Binomial   Df Model:                           13\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -1990.5\n",
       "Date:                Mon, 20 Apr 2020   Deviance:                       3981.1\n",
       "Time:                        16:24:15   Pearson chi2:                 1.09e+04\n",
       "No. Iterations:                     8                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================================================\n",
       "                                                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------------------------\n",
       "const                                         -0.7700      0.249     -3.089      0.002      -1.259      -0.282\n",
       "Lead Origin_Landing Page Submission           -1.4894      0.149    -10.012      0.000      -1.781      -1.198\n",
       "Lead Origin_Lead Add Form                      1.7037      0.319      5.334      0.000       1.078       2.330\n",
       "Lead Source_Welingak Website                   2.4855      0.805      3.088      0.002       0.908       4.063\n",
       "Last Activity_Email Bounced                   -2.0792      0.390     -5.331      0.000      -2.844      -1.315\n",
       "Last Activity_Had a Phone Conversation         2.7710      0.970      2.857      0.004       0.870       4.672\n",
       "Specialization_Unknown                        -2.1375      0.152    -14.100      0.000      -2.435      -1.840\n",
       "What is your current occupation_Unemployed    -2.4096      0.192    -12.574      0.000      -2.785      -2.034\n",
       "Tags_Busy                                      3.3570      0.293     11.471      0.000       2.783       3.931\n",
       "Tags_Closed by Horizzon                        8.7308      0.739     11.807      0.000       7.282      10.180\n",
       "Tags_Lost to EINS                              8.7022      0.743     11.712      0.000       7.246      10.158\n",
       "Tags_Ringing                                  -0.8313      0.301     -2.761      0.006      -1.421      -0.241\n",
       "Tags_Will revert after reading the email       4.0717      0.191     21.358      0.000       3.698       4.445\n",
       "Last Notable Activity_SMS Sent                 2.7642      0.111     24.968      0.000       2.547       2.981\n",
       "==============================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_const = sm.add_constant(X_train[col2])\n",
    "log4 = sm.GLM(y_train,X_train_const, family = sm.families.Binomial())\n",
    "res_mod2 = log4.fit()\n",
    "res_mod2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Conclusion :\n",
    "    \n",
    "    So the p value is low for all the feilds . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3009    0.354966\n",
       "1012    0.082831\n",
       "9226    0.002132\n",
       "4750    0.897234\n",
       "7987    0.982591\n",
       "          ...   \n",
       "367     0.223496\n",
       "82      0.964487\n",
       "8199    0.157027\n",
       "7077    0.094541\n",
       "5754    0.009295\n",
       "Length: 6351, dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the predicted values on the train set\n",
    "pred = res_mod2.predict(X_train_const)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.35496551, 0.08283102, 0.00213239, ..., 0.15702709, 0.09454058,\n",
       "       0.00929462])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = pred.values.reshape(-1)\n",
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Coverted flag and predicted probabilities together "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Converted</th>\n",
       "      <th>Converted_prob</th>\n",
       "      <th>Prospect ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.354966</td>\n",
       "      <td>3009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.082831</td>\n",
       "      <td>1012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002132</td>\n",
       "      <td>9226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.897234</td>\n",
       "      <td>4750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.982591</td>\n",
       "      <td>7987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Converted_prob  Prospect ID\n",
       "0          0        0.354966         3009\n",
       "1          0        0.082831         1012\n",
       "2          0        0.002132         9226\n",
       "3          1        0.897234         4750\n",
       "4          1        0.982591         7987"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_final = pd.DataFrame({'Converted':y_train.values, 'Converted_prob':pred})\n",
    "pred_final['Prospect ID'] = y_train.index\n",
    "pred_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Creating new column 'predicted' with 1 if Churn_Prob > 0.5 else 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Converted</th>\n",
       "      <th>Converted_prob</th>\n",
       "      <th>Prospect ID</th>\n",
       "      <th>predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.354966</td>\n",
       "      <td>3009</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.082831</td>\n",
       "      <td>1012</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002132</td>\n",
