Percobaan.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Percobaan.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "CZEYLFAP4esh"
      },
      "source": [
        "#linear regression y = a + bx\n",
        "a = 2\n",
        "b = 4"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w90d0Bfq5-Pz",
        "outputId": "88c37a60-c12d-4ab6-cda7-d9a69cd01960"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x = np.linspace(1,1000,num=10000)\n",
        "x"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([   1.        ,    1.09990999,    1.19981998, ...,  999.80018002,\n",
              "        999.90009001, 1000.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s-3rYggz6WLa",
        "outputId": "c1670388-2680-49f0-b7e9-3ff5bdd9b5bb"
      },
      "source": [
        "x.dtype"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('float64')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k2CJx-I76cK4",
        "outputId": "2a957407-f92c-4447-bb24-642bc64b9a76"
      },
      "source": [
        "y = 2 + 4*x\n",
        "y"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([   6.        ,    6.39963996,    6.79927993, ..., 4001.20072007,\n",
              "       4001.60036004, 4002.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "xWHxDH3T6wA0",
        "outputId": "6ad05f56-d568-4893-9be0-98c6f0e58843"
      },
      "source": [
        "plt.plot(x,y,'r-')\n",
        "plt.xlabel('X')\n",
        "plt.ylabel('Y')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Y')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "6zKXyYf06_qd",
        "outputId": "ef35c7b9-a72f-4b5d-f9f2-7e4b2f50a55f"
      },
      "source": [
        "import pandas as pd \n",
        "\n",
        "dataset = pd.read_csv('https://raw.githubusercontent.com/asrulabdullah99/data_mining/master/Customer.csv')\n",
        "dataset"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>IDPelanggan</th>\n",
              "      <th>Kelamin</th>\n",
              "      <th>Usia</th>\n",
              "      <th>Rating_belanja (1-100)</th>\n",
              "      <th>Pendapatan (juta Rp)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Perempuan</td>\n",
              "      <td>23</td>\n",
              "      <td>87</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Laki</td>\n",
              "      <td>60</td>\n",
              "      <td>4</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Perempuan</td>\n",
              "      <td>21</td>\n",
              "      <td>73</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Laki</td>\n",
              "      <td>53</td>\n",
              "      <td>4</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Laki</td>\n",
              "      <td>18</td>\n",
              "      <td>92</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>196</td>\n",
              "      <td>Laki</td>\n",
              "      <td>28</td>\n",
              "      <td>75</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>197</td>\n",
              "      <td>Laki</td>\n",
