Loan_Prediction.ipynb

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          "text": [
            "Saving prediction_loan.csv to prediction_loan (1).csv\n"
          ]
        }
      ],
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        "from google.colab import files\n",
        "\n",
        "\n",
        "uploaded = files.upload()"
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "dWncN0bkV4Ak"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('prediction_loan.csv')"
      ],
      "metadata": {
        "id": "MLsSnRquV6_4"
      },
      "execution_count": 82,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "# Loan_ID : Unique Loan ID\n",
        "\n",
        "# Gender : Male/ Female\n",
        "\n",
        "# Married : Applicant married (Y/N)\n",
        "\n",
        "# Dependents : Number of dependents\n",
        "\n",
        "# Education : Applicant Education (Graduate/ Under Graduate)\n",
        "\n",
        "# Self_Employed : Self employed (Y/N)\n",
        "\n",
        "# ApplicantIncome : Applicant income\n",
        "\n",
        "# CoapplicantIncome : Coapplicant income\n",
        "\n",
        "# LoanAmount : Loan amount in thousands of dollars\n",
        "\n",
        "# Loan_Amount_Term : Term of loan in months\n",
        "\n",
        "# Credit_History : Credit history meets guidelines yes or no\n",
        "\n",
        "# Property_Area : Urban/ Semi Urban/ Rural\n",
        "\n",
        "# Loan_Status : Loan approved (Y/N) this is the target variable"
      ],
      "metadata": {
        "id": "Qz-rYatuV-UK"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#1. Display Top 5 Rows of The Dataset"
      ],
      "metadata": {
        "id": "VTUPJBZdWDse"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
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      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
              "0  LP001015   Male     Yes          0      Graduate            No   \n",
              "1  LP001022   Male     Yes          1      Graduate            No   \n",
              "2  LP001031   Male     Yes          2      Graduate            No   \n",
              "3  LP001035   Male     Yes          2      Graduate            No   \n",
              "4  LP001051   Male      No          0  Not Graduate            No   \n",
              "\n",
              "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "0             5720                  0       110.0             360.0   \n",
              "1             3076               1500       126.0             360.0   \n",
              "2             5000               1800       208.0             360.0   \n",
              "3             2340               2546       100.0             360.0   \n",
              "4             3276                  0        78.0             360.0   \n",
              "\n",
              "   Credit_History Property_Area Loan_Status  \n",
              "0             1.0         Urban           Y  \n",
              "1             1.0         Urban           Y  \n",
              "2             1.0         Urban           Y  \n",
              "3             NaN         Urban           N  \n",
              "4             1.0         Urban           Y  "
            ],
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-67508f0e-b5c0-46ba-9d62-3a47964d50f4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 367,\n  \"fields\": [\n    {\n      \"column\": \"Loan_ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 367,\n        \"samples\": [\n          \"LP002070\",\n          \"LP001187\",\n          \"LP001099\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"1\",\n          \"3+\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Not Graduate\",\n          \"Graduate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4910,\n        \"min\": 0,\n        \"max\": 72529,\n        \"num_unique_values\": 314,\n        \"samples\": [\n          6794,\n          2783\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2334,\n        \"min\": 0,\n        \"max\": 24000,\n        \"num_unique_values\": 194,\n        \"samples\": [\n          3725,\n          833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61.36665239301822,\n        \"min\": 28.0,\n        \"max\": 550.0,\n        \"num_unique_values\": 144,\n        \"samples\": [\n          112.0,\n          28.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.15664341399702,\n        \"min\": 6.0,\n        \"max\": 480.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          120.0,\n          36.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38014983549147024,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Urban\",\n          \"Semiurban\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"N\",\n          \"Y\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2. Check Last 5 Rows of The Dataset"
      ],
      "metadata": {
        "id": "9IuxwpuLWLsT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "cApAY0w_WN59",
        "outputId": "cc87c784-8c82-476d-87af-a8bb7856564d"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
              "362  LP002971   Male     Yes         3+  Not Graduate           Yes   \n",
              "363  LP002975   Male     Yes          0      Graduate            No   \n",
              "364  LP002980   Male      No          0      Graduate            No   \n",
              "365  LP002986   Male     Yes          0      Graduate            No   \n",
              "366  LP002989   Male      No          0      Graduate           Yes   \n",