       "      <td>9226</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.897234</td>\n",
       "      <td>4750</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.982591</td>\n",
       "      <td>7987</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Converted_prob  Prospect ID  predicted\n",
       "0          0        0.354966         3009          0\n",
       "1          0        0.082831         1012          0\n",
       "2          0        0.002132         9226          0\n",
       "3          1        0.897234         4750          1\n",
       "4          1        0.982591         7987          1"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_final['predicted'] = pred_final.Converted_prob.map(lambda x: 1 if x > 0.5 else 0)\n",
    "\n",
    "# Let's see the head\n",
    "pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3739  166]\n",
      " [ 640 1806]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "# Confusion matrix \n",
    "confusion_mat = metrics.confusion_matrix(pred_final.Converted, pred_final.predicted )\n",
    "print(confusion_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8730908518343568\n"
     ]
    }
   ],
   "source": [
    "#overall accuracy.\n",
    "print(metrics.accuracy_score(pred_final.Converted, pred_final.predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check for the VIF values of the feature variables. \n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Lead Source_Welingak Website</td>\n",
       "      <td>1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Tags_Closed by Horizzon</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Tags_Busy</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Tags_Lost to EINS</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Last Activity_Email Bounced</td>\n",
       "      <td>1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Last Notable Activity_SMS Sent</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Last Activity_Had a Phone Conversation</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Lead Origin_Lead Add Form</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Tags_Will revert after reading the email</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Tags_Ringing</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Lead Origin_Landing Page Submission</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>What is your current occupation_Unemployed</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Specialization_Unknown</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Dataset Features   VIF\n",
       "2                 Lead Source_Welingak Website  1.34\n",
       "8                      Tags_Closed by Horizzon  1.25\n",
       "7                                    Tags_Busy  1.14\n",
       "9                            Tags_Lost to EINS  1.09\n",
       "3                  Last Activity_Email Bounced  1.07\n",
       "12              Last Notable Activity_SMS Sent  1.05\n",
       "4       Last Activity_Had a Phone Conversation  1.01\n",
       "1                    Lead Origin_Lead Add Form  0.69\n",
       "11    Tags_Will revert after reading the email  0.17\n",
       "10                                Tags_Ringing  0.12\n",
       "0          Lead Origin_Landing Page Submission  0.10\n",
       "6   What is your current occupation_Unemployed  0.09\n",
       "5                       Specialization_Unknown  0.00"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe that will contain the names of all the feature variables and their respective VIFs\n",
    "vif_df = pd.DataFrame()\n",
    "vif_df['Dataset Features'] = X_train[col2].columns\n",
    "vif_df['VIF'] = [variance_inflation_factor(X_train[col].values, i) for i in range(X_train[col2].shape[1])]\n",
    "vif_df['VIF'] = round(vif_df['VIF'], 2)\n",
    "vif_df = vif_df.sort_values(by = \"VIF\", ascending = False)\n",
    "vif_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Seems there is no issue with VIF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "True_Positive = confusion_mat[1,1] # true positive \n",
    "True_Negative = confusion_mat[0,0] # true negatives\n",
    "False_Positive = confusion_mat[0,1] # false positives\n",
    "False_Negative = confusion_mat[1,0] # false negatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7383483237939493"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  sensitivity of the model\n",
    "True_Positive / float(True_Positive+False_Negative)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9574903969270167"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# specificity\n",
    "True_Negative / float(True_Negative+False_Positive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.042509603072983355\n"
     ]
    }
   ],
   "source": [
    "# false postive rate\n",
    "print(False_Positive/ float(True_Negative+False_Positive))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9158215010141988\n"
     ]
    }
   ],
   "source": [
    "# positive predictive value \n",
    "print (True_Positive / float(True_Positive+False_Positive))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8538479104818452\n"
     ]
    }
   ],
   "source": [
    "# Negative predictive value\n",
    "print (True_Negative / float(True_Negative+ False_Negative))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the ROC Curve\n",