              "      <td>36</td>\n",
              "      <td>10</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>198</td>\n",
              "      <td>Laki</td>\n",
              "      <td>36</td>\n",
              "      <td>92</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>199</td>\n",
              "      <td>Perempuan</td>\n",
              "      <td>52</td>\n",
              "      <td>13</td>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>200</td>\n",
              "      <td>Perempuan</td>\n",
              "      <td>30</td>\n",
              "      <td>86</td>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     IDPelanggan    Kelamin  Usia  Rating_belanja (1-100)  Pendapatan (juta Rp)\n",
              "0              1  Perempuan    23                      87                    29\n",
              "1              2       Laki    60                       4                    30\n",
              "2              3  Perempuan    21                      73                    30\n",
              "3              4       Laki    53                       4                    33\n",
              "4              5       Laki    18                      92                    33\n",
              "..           ...        ...   ...                     ...                   ...\n",
              "195          196       Laki    28                      75                    87\n",
              "196          197       Laki    36                      10                    87\n",
              "197          198       Laki    36                      92                    87\n",
              "198          199  Perempuan    52                      13                    88\n",
              "199          200  Perempuan    30                      86                    88\n",
              "\n",
              "[200 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJBgZZf8839u",
        "outputId": "1c260d87-6731-45f9-9a57-0af2b8127c88"
      },
      "source": [
        "dataset.describe"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.describe of      IDPelanggan    Kelamin  Usia  Rating_belanja (1-100)  Pendapatan (juta Rp)\n",
              "0              1  Perempuan    23                      87                    29\n",
              "1              2       Laki    60                       4                    30\n",
              "2              3  Perempuan    21                      73                    30\n",
              "3              4       Laki    53                       4                    33\n",
              "4              5       Laki    18                      92                    33\n",
              "..           ...        ...   ...                     ...                   ...\n",
              "195          196       Laki    28                      75                    87\n",
              "196          197       Laki    36                      10                    87\n",
              "197          198       Laki    36                      92                    87\n",