              "\n",
              "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "362             4009               1777       113.0             360.0   \n",
              "363             4158                709       115.0             360.0   \n",
              "364             3250               1993       126.0             360.0   \n",
              "365             5000               2393       158.0             360.0   \n",
              "366             9200                  0        98.0             180.0   \n",
              "\n",
              "     Credit_History Property_Area Loan_Status  \n",
              "362             1.0         Urban           Y  \n",
              "363             1.0         Urban           Y  \n",
              "364             NaN     Semiurban           Y  \n",
              "365             1.0         Rural           Y  \n",
              "366             1.0         Rural           Y  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-047cecaa-169b-4cb8-9f3a-408eb084d012\" class=\"colab-df-container\">\n",
              "    <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",
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              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
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              "      <th>Education</th>\n",
              "      <th>Self_Employed</th>\n",
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              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Property_Area</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>362</th>\n",
              "      <td>LP002971</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>3+</td>\n",
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              "      <td>1993</td>\n",
              "      <td>126.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Semiurban</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
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              "      <td>LP002986</td>\n",
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              "      <td>2393</td>\n",
              "      <td>158.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>366</th>\n",
              "      <td>LP002989</td>\n",
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              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
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              "      <td>9200</td>\n",
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              "      <td>98.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>Y</td>\n",
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              "  </tbody>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    }\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "        const element = document.querySelector('#df-047cecaa-169b-4cb8-9f3a-408eb084d012');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Loan_ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"LP002975\",\n          \"LP002989\",\n          \"LP002980\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Graduate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2362,\n        \"min\": 3250,\n        \"max\": 9200,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4158\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 989,\n        \"min\": 0,\n        \"max\": 2393,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          709\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.4610774452162,\n        \"min\": 98.0,\n        \"max\": 158.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          115.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.49844718999243,\n        \"min\": 180.0,\n        \"max\": 360.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          180.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Urban\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Y\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Find Shape of Our Dataset (Number of Rows And Number of Columns)"
      ],
      "metadata": {
        "id": "P-8EdW-hWT99"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dcVaBCmBWVkm",
        "outputId": "7631290f-1cb5-45e2-c6dd-b56b6eb20611"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(367, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Number of Rows\",data.shape[0])\n",
        "print(\"Number of Columns\",data.shape[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t-AxwdPNWZEe",
        "outputId": "fd63879e-cc44-455e-b3f0-1fad610ac4ca"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Rows 367\n",
            "Number of Columns 13\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Get Information About Our Dataset Like Total Number Rows, Total Number of Columns, Datatypes of Each Column And Memory Requirement"
      ],
      "metadata": {
        "id": "LqOPcf-mWhE5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rik5R97CWisi",
        "outputId": "c5313db6-5517-4841-b978-5af2fedf8a36"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 367 entries, 0 to 366\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Loan_ID            367 non-null    object \n",
            " 1   Gender             356 non-null    object \n",
            " 2   Married            367 non-null    object \n",
            " 3   Dependents         357 non-null    object \n",
            " 4   Education          367 non-null    object \n",
            " 5   Self_Employed      344 non-null    object \n",
            " 6   ApplicantIncome    367 non-null    int64  \n",