    "\n",
    "An ROC curve demonstrates several things:\n",
    "It shows the tradeoff between sensitivity and specificity (any increase in sensitivity will be accompanied by a decrease in specificity).\n",
    "The closer the curve follows the left-hand border and then the top border of the ROC space, the more accurate the test.\n",
    "The closer the curve comes to the 45-degree diagonal of the ROC space, the less accurate the test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_roc( actual, probs ):\n",
    "    fpr, tpr, thresholds = metrics.roc_curve( actual, probs,\n",
    "                                              drop_intermediate = False )\n",
    "    auc_score = metrics.roc_auc_score( actual, probs )\n",
    "    plt.figure(figsize=(5, 5))\n",
    "    plt.plot( fpr, tpr, label='ROC curve (area = %0.2f)' % auc_score )\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate or [1 - True Negative Rate]')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic example')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = metrics.roc_curve( pred_final.Converted, pred_final.Converted_prob, drop_intermediate = False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_roc(pred_final.Converted, pred_final.Converted_prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion :\n",
    "    \n",
    "    The model is accurate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding Optimal Cutoff Point\n",
    "\n",
    "Optimal cutoff probability is that prob where we get balanced sensitivity and specificity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Converted</th>\n",
       "      <th>Converted_prob</th>\n",
       "      <th>Prospect ID</th>\n",
       "      <th>predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.2</th>\n",
       "      <th>0.3</th>\n",
       "      <th>0.4</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.6</th>\n",
       "      <th>0.7</th>\n",
       "      <th>0.8</th>\n",
       "      <th>0.9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.354966</td>\n",
       "      <td>3009</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.082831</td>\n",
       "      <td>1012</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002132</td>\n",
       "      <td>9226</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.897234</td>\n",
       "      <td>4750</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.982591</td>\n",
       "      <td>7987</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Converted_prob  Prospect ID  predicted  0.0  0.1  0.2  0.3  0.4  \\\n",
       "0          0        0.354966         3009          0    1    1    1    1    0   \n",
       "1          0        0.082831         1012          0    1    0    0    0    0   \n",
       "2          0        0.002132         9226          0    1    0    0    0    0   \n",
       "3          1        0.897234         4750          1    1    1    1    1    1   \n",
       "4          1        0.982591         7987          1    1    1    1    1    1   \n",
       "\n",
       "   0.5  0.6  0.7  0.8  0.9  \n",
       "0    0    0    0    0    0  \n",
       "1    0    0    0    0    0  \n",
       "2    0    0    0    0    0  \n",
       "3    1    1    1    1    0  \n",
       "4    1    1    1    1    1  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no = [float(x)/10 for x in range(10)]\n",
    "for i in no:\n",
    "    pred_final[i]= pred_final.Converted_prob.map(lambda x: 1 if x > i else 0)\n",
    "pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     probability  accuracy  sensitivity  specificity\n",
      "0.0          0.0  0.385136     1.000000     0.000000\n",
      "0.1          0.1  0.706503     0.977105     0.537004\n",
      "0.2          0.2  0.722406     0.974652     0.564405\n",
      "0.3          0.3  0.825382     0.883892     0.788732\n",
      "0.4          0.4  0.871989     0.738348     0.955698\n",
      "0.5          0.5  0.873091     0.738348     0.957490\n",
      "0.6          0.6  0.873563     0.736713     0.959283\n",
      "0.7          0.7  0.871989     0.730989     0.960307\n",
      "0.8          0.8  0.860967     0.684383     0.971575\n",
      "0.9          0.9  0.758306     0.376942     0.997183\n"
     ]
    }
   ],
   "source": [
    "# To calculate accuracy sensitivity and specificity for various probability cutoffs.\n",
    "cutoff_dataframe = pd.DataFrame( columns = ['probability','accuracy','sensitivity','specificity'])\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# TP = confusion[1,1] # true positive \n",
    "# TN = confusion[0,0] # true negatives\n",
    "# FP = confusion[0,1] # false positives\n",
    "# FN = confusion[1,0] # false negatives\n",
    "\n",
    "num = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]\n",
    "for i in num:\n",
    "    cm1 = metrics.confusion_matrix(pred_final.Converted, pred_final[i] )\n",
    "    total1=sum(sum(cm1))\n",
    "    accuracy = (cm1[0,0]+cm1[1,1])/total1\n",
    "    \n",
    "    specificity = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "    sensitivity = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "    cutoff_dataframe.loc[i] =[ i ,accuracy,sensitivity,specificity]\n",