              "198          199  Perempuan    52                      13                    88\n",
              "199          200  Perempuan    30                      86                    88\n",
              "\n",
              "[200 rows x 5 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "EpY7mMpL88C5",
        "outputId": "b5030e56-8d55-48c5-e650-6b753d7ef836"
      },
      "source": [
        "dataset.describe()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>IDPelanggan</th>\n",
              "      <th>Usia</th>\n",
              "      <th>Rating_belanja (1-100)</th>\n",
              "      <th>Pendapatan (juta Rp)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>38.850000</td>\n",
              "      <td>50.200000</td>\n",
              "      <td>60.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>57.879185</td>\n",
              "      <td>13.969007</td>\n",
              "      <td>25.823522</td>\n",
              "      <td>26.264721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50.750000</td>\n",
              "      <td>28.750000</td>\n",
              "      <td>34.750000</td>\n",
              "      <td>41.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>61.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>150.250000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>78.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>137.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       IDPelanggan        Usia  Rating_belanja (1-100)  Pendapatan (juta Rp)\n",
              "count   200.000000  200.000000              200.000000            200.000000\n",
              "mean    100.500000   38.850000               50.200000             60.560000\n",
              "std      57.879185   13.969007               25.823522             26.264721\n",
              "min       1.000000   18.000000                1.000000             15.000000\n",
              "25%      50.750000   28.750000               34.750000             41.500000\n",
              "50%     100.500000   36.000000               50.000000             61.500000\n",
              "75%     150.250000   49.000000               73.000000             78.000000\n",
              "max     200.000000   70.000000               99.000000            137.000000"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7giVw7-w9Bnm",
        "outputId": "b07836d5-6fe7-4f2f-d2c1-233eea330588"
      },
      "source": [
        "usia = dataset.iloc[:,2]\n",
        "usia"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      23\n",
              "1      60\n",
              "2      21\n",
              "3      53\n",
              "4      18\n",
              "       ..\n",
              "195    28\n",
              "196    36\n",
              "197    36\n",
              "198    52\n",
              "199    30\n",
              "Name: Usia, Length: 200, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SIcwdeGh9ojU",
        "outputId": "9141da96-06d4-4fc5-82a1-2c6693a04a99"