            " 7   CoapplicantIncome  367 non-null    int64  \n",
            " 8   LoanAmount         362 non-null    float64\n",
            " 9   Loan_Amount_Term   361 non-null    float64\n",
            " 10  Credit_History     338 non-null    float64\n",
            " 11  Property_Area      367 non-null    object \n",
            " 12  Loan_Status        367 non-null    object \n",
            "dtypes: float64(3), int64(2), object(8)\n",
            "memory usage: 37.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Check Null Values In The Dataset"
      ],
      "metadata": {
        "id": "Y9Z_S3gmWoPb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bEfpK0GPWp9-",
        "outputId": "cdb72d97-092f-4c60-d055-eae233153234"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID               0\n",
              "Gender               11\n",
              "Married               0\n",
              "Dependents           10\n",
              "Education             0\n",
              "Self_Employed        23\n",
              "ApplicantIncome       0\n",
              "CoapplicantIncome     0\n",
              "LoanAmount            5\n",
              "Loan_Amount_Term      6\n",
              "Credit_History       29\n",
              "Property_Area         0\n",
              "Loan_Status           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()*100 / len(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HAl5jWjGWuSQ",
        "outputId": "0558513a-c71f-4f66-9c59-87063d9504e3"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID              0.000000\n",
              "Gender               2.997275\n",
              "Married              0.000000\n",
              "Dependents           2.724796\n",
              "Education            0.000000\n",
              "Self_Employed        6.267030\n",
              "ApplicantIncome      0.000000\n",
              "CoapplicantIncome    0.000000\n",
              "LoanAmount           1.362398\n",
              "Loan_Amount_Term     1.634877\n",
              "Credit_History       7.901907\n",
              "Property_Area        0.000000\n",
              "Loan_Status          0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6. Handling The missing Values"
      ],
      "metadata": {
        "id": "H6EXQEulWw8a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "data = data.drop('Loan_ID',axis=1)"
      ],
      "metadata": {
        "id": "nzkz_kjcWydk"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "id": "4irGWYrXW09V",
        "outputId": "3ce45f91-04e7-4d7a-baad-e57a9ce7656a"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Gender Married Dependents Education Self_Employed  ApplicantIncome  \\\n",
              "0   Male     Yes          0  Graduate            No             5720   \n",
              "\n",
              "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
              "0                  0       110.0             360.0             1.0   \n",
              "\n",
              "  Property_Area Loan_Status  \n",
              "0         Urban           Y  "
            ],
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>Gender</th>\n",
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              "        document.querySelector('#df-d1a84125-52a4-4c30-8059-c7d06e798860 button.colab-df-convert');\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 367,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"1\",\n          \"3+\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Not Graduate\",\n          \"Graduate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4910,\n        \"min\": 0,\n        \"max\": 72529,\n        \"num_unique_values\": 314,\n        \"samples\": [\n          6794,\n          2783\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2334,\n        \"min\": 0,\n        \"max\": 24000,\n        \"num_unique_values\": 194,\n        \"samples\": [\n          3725,\n          833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61.36665239301822,\n        \"min\": 28.0,\n        \"max\": 550.0,\n        \"num_unique_values\": 144,\n        \"samples\": [\n          112.0,\n          28.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.15664341399702,\n        \"min\": 6.0,\n        \"max\": 480.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          120.0,\n          36.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38014983549147024,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Urban\",\n          \"Semiurban\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"N\",\n          \"Y\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "columns = ['Gender','Dependents','LoanAmount','Loan_Amount_Term']"
      ],
      "metadata": {
        "id": "HkurD9uqW5rb"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.dropna(subset=columns)"
      ],
      "metadata": {
        "id": "AAV9po5SW7uV"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()*100 / len(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rVrJaNm7W90k",
        "outputId": "e1149eca-d9d9-4c54-cfde-bbd5e21da6f9"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender               0.000000\n",
              "Married              0.000000\n",
              "Dependents           0.000000\n",
              "Education            0.000000\n",
              "Self_Employed        6.824926\n",
              "ApplicantIncome      0.000000\n",
              "CoapplicantIncome    0.000000\n",
              "LoanAmount           0.000000\n",
              "Loan_Amount_Term     0.000000\n",