    "print(cutoff_dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cutoff_dataframe.plot.line(x='probability', y=['accuracy','sensitivity','specificity'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion :\n",
    "    \n",
    "    From above 0.4 is the optimum cut off point for this model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Converted</th>\n",
       "      <th>Converted_prob</th>\n",
       "      <th>Prospect ID</th>\n",
       "      <th>predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.2</th>\n",
       "      <th>0.3</th>\n",
       "      <th>0.4</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.6</th>\n",
       "      <th>0.7</th>\n",
       "      <th>0.8</th>\n",
       "      <th>0.9</th>\n",
       "      <th>final_predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.354966</td>\n",
       "      <td>3009</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.082831</td>\n",
       "      <td>1012</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002132</td>\n",
       "      <td>9226</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.897234</td>\n",
       "      <td>4750</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.982591</td>\n",
       "      <td>7987</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Converted_prob  Prospect ID  predicted  0.0  0.1  0.2  0.3  0.4  \\\n",
       "0          0        0.354966         3009          0    1    1    1    1    0   \n",
       "1          0        0.082831         1012          0    1    0    0    0    0   \n",
       "2          0        0.002132         9226          0    1    0    0    0    0   \n",
       "3          1        0.897234         4750          1    1    1    1    1    1   \n",
       "4          1        0.982591         7987          1    1    1    1    1    1   \n",
       "\n",
       "   0.5  0.6  0.7  0.8  0.9  final_predicted  \n",
       "0    0    0    0    0    0                0  \n",
       "1    0    0    0    0    0                0  \n",
       "2    0    0    0    0    0                0  \n",
       "3    1    1    1    1    0                1  \n",
       "4    1    1    1    1    1                1  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_final['final_predicted'] = pred_final.Converted_prob.map( lambda x: 1 if x > 0.4 else 0)\n",
    "\n",
    "pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Converted</th>\n",
       "      <th>Converted_prob</th>\n",
       "      <th>Prospect ID</th>\n",
       "      <th>predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.2</th>\n",
       "      <th>0.3</th>\n",
       "      <th>0.4</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.6</th>\n",
       "      <th>0.7</th>\n",
       "      <th>0.8</th>\n",
       "      <th>0.9</th>\n",
       "      <th>final_predicted</th>\n",
       "      <th>Lead_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.354966</td>\n",
       "      <td>3009</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.082831</td>\n",
       "      <td>1012</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002132</td>\n",
       "      <td>9226</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.897234</td>\n",
       "      <td>4750</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.982591</td>\n",
       "      <td>7987</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Converted_prob  Prospect ID  predicted  0.0  0.1  0.2  0.3  0.4  \\\n",
       "0          0        0.354966         3009          0    1    1    1    1    0   \n",
       "1          0        0.082831         1012          0    1    0    0    0    0   \n",
       "2          0        0.002132         9226          0    1    0    0    0    0   \n",
       "3          1        0.897234         4750          1    1    1    1    1    1   \n",
       "4          1        0.982591         7987          1    1    1    1    1    1   \n",
       "\n",
       "   0.5  0.6  0.7  0.8  0.9  final_predicted  Lead_Score  \n",
       "0    0    0    0    0    0                0          35  \n",
       "1    0    0    0    0    0                0           8  \n",
       "2    0    0    0    0    0                0           0  \n",
       "3    1    1    1    1    0                1          90  \n",
       "4    1    1    1    1    1                1          98  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Assinging lead score\n",
    "\n",
    "pred_final['Lead_Score'] = pred_final.Converted_prob.map( lambda x: round(x*100))\n",
    "pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8719886632026452"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# overall accuracy.\n",
    "metrics.accuracy_score(pred_final.Converted, pred_final.final_predicted)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3732,  173],\n",
       "       [ 640, 1806]], dtype=int64)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "confusion_mat1 = metrics.confusion_matrix(pred_final.Converted, pred_final.final_predicted )\n",
    "confusion_mat1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "True_Positive = confusion_mat1[1,1] # true positive \n",
    "True_Negative = confusion_mat1[0,0] # true negatives\n",
    "False_Positive = confusion_mat1[0,1] # false positives\n",
    "False_Negative = confusion_mat1[1,0] # false negatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7383483237939493"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  sensitivity of the model\n",
    "True_Positive / float(True_Positive+False_Negative)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9556978233034571"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# specificity\n",
    "True_Negative / float(True_Negative+False_Positive)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04430217669654289\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# false postive rate\n",
    "print(False_Positive/ float(True_Negative+False_Positive))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9125821121778676\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# positive predictive value \n",
    "print (True_Positive / float(True_Positive+False_Positive))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8536139066788655\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Negative predictive value\n",
    "print (True_Negative / float(True_Negative+ False_Negative))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Precision and Recall "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9158215010141988"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Precision\n",
    "True_Positive / True_Positive + False_Positive\n",
    "\n",
    "confusion_mat[1,1]/(confusion_mat[0,1]+confusion_mat[1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7383483237939493"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recall\n",
    "True_Positive / True_Positive + False_Negative\n",
    "\n",
    "confusion_mat[1,1]/(confusion_mat[1,0]+confusion_mat[1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0       0\n",
       " 1       0\n",
       " 2       0\n",
       " 3       1\n",
       " 4       1\n",
       "        ..\n",
       " 6346    0\n",
       " 6347    1\n",
       " 6348    0\n",
       " 6349    0\n",
       " 6350    0\n",
       " Name: Converted, Length: 6351, dtype: int64, 0       0\n",
       " 1       0\n",
       " 2       0\n",
       " 3       1\n",
       " 4       1\n",
       "        ..\n",
       " 6346    0\n",
       " 6347    1\n",
       " 6348    0\n",
       " 6349    0\n",
       " 6350    0\n",
       " Name: predicted, Length: 6351, dtype: int64)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Precision and recall tradeoff\n",
    "\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "pred_final.Converted, pred_final.predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "p, r, thresholds = precision_recall_curve(pred_final.Converted,pred_final.Converted_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(thresholds, p[:-1], \"g-\")\n",
    "plt.plot(thresholds, r[:-1], \"r-\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Testing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TotalVisits</th>\n",
       "      <th>Total Time Spent on Website</th>\n",
       "      <th>Page Views Per Visit</th>\n",
       "      <th>Lead Origin_Landing Page Submission</th>\n",
       "      <th>Lead Origin_Lead Add Form</th>\n",
       "      <th>Lead Origin_Lead Import</th>\n",
       "      <th>Lead Source_Direct Traffic</th>\n",
       "      <th>Lead Source_Facebook</th>\n",
       "      <th>Lead Source_Google</th>\n",
       "      <th>Lead Source_Live Chat</th>\n",
       "      <th>...</th>\n",
       "      <th>Last Notable Activity_Form Submitted on Website</th>\n",
       "      <th>Last Notable Activity_Had a Phone Conversation</th>\n",
       "      <th>Last Notable Activity_Modified</th>\n",
       "      <th>Last Notable Activity_Olark Chat Conversation</th>\n",
       "      <th>Last Notable Activity_Page Visited on Website</th>\n",
       "      <th>Last Notable Activity_Resubscribed to emails</th>\n",
       "      <th>Last Notable Activity_SMS Sent</th>\n",
       "      <th>Last Notable Activity_Unreachable</th>\n",
       "      <th>Last Notable Activity_Unsubscribed</th>\n",
       "      <th>Last Notable Activity_View in browser link Clicked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3009</td>\n",
       "      <td>-0.284595</td>\n",
       "      <td>-0.160255</td>\n",
       "      <td>-0.155018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1012</td>\n",
       "      <td>-0.284595</td>\n",
       "      <td>-0.540048</td>\n",
       "      <td>-0.155018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9226</td>\n",
       "      <td>-0.671384</td>\n",
       "      <td>-0.888650</td>\n",
       "      <td>-1.265540</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4750</td>\n",
       "      <td>-0.284595</td>\n",
       "      <td>1.643304</td>\n",
       "      <td>-0.155018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7987</td>\n",
       "      <td>0.295588</td>\n",
       "      <td>2.017593</td>\n",
       "      <td>0.122613</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 110 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      TotalVisits  Total Time Spent on Website  Page Views Per Visit  \\\n",
       "3009    -0.284595                    -0.160255             -0.155018   \n",
       "1012    -0.284595                    -0.540048             -0.155018   \n",