              "Credit_History       7.715134\n",
              "Property_Area        0.000000\n",
              "Loan_Status          0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Self_Employed'].mode()[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "9LFhpsduW_lw",
        "outputId": "d88472d6-4c3b-4267-f65b-5180cbc1fe32"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'No'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Self_Employed'] =data['Self_Employed'].fillna(data['Self_Employed'].mode()[0])"
      ],
      "metadata": {
        "id": "Zmgq-nJ1XB6M"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()*100 / len(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ABrZtIyXXE1z",
        "outputId": "aa67e23d-0afd-42b5-d9b8-2e8392afbf25"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender               0.000000\n",
              "Married              0.000000\n",
              "Dependents           0.000000\n",
              "Education            0.000000\n",
              "Self_Employed        0.000000\n",
              "ApplicantIncome      0.000000\n",
              "CoapplicantIncome    0.000000\n",
              "LoanAmount           0.000000\n",
              "Loan_Amount_Term     0.000000\n",
              "Credit_History       7.715134\n",
              "Property_Area        0.000000\n",
              "Loan_Status          0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Gender'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ahz85xNXXHXz",
        "outputId": "9c5642bb-1983-47c4-8f03-3c057e4ed1ce"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Male', 'Female'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Self_Employed'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eM8Sot_7XKDS",
        "outputId": "65c1f82c-009e-4d92-9070-20054bce9eda"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['No', 'Yes'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Credit_History'].mode()[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3oI07ZSVXMak",
        "outputId": "03fbab23-c8ae-40fc-9ea1-4d686a6218ea"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Credit_History'] =data['Credit_History'].fillna(data['Credit_History'].mode()[0])"
      ],
      "metadata": {
        "id": "9sy4xK1mXO3V"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()*100 / len(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K9aiDL5fXRoF",
        "outputId": "cda2436b-8060-4e55-fcae-03004f3fdeb9"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender               0.0\n",
              "Married              0.0\n",
              "Dependents           0.0\n",
              "Education            0.0\n",
              "Self_Employed        0.0\n",
              "ApplicantIncome      0.0\n",
              "CoapplicantIncome    0.0\n",
              "LoanAmount           0.0\n",
              "Loan_Amount_Term     0.0\n",
              "Credit_History       0.0\n",
              "Property_Area        0.0\n",
              "Loan_Status          0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 7. Handling Categorical Columns"
      ],
      "metadata": {
        "id": "VaK0j93BXabq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "9oIiEFKCXb5p",
        "outputId": "ddbee47f-c261-40c1-86d0-77be2dadf77c"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Gender Married Dependents     Education Self_Employed  ApplicantIncome  \\\n",
              "288   Male      No          0      Graduate            No             4983   \n",
              "36    Male     Yes          2      Graduate            No             7350   \n",
              "135   Male     Yes          0      Graduate            No             4258   \n",
              "155   Male      No          0  Not Graduate           Yes             1599   \n",
              "82    Male     Yes          2  Not Graduate            No             4136   \n",
              "\n",
              "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
              "288                  0       141.0             360.0             1.0   \n",
              "36                4029       185.0             180.0             1.0   \n",
              "135                  0       125.0             360.0             1.0   \n",
              "155               2474       125.0             300.0             1.0   \n",
              "82                   0       149.0             480.0             0.0   \n",
              "\n",
              "    Property_Area Loan_Status  \n",
              "288         Urban           Y  \n",
              "36          Urban           Y  \n",
              "135         Urban           Y  \n",
              "155     Semiurban           Y  \n",
              "82          Rural           N  "
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Not Graduate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2058,\n        \"min\": 1599,\n        \"max\": 7350,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          7350\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1863,\n        \"min\": 0,\n        \"max\": 4029,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.657656011875904,\n        \"min\": 125.0,\n        \"max\": 185.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          185.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 108.9954127475097,\n        \"min\": 180.0,\n        \"max\": 480.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          180.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4472135954999579,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Urban\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"N\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Dependents'] =data['Dependents'].replace(to_replace=\"3+\",value='4')"