       "9226    -0.671384                    -0.888650             -1.265540   \n",
       "4750    -0.284595                     1.643304             -0.155018   \n",
       "7987     0.295588                     2.017593              0.122613   \n",
       "\n",
       "      Lead Origin_Landing Page Submission  Lead Origin_Lead Add Form  \\\n",
       "3009                                    1                          0   \n",
       "1012                                    1                          0   \n",
       "9226                                    0                          0   \n",
       "4750                                    1                          0   \n",
       "7987                                    1                          0   \n",
       "\n",
       "      Lead Origin_Lead Import  Lead Source_Direct Traffic  \\\n",
       "3009                        0                           1   \n",
       "1012                        0                           1   \n",
       "9226                        0                           0   \n",
       "4750                        0                           1   \n",
       "7987                        0                           1   \n",
       "\n",
       "      Lead Source_Facebook  Lead Source_Google  Lead Source_Live Chat  ...  \\\n",
       "3009                     0                   0                      0  ...   \n",
       "1012                     0                   0                      0  ...   \n",
       "9226                     0                   0                      0  ...   \n",
       "4750                     0                   0                      0  ...   \n",
       "7987                     0                   0                      0  ...   \n",
       "\n",
       "      Last Notable Activity_Form Submitted on Website  \\\n",
       "3009                                                0   \n",
       "1012                                                0   \n",
       "9226                                                0   \n",
       "4750                                                0   \n",
       "7987                                                0   \n",
       "\n",
       "      Last Notable Activity_Had a Phone Conversation  \\\n",
       "3009                                               0   \n",
       "1012                                               0   \n",
       "9226                                               0   \n",
       "4750                                               0   \n",
       "7987                                               0   \n",
       "\n",
       "      Last Notable Activity_Modified  \\\n",
       "3009                               0   \n",
       "1012                               0   \n",
       "9226                               1   \n",
       "4750                               0   \n",
       "7987                               1   \n",
       "\n",
       "      Last Notable Activity_Olark Chat Conversation  \\\n",
       "3009                                              0   \n",
       "1012                                              0   \n",
       "9226                                              0   \n",
       "4750                                              0   \n",
       "7987                                              0   \n",
       "\n",
       "      Last Notable Activity_Page Visited on Website  \\\n",
       "3009                                              0   \n",
       "1012                                              0   \n",
       "9226                                              0   \n",
       "4750                                              0   \n",
       "7987                                              0   \n",
       "\n",
       "      Last Notable Activity_Resubscribed to emails  \\\n",
       "3009                                             0   \n",
       "1012                                             0   \n",
       "9226                                             0   \n",
       "4750                                             0   \n",
       "7987                                             0   \n",
       "\n",
       "      Last Notable Activity_SMS Sent  Last Notable Activity_Unreachable  \\\n",
       "3009                               0                                  0   \n",
       "1012                               0                                  0   \n",
       "9226                               0                                  0   \n",
       "4750                               1                                  0   \n",
       "7987                               0                                  0   \n",
       "\n",
       "      Last Notable Activity_Unsubscribed  \\\n",
       "3009                                   0   \n",
       "1012                                   0   \n",
       "9226                                   0   \n",
       "4750                                   0   \n",
       "7987                                   0   \n",
       "\n",
       "      Last Notable Activity_View in browser link Clicked  \n",
       "3009                                                  0   \n",
       "1012                                                  0   \n",
       "9226                                                  0   \n",
       "4750                                                  0   \n",
       "7987                                                  0   \n",
       "\n",
       "[5 rows x 110 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[['TotalVisits','Total Time Spent on Website','Page Views Per Visit']] = scaler.fit_transform(X_test[['TotalVisits','Total Time Spent on Website','Page Views Per Visit']])\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lead Origin_Landing Page Submission</th>\n",
       "      <th>Lead Origin_Lead Add Form</th>\n",
       "      <th>Lead Source_Welingak Website</th>\n",
       "      <th>Last Activity_Email Bounced</th>\n",
       "      <th>Last Activity_Had a Phone Conversation</th>\n",
       "      <th>Specialization_Unknown</th>\n",
       "      <th>What is your current occupation_Unemployed</th>\n",
       "      <th>Tags_Busy</th>\n",
       "      <th>Tags_Closed by Horizzon</th>\n",
       "      <th>Tags_Lost to EINS</th>\n",
       "      <th>Tags_Ringing</th>\n",
       "      <th>Tags_Will revert after reading the email</th>\n",
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       "  <tbody>\n",
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       "      <td>7936</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4216</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3830</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Lead Origin_Landing Page Submission  Lead Origin_Lead Add Form  \\\n",
       "3271                                    0                          0   \n",
       "1490                                    1                          0   \n",
       "7936                                    0                          0   \n",
       "4216                                    0                          1   \n",
       "3830                                    1                          0   \n",
       "\n",
       "      Lead Source_Welingak Website  Last Activity_Email Bounced  \\\n",
       "3271                             0                            0   \n",
       "1490                             0                            0   \n",
       "7936                             0                            0   \n",
       "4216                             0                            0   \n",
       "3830                             0                            0   \n",
       "\n",
       "      Last Activity_Had a Phone Conversation  Specialization_Unknown  \\\n",
       "3271                                       0                       1   \n",
       "1490                                       0                       0   \n",
       "7936                                       0                       1   \n",
       "4216                                       0                       0   \n",
       "3830                                       0                       0   \n",
       "\n",
       "      What is your current occupation_Unemployed  Tags_Busy  \\\n",
       "3271                                           1          0   \n",
       "1490                                           0          0   \n",
       "7936                                           1          0   \n",
       "4216                                           0          0   \n",
       "3830                                           1          0   \n",
       "\n",
       "      Tags_Closed by Horizzon  Tags_Lost to EINS  Tags_Ringing  \\\n",
       "3271                        0                  0             0   \n",
       "1490                        0                  0             0   \n",
       "7936                        0                  0             0   \n",
       "4216                        1                  0             0   \n",
       "3830                        0                  0             0   \n",
       "\n",
       "      Tags_Will revert after reading the email  Last Notable Activity_SMS Sent  \n",
       "3271                                         1                               0  \n",
       "1490                                         1                               0  \n",
       "7936                                         1                               0  \n",
       "4216                                         0                               0  \n",
       "3830                                         1                               0  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = X_test[col2]\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_const = sm.add_constant(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3271    0.223496\n",
       "1490    0.859638\n",
       "7936    0.223496\n",
       "4216    0.999937\n",
       "3830    0.354966\n",
       "          ...   \n",
       "850     0.354966\n",
       "2879    0.009295\n",
       "6501    0.989813\n",
       "7155    0.354966\n",
       "376     0.354966\n",
       "Length: 2723, dtype: float64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_ytest = res_mod2.predict(X_test_const)\n",
    "\n",
    "pred_ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting y_pred to a dataframe which is an array\n",