      ],
      "metadata": {
        "id": "eqFdnJPzXfwr"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Dependents'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NthSU4kAXiMA",
        "outputId": "0cd15d02-8236-47f7-bdc3-ed994453211c"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['0', '1', '2', '4'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Loan_Status'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4SgRMlNvX_2X",
        "outputId": "5394112b-2ea5-46ac-9bcf-d1eda5c89118"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Y', 'N'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q6Z7LbHIfra_",
        "outputId": "0e34f86e-d592-4d7b-9ad7-dd9b7b8ee525"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Gender  Married Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
            "0       0        0          0          0              0             5720   \n",
            "1       0        0          1          0              0             3076   \n",
            "2       0        0          2          0              0             5000   \n",
            "3       0        0          2          0              0             2340   \n",
            "4       0        0          0          0              0             3276   \n",
            "\n",
            "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
            "0                  0       110.0             360.0             1.0   \n",
            "1               1500       126.0             360.0             1.0   \n",
            "2               1800       208.0             360.0             1.0   \n",
            "3               2546       100.0             360.0             0.0   \n",
            "4                  0        78.0             360.0             1.0   \n",
            "\n",
            "   Property_Area  Loan_Status  \n",
            "0              0          0.0  \n",
            "1              0          0.0  \n",
            "2              0          0.0  \n",
            "3              0          0.0  \n",
            "4              0          0.0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"Gender\"] = data[\"Gender\"].map({\"Male\": 1, \"Female\": 0})\n",
        "data[\"Married\"] = data[\"Married\"].map({\"Yes\": 1, \"No\": 0})\n",
        "data[\"Education\"] = data[\"Education\"].map({\"Graduate\": 1, \"Not Graduate\": 0})\n",
        "data[\"Self_Employed\"] = data[\"Self_Employed\"].map({\"Yes\": 1, \"No\": 0})\n",
        "data[\"Property_Area\"] = data[\"Property_Area\"].map({\"Rural\": 0, \"Urban\": 1, \"Semiurban\": 2})\n",
        "data[\"Loan_Status\"] = data[\"Loan_Status\"].map({\"Y\": 1, \"N\": 0}) # Changed all instances of 'df' to 'data'"
      ],
      "metadata": {
        "id": "jW-ncstAhN2H"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "w7qm5Dd8h5Xn",
        "outputId": "c27e7437-c450-4011-94f1-bfb7cdb1ac9d"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Gender  Married Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
              "0       1        1          0          1              0             5720   \n",
              "1       1        1          1          1              0             3076   \n",
              "2       1        1          2          1              0             5000   \n",
              "3       1        1          2          1              0             2340   \n",
              "4       1        0          0          0              0             3276   \n",
              "\n",
              "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
              "0                  0       110.0             360.0             1.0   \n",
              "1               1500       126.0             360.0             1.0   \n",
              "2               1800       208.0             360.0             1.0   \n",
              "3               2546       100.0             360.0             1.0   \n",
              "4                  0        78.0             360.0             1.0   \n",
              "\n",
              "   Property_Area  Loan_Status  \n",
              "0              1            1  \n",
              "1              1            1  \n",
              "2              1            1  \n",
              "3              1            0  \n",
              "4              1            1  "
            ],
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-8fa1d38a-b25d-475c-b5a6-857f863bd051 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8fa1d38a-b25d-475c-b5a6-857f863bd051');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 337,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"1\",\n          \"4\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4848,\n        \"min\": 0,\n        \"max\": 72529,\n        \"num_unique_values\": 289,\n        \"samples\": [\n          8000,\n          7874\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2393,\n        \"min\": 0,\n        \"max\": 24000,\n        \"num_unique_values\": 180,\n        \"samples\": [\n          1683,\n          2367\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62.715533548130246,\n        \"min\": 28.0,\n        \"max\": 550.0,\n        \"num_unique_values\": 141,\n        \"samples\": [\n          104.0,\n          64.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 64.74018409805147,\n        \"min\": 6.0,\n        \"max\": 480.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          120.0,\n          36.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3701018958485958,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 8. Store Feature Matrix In X And Response (Target) In Vector y"
      ],
      "metadata": {
        "id": "9YxxEZgiiM75"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop('Loan_Status',axis=1)"
      ],
      "metadata": {
        "id": "xu0kEXODiOwM"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = data['Loan_Status']"
      ],
      "metadata": {
        "id": "F5d3THmRiRSl"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "85EDMZIyiTxM",
        "outputId": "7c4b8c17-c352-44dd-c859-71ba4d981577"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "1      1\n",
              "2      1\n",
              "3      0\n",
              "4      1\n",
              "      ..\n",
              "362    1\n",
              "363    1\n",
              "364    1\n",
              "365    1\n",
              "366    1\n",
              "Name: Loan_Status, Length: 337, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 9. Feature Scaling"
      ],
      "metadata": {
        "id": "ykoPqQLYiX3W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "7Qcjp-ghiWGt",
        "outputId": "4ff862d4-2ceb-43e8-c097-00c1ffb062ab"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Gender  Married Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
              "0       1        1          0          1              0             5720   \n",
              "1       1        1          1          1              0             3076   \n",
              "2       1        1          2          1              0             5000   \n",
              "3       1        1          2          1              0             2340   \n",
              "4       1        0          0          0              0             3276   \n",
              "\n",
              "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
              "0                  0       110.0             360.0             1.0   \n",
              "1               1500       126.0             360.0             1.0   \n",
              "2               1800       208.0             360.0             1.0   \n",
              "3               2546       100.0             360.0             1.0   \n",
              "4                  0        78.0             360.0             1.0   \n",
              "\n",
              "   Property_Area  Loan_Status  \n",
              "0              1            1  \n",
              "1              1            1  \n",
              "2              1            1  \n",
              "3              1            0  \n",
              "4              1            1  "
            ],
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              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 337,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"1\",\n          \"4\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4848,\n        \"min\": 0,\n        \"max\": 72529,\n        \"num_unique_values\": 289,\n        \"samples\": [\n          8000,\n          7874\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2393,\n        \"min\": 0,\n        \"max\": 24000,\n        \"num_unique_values\": 180,\n        \"samples\": [\n          1683,\n          2367\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62.715533548130246,\n        \"min\": 28.0,\n        \"max\": 550.0,\n        \"num_unique_values\": 141,\n        \"samples\": [\n          104.0,\n          64.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 64.74018409805147,\n        \"min\": 6.0,\n        \"max\": 480.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          120.0,\n          36.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3701018958485958,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols = ['ApplicantIncome','CoapplicantIncome','LoanAmount','Loan_Amount_Term']"
      ],
      "metadata": {
        "id": "aiQ3ogpEicm9"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "st = StandardScaler()\n",
        "X[cols]=st.fit_transform(X[cols])"
      ],
      "metadata": {
        "id": "wj6ZdVGDigbG"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "PbgHRs_6iiL0",
        "outputId": "d216a015-f33d-47f8-fc96-90f0817d4d08"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Gender  Married Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
              "0         1        1          0          1              0         0.203074   \n",
              "1         1        1          1          1              0        -0.343098   \n",
              "2         1        1          2          1              0         0.054343   \n",
              "3         1        1          2          1              0        -0.495133   \n",
              "4         1        0          0          0              0        -0.301784   \n",
              "..      ...      ...        ...        ...            ...              ...   \n",
              "362       1        1          4          0              1        -0.150368   \n",
              "363       1        1          0          1              0        -0.119589   \n",
              "364       1        0          0          1              0        -0.307154   \n",
              "365       1        1          0          1              0         0.054343   \n",
              "366       1        0          0          1              1         0.921938   \n",
              "\n",