    "pred_y1 = pd.DataFrame(pred_ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting y_test to dataframe\n",
    "y_test_df = pd.DataFrame(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Putting Propect ID to index\n",
    "y_test_df['Prospect ID'] = y_test_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing index for both dataframes to append them side by side \n",
    "pred_y1.reset_index(drop=True, inplace=True)\n",
    "y_test_df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Appending y_test_df and y_pred_1\n",
    "y_pred_final = pd.concat([y_test_df, pred_y1],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Converted</th>\n",
       "      <th>Prospect ID</th>\n",
       "      <th>0</th>\n",
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       "  <tbody>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
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       "      <td>7936</td>\n",
       "      <td>0.223496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4216</td>\n",
       "      <td>0.999937</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Prospect ID         0\n",
       "0          0         3271  0.223496\n",
       "1          1         1490  0.859638\n",
       "2          0         7936  0.223496\n",
       "3          1         4216  0.999937\n",
       "4          0         3830  0.354966"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming the column \n",
    "y_pred_final= y_pred_final.rename(columns={ 0 : 'Converted_prob'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rearranging the columns\n",
    "y_pred_final = y_pred_final.reindex(['Prospect ID','Converted','Converted_prob'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>3271</td>\n",
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       "      <td>1</td>\n",
       "      <td>1490</td>\n",
       "      <td>1</td>\n",
       "      <td>0.859638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>7936</td>\n",
       "      <td>0</td>\n",
       "      <td>0.223496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4216</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999937</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3830</td>\n",
       "      <td>0</td>\n",
       "      <td>0.354966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Prospect ID  Converted  Converted_prob\n",
       "0         3271          0        0.223496\n",
       "1         1490          1        0.859638\n",
       "2         7936          0        0.223496\n",
       "3         4216          1        0.999937\n",
       "4         3830          0        0.354966"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_final['Final_Predicted'] = y_pred_final.Converted_prob.map(lambda x: 1 if x > 0.4 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prospect ID</th>\n",
       "      <th>Converted</th>\n",
       "      <th>Converted_prob</th>\n",
       "      <th>Final_Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3271</td>\n",
       "      <td>0</td>\n",
       "      <td>0.223496</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1490</td>\n",
       "      <td>1</td>\n",
       "      <td>0.859638</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>7936</td>\n",
       "      <td>0</td>\n",
       "      <td>0.223496</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4216</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999937</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3830</td>\n",
       "      <td>0</td>\n",
       "      <td>0.354966</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Prospect ID  Converted  Converted_prob  Final_Predicted\n",
       "0         3271          0        0.223496                0\n",
       "1         1490          1        0.859638                1\n",
       "2         7936          0        0.223496                0\n",
       "3         4216          1        0.999937                1\n",
       "4         3830          0        0.354966                0"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8604480352552332"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# overall accuracy.\n",
    "metrics.accuracy_score(y_pred_final.Converted, y_pred_final.Final_Predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1645,   89],\n",
       "       [ 291,  698]], dtype=int64)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_mat2 = metrics.confusion_matrix(y_pred_final.Converted, y_pred_final.Final_Predicted )\n",
    "confusion_mat2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "TruePositive = confusion_mat2[1,1] # true positive \n",
    "TrueNegative = confusion_mat2[0,0] # true negatives\n",
    "FalsePositive = confusion_mat2[0,1] # false positives\n",
    "FalseNegative = confusion_mat2[1,0] # false negatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7057633973710818"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sensitivity of the model\n",
    "TruePositive / float(TruePositive+FalseNegative)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9486735870818915"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  specificity\n",
    "TrueNegative / float(TrueNegative+FalsePositive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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