              "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
              "0            -0.654399   -0.433904          0.271195             1.0   \n",
              "1            -0.026659   -0.178404          0.271195             1.0   \n",
              "2             0.098889    1.131031          0.271195             1.0   \n",
              "3             0.411085   -0.593591          0.271195             1.0   \n",
              "4            -0.654399   -0.944903          0.271195             1.0   \n",
              "..                 ...         ...               ...             ...   \n",
              "362           0.089263   -0.385998          0.271195             1.0   \n",
              "363          -0.357688   -0.354060          0.271195             1.0   \n",
              "364           0.179658   -0.178404          0.271195             1.0   \n",
              "365           0.347055    0.332595          0.271195             1.0   \n",
              "366          -0.654399   -0.625529         -2.513283             1.0   \n",
              "\n",
              "     Property_Area  \n",
              "0                1  \n",
              "1                1  \n",
              "2                1  \n",
              "3                1  \n",
              "4                1  \n",
              "..             ...  \n",
              "362              1  \n",
              "363              1  \n",
              "364              2  \n",
              "365              0  \n",
              "366              0  \n",
              "\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f9c67bab-01aa-4136-bf8b-2fcddcbc7802 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_bcf544b1-fc62-483e-894f-dcb66e4ca84b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_bcf544b1-fc62-483e-894f-dcb66e4ca84b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 337,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"1\",\n          \"4\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0014869896689576,\n        \"min\": -0.9785070896484029,\n        \"max\": 14.003813398681016,\n        \"num_unique_values\": 289,\n        \"samples\": [\n          0.6740534572588391,\n          0.6480256286450501\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0014869896689576,\n        \"min\": -0.6543992751396679,\n        \"max\": 9.389438941468617,\n        \"num_unique_values\": 180,\n        \"samples\": [\n          0.049924879799987956,\n          0.33617426897332403\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0014869896689573,\n        \"min\": -1.743339144775121,\n        \"max\": 6.592334316505374,\n        \"num_unique_values\": 141,\n        \"samples\": [\n          -0.5297161887266199,\n          -1.168465112962673\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0014869896689576,\n        \"min\": -5.204946095190017,\n        \"max\": 2.127514257164361,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -3.441442972471876,\n          -4.7408663260536645\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3701018958485958,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 10. Splitting The Dataset Into The Training Set And Test Set & Applying K-Fold Cross Validation"
      ],
      "metadata": {
        "id": "aZ3Sk5_PioLc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "iIT6bPWPip4s"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_df={}\n",
        "def model_val(model,X,y):\n",
        "    X_train,X_test,y_train,y_test=train_test_split(X,y,\n",
        "                                                   test_size=0.20,\n",
        "                                                   random_state=42)\n",
        "    model.fit(X_train,y_train)\n",
        "    y_pred=model.predict(X_test)\n",
        "    print(f\"{model} accuracy is {accuracy_score(y_test,y_pred)}\")\n",
        "\n",
        "    score = cross_val_score(model,X,y,cv=5)\n",
        "    print(f\"{model} Avg cross val score is {np.mean(score)}\")\n",
        "    model_df[model]=round(np.mean(score)*100,2)"
      ],
      "metadata": {
        "id": "CNccdikvi018"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8O9_rwrci2BM",
        "outputId": "69dfb011-8dd1-4da1-b072-906f164323a1"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{}"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 11. Logistic Regression"
      ],
      "metadata": {
        "id": "D10V4PNDjCUD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "model = LogisticRegression()\n",
        "model_val(model,X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DR7XGOswi4ZD",
        "outputId": "fc415dcf-216d-4be3-9397-9f320d1e8bf6"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LogisticRegression() accuracy is 0.9705882352941176\n",
            "LogisticRegression() Avg cross val score is 0.9614135206321335\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 12. SVC"
      ],
      "metadata": {
        "id": "Ag8OnTmbjJC1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import svm\n",
        "model = svm.SVC()\n",
        "model_val(model,X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zdw1hovTjGij",
        "outputId": "ac78156b-a20e-4bce-8676-50d6a156417d"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC() accuracy is 0.9558823529411765\n",
            "SVC() Avg cross val score is 0.949604916593503\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 13. Decision Tree Classifier"
      ],
      "metadata": {
        "id": "xEZllG2ajSns"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "model = DecisionTreeClassifier()\n",
        "model_val(model,X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hPrshv-djOkT",
        "outputId": "f7427471-2bcb-4ec6-cf67-f41d0dd4e9ba"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DecisionTreeClassifier() accuracy is 0.9117647058823529\n",
            "DecisionTreeClassifier() Avg cross val score is 0.940649692712906\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 14. Random Forest Classifier"
      ],
      "metadata": {
        "id": "sJGOYAzvjVEg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "model =RandomForestClassifier()\n",
        "model_val(model,X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cmzb966njZnU",
        "outputId": "ec0a9da5-f6de-4b63-b0db-3c0fc4f63a44"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RandomForestClassifier() accuracy is 0.9705882352941176\n",
            "RandomForestClassifier() Avg cross val score is 0.9673836698858647\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 15. Gradient Boosting Classifier"
      ],
      "metadata": {
        "id": "xrjsp63DjdKd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "model =GradientBoostingClassifier()\n",
        "model_val(model,X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ucoAQEdjcrW",
        "outputId": "eace77c1-6476-4875-bd23-2201740602c8"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GradientBoostingClassifier() accuracy is 0.9558823529411765\n",
            "GradientBoostingClassifier() Avg cross val score is 0.9585162423178227\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 16. Save The Model"
      ],
      "metadata": {
        "id": "UKVUSAE3kYhF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop('Loan_Status',axis=1)\n",
        "y = data['Loan_Status']"
      ],
      "metadata": {
        "id": "HDmmDS03kVrE"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier(n_estimators=270,\n",
        " min_samples_split=5,\n",
        " min_samples_leaf=5,\n",
        " max_features='sqrt',\n",
        " max_depth=5)"
      ],
      "metadata": {
        "id": "FsAptBLukdP9"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf.fit(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "pgSfaswFkf6i",
        "outputId": "682bd949-c629-47a2-e5f6-e81b399d6079"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=5, min_samples_leaf=5, min_samples_split=5,\n",
              "                       n_estimators=270)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=5, min_samples_leaf=5, min_samples_split=5,\n",
              "                       n_estimators=270)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=5, min_samples_leaf=5, min_samples_split=5,\n",
              "                       n_estimators=270)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib"
      ],
      "metadata": {
        "id": "Cu4LdnNUkisc"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(rf,'loan_status_predict')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "13myN77TkmSE",
        "outputId": "bd48b4c9-bbec-4e2d-f028-32587646114f"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['loan_status_predict']"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = joblib.load('loan_status_predict')"
      ],
      "metadata": {
        "id": "3dAsrTXQkqFS"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.DataFrame({\n",
        "    'Gender':1,\n",
        "    'Married':1,\n",
        "    'Dependents':2,\n",
        "    'Education':0,\n",
        "    'Self_Employed':0,\n",
        "    'ApplicantIncome':2889,\n",
        "    'CoapplicantIncome':0.0,\n",
        "    'LoanAmount':45,\n",
        "    'Loan_Amount_Term':180,\n",
        "    'Credit_History':0,\n",
        "    'Property_Area':1\n",
        "},index=[0])"
      ],
      "metadata": {
        "id": "z-MmOVOLkrqj"
      },
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "AqbehfEckwZU",
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      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
              "0       1        1           2          0              0             2889   \n",
              "\n",
              "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
              "0                0.0          45               180               0   \n",
              "\n",
              "   Property_Area  \n",
              "0              1  "
            ],
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              "      <th>Gender</th>\n",
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              "      <th>Education</th>\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2889,\n        \"max\": 2889,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2889\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 45,\n        \"max\": 45,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 180,\n        \"max\": 180,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          180\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result = model.predict(df)"
      ],
      "metadata": {
        "id": "VDdt1Cd2kz7u"
      },
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if result==1:\n",
        "    print(\"Loan Approved\")\n",
        "else:\n",
        "    print(\"Loan Not Approved\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6QBf7TXk2gM",
        "outputId": "5d84a612-ab0b-4822-f184-03982cc70fdc"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loan Not Approved\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
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    },
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