      },
      "source": [
        "rating = dataset.iloc[:,3]\n",
        "rating"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      87\n",
              "1       4\n",
              "2      73\n",
              "3       4\n",
              "4      92\n",
              "       ..\n",
              "195    75\n",
              "196    10\n",
              "197    92\n",
              "198    13\n",
              "199    86\n",
              "Name: Rating_belanja (1-100), Length: 200, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "5dA1WxTR97Lr",
        "outputId": "5006e024-23bb-4bee-b027-7f4ecb878649"
      },
      "source": [
        "plt.scatter(usia,rating)\n",
        "plt.xlabel('usia')\n",
        "plt.ylabel('rating')"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'rating')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ysmIA4wdfa2d59Ylg/coB7Hn2iLf7NMTe4GVi/x1wueKy73MN0lJwJdQ8hZiizdgikNjV8XrFFLWHuL51/ExNXJ1rdC2FGNnUvmXRps9anLOCi9lc8zruXW26B0LMAwlxfVP/HbC5f6s4V1YKsM9IMWVBXLG831vmhamMpmgwxqwGH7rJJkp1dVoT26y6K5bbpXLbqvr6ppTZM5Pt/VsUjJYd70ZH2Ucisl9Enp7x5/si8hUROc9ZaQKxzUgxZUGU9eH6KmM3mVM2Ys+yqmM2kS3brLo9zx7xWZzKpZTZM5Pt/Wv6TE3Hu9FpS+HvAJwE8K388Q3I1it6GcDfAPgdZyUKoJvatyga+sx9TxU+10XEYiqLz1nBKeT0pxwlumJquTalFdmk+QtlqwO40uk8hd9W1VtVdX/+578C+Leq+mVkq5omzVXt6zNiKSujr9ztFKLwVKJEny0uU8t1IJFr06smzV8wfaam493otKXQJyKXqeoPAUBEfgNAa0H9E85KE4ir2tdnxFJWRl99uClE4SlEiVW0uEz3QOzXxpVUx4ls798q7vdOK4VPAfi6iLwXWUbbmwA+JSLvAXCbs9IEMmDIJrKtfcsyL4oyDEzP9VlGGynk9KeQTVRFxkhZBkvM1yZltpmGLj6jKj5Tq3kK+bLXUNU3nJWgB67mKfjOcy56/blnCCDA8ZOnrn9ss6iblP/tk+95Lfycqmd7zWP7jHqepyAivwJgPbLxgzmS92+r6pcclTEo37VvUaR4/N3TvybKokfbVoiLsjPSdMN3i6tJefqxlMX2mqf0GXXaffQQgDeQ7X3wtr/ihOOzT9KmD77suUVl9N1fnWpfbUx89wOnMPZjElOGm01ZbK95Sp9Rp5XCIlW9ymtJAvMZrZgiRdNzbaQUgdRNp/eM7xaXbUskpsg8pvvXpiy2qxqkMD7X0mlK6v8SkSGvJQmoFSFMHj0GxakIwVXa4OZ1yzBvbt+0Y3PPEMztm55m2k30WMUMRzqd7T0zsmIAj4+uxQvbrsbjo2udfuEV3V+me8n3vW4rpgjapiyma95a1WDm9b1ieX/Hn1FonbYUPgLgkyLyArLuI0G2QdqveytZhWbLx+81qjJFii5e27S+jcsZjnS6UBFurxksMUXmgLvxFhetH5uylK0SW3R99zx7BLddNxRNC61Mp5XCR72WIrAqdpEy9c33elNUMcORThciwp2tz7uTeymmyBxwM97ialzCtiy2qxqkMj5XWimIyDmq+iaAf62oPN7Z9PeV7SIVy4cbYv5C6qqOKl1x0aKNrW/bxXiLq9aPi7J0c31jGuMBZm8pfAvANciyjhRZt1GLArjYU7m8MEUUph2qUlg7JoUZvTEJFVW64KJFG+P90msE7bL102tZrljej3ueeLHweJGYsq9aSgeaVfWa/O+lqnpx/nfrT1IVAmCOKFr9fSmuHTOyYgDrVw60xxBau275vqFiXz3VxNV6Tr7X2ym6vi72RU55nSDA7rqE+D01rUBrOh7j+mKdTl57TFWvnO1Y7MoiilTXjhnbN4mdE5PtMYSTqtg5MYnhixZUNtM5huimUzFFlSa+W7Sp9G3PZHtdQvye1mH+wmxjCmchWyL7fBE5F6e6j84BkNxd1e2OaTH1980UIpsktgwWG7H1qRcpa9GuXzlQuBezz50ATa9RdV94Cpk9tvdXqF0cy8zWUvg9ALcAuBDZuEKrUngTwFc9lsuLbndMi/mLLkSkEWN006kY+9RnKhs7KGoVmiJlFzsBmqLz8YOvTXvPKlqL3bT0q+Zq1VOfuzjOZrYxhTtUdSmAz80YU7hEVZOrFEz9qT53TPMtRH9qTH24tlLoU7cdOzCNibm4r03R+Y69hyr/nUnhvrO9v2L8TupoTEFV/0JEPgTggwDOmnL8G74K5ottbnHsQkS+KUTbZWKJKk262Umt6JxuMdzXNrPdbXcA8/k7k8p9Z3t/xfad1OlA8x8DuBxZpfAIsslsPwCQXKVQJIV+ZpMQ4x4pjLWkwmYs6/ZHn7O6T13Mdi+bw1P02j5/Z5p034X8Tup0RvMnAFwCYJ+q3iQiFwC4x1+xqpVKBGISIvKNPdpOQTczlG3uUxez3U2/G6Eyfppy34X8Tuq0Uvh/qvquiJwQkXMAvApgscdyVapJEQjFwzaLy/Y+Nc12nz9vLtZs293zTl/DFy2o3e9MiIyf2DIfZ915TbIdde4C8FkAN+R//18AT6nqTd5LWMLVzmtEIYTYkc12x78madLuhj3tvKaqKiKXqepRAH8lIn8P4BxVfdp1QVMS23olNmzKHkuOepmYymKjmxz1bj6PqcffeucEXn/r+LT3S2WOiW+c85PptPvoSRH5DVX9kaoe8FmgFKQ8o9em7DHlqJuk/FnY5qibrvtsn8fU67B0dFdhWVLItPONc34ynW6yswrA/xaRn4nI0yKyX0SSbCm4WLOnm/VKYlkryKbsMeWom8S4dkynbHPUTdfd5vNIIdc/FN/XJvZ1m1o6bSms81qKiriKKm1r95iiWZuyx5SjbhJjpGXDZn6BbTaRacewlDPtfPJ5bVJYt6mlo5aCqh4s+tPLG4tIn4jsE5Hv5I+XisheEXleRO4TkTN7ef0irqJK29o9VDTba2RSlv/e6Wv41k2kFUurzcR21zybzyOFGd2h+Lw2tis0x7yfgk+fBvAMssX1AODLAL6iqveKyF8BuBnAnS7f0FVUaRtRxLRLl01kYlobfvXF5+LJF9+IIrqpw/r1M5XNI5g5s7mbOQNNyfXvhq9rk8K6TS2djik4JSKLAFyNLNW1lfa6FsAD+VPuBjDi+n3Lokqb6NE2ogjRb+giMjGtAX/gF8eiiW5crl8fSwvCtI/H1H0+pl73rSND0XweVCzGsQOTUC2FPwPwRwDelz8+D8BRVT2RPz4Mw9LcIrIJwCYAGBwctHpTlysS2tTuMe3SZROZpBDduFqn3uV+3L0qu19M1z2Wz4OKpTSWU3lLQUSuAfCqqk508/9VdbuqDqvqcH9/cReBSagVCUP047qITFKIbmzL6GL3Mt/Y718/KX2mIVoKawB8XEQ+hmzF1XMA3AFgvojMyVsLiwB4abuHWpGw6kjORWSSQnTjav16293LfGPkXz+pfKaVtxRU9VZVXaSqS5Atm7FbVX8XwB5kC+8BwEYAD1VVphQiYlsuIpMUohtX69ensB83URVmXfvI65uLXI5sA59rRORiAPcCWABgH4AbVfXtsv/vau2jUOuPUDx4D1CT9LT2kU+q+j0A38v//XMAl4UoB1dJpVTugS1j+6ft0bxh1WJsHRkKXSyqkaAthV5xlVRqki1j+wvnZNy4epAVA1mJtqVAlHLkW/XqrDv2HjIeT+GapbqarUndzqeFlQIFMzPyPanafhz7l1yImdEudlILJYWZ5Dbqdj5TBZnRTOVimVnrW1nkG7sQ61mZ1jiyXSsphJRXsy1St/OZipVCZFoRyOTRY1CcikDqWDGkHPmGWM9qw6riHXBNx2OS+mq2M9XtfKZipRCZOkcgM6Uc+YaY27J1ZAg3rh5sX58+kWQGmes2F6hu5zMVK4XI1DkCmSnlyHfzumWYN7dv2rEqZntvHRnCz277GA5suxo/u+1jSVQIQLjr5UvdzmcqDjRHpmzf3rppfaHZZB/FkvFhO68hlnKHkso8kE7V7Xym4jyFyHBmrVmq1ybVclN9lc1TYPdRZFJYbygUV+MtVWd3NWmciNLH7qMIpbKaYtVcjLeEyC9v0jgRpY8tBUqGi4yPEFF7nTNVqH5YKVAyXGR8hIja65ypQvXD7qOKND37xAUXGR8hsrtSyVThPUoAs48qweyTePCzKMbr0ixcJTWwsn5s/sJVK1TU7jsK7/X1eY9SCyuFCjD7JC5VZ3f5znhy8fq8R6mFA80VYPaJvTqtFOs748nF6/MepRZWChVg9omduq0U6zsKd/H6vEephZVCBThL2U7dZgD7jsLLXr/TFhfvUWrhmEJFOEu5c0Upo2XHY7d53bLCzB5XUbjp9a9Y3m811sB7lAC2FChCKe+zUMR3FG56/T3PHqlVi4uqwZYCRSflHdlMfEfhRa//mfueKnwuM4qoDFsKFJ0BQx+56TgVY0YRdYOVAkWHmTBu+L6OdUobplPYfUTRSWWtoNj5vI4hliCnanDtI6oEF1urlzXbdhdmgw3Mn4fHR9cGKBHZ4NpHFBSjyvrhshj1xTEF8q5uk9GIg9h1xkqBvKvbZDRiMkCdsfuIvOsTKZxjkOpkNGIyQJ1VXimIyGIA3wBwAQAFsF1V7xCRBQDuA7AEwAEA16vq61WXj9yr42Q04rIYdRWipXACwGdV9UkReR+ACRH5LoBPAnhMVbeJyCiAUQCfD1A+cmzAsAVmKpPRbDKnmGVFqat8TEFVX1LVJ/N//yuAZwAMALgWwN350+4GMFJ12ciPlPufbZbxrtuS39RMQQeaRWQJgBUA9gK4QFVfyn/0MrLuJaqBlJdltsmcYpYV1UGwgWYReS+AnQBuUdU3Zcqgo6qqiBR2OIvIJgCbAGBwcLCKopIDqfY/2+TjM3ef6iBIS0FE5iKrEL6pqg/mh18RkYX5zxcCeLXo/6rqdlUdVtXh/v7+agpMp2nKujc2+fjM3ac6qLxSkKxJ8DUAz6jqn0750cMANub/3gjgoarLRp1pUt+5zXhIymMnRC0huo/WAPhPAPaLSGvB9y8A2AbgfhG5GcBBANcHKBt1oKzvPMUuohZT5tD4wdewY+8hnFRFnwjWr8y6woqef9t1Q8w+oqRVXimo6g8AmGYtXVllWag7dew7N63PNH7wNeycmGzPqTipip0TWYto58Tkac+/7bohLghHSeOMZrJ2oWHeQcp956bWT6uF0OlxV62lLWP7p7VONqxajK0jQz2/LtFsuPYRWatj37mplWM7G9tFa2nL2H7c88SL01on9zzxIraM7e/5tYlmw5YCWavjujem1k/Zuk1Fx21bS0XjEjv2Hip87o69hxrTWmBLKRxWCtSVVOcdmGxet2zamAKQtX7WrxyYNnYw23Gb1pJpHKPpa0W1WkotrZYSAFYMFWClQITy1s/wRQusjnfKNI5hUraqbIg1l3y9Z1lLqddrHlIq62JxO06iQJaO7oLNb9+NqwcLI+WZLQ4ga7X4XErE53suGd1l/Nm8uX2VnqcrIT6jMmXbcXKgmZLiYiZ1LLOxTeMPA/Pn4cbVg+2WQZ+IsUIA3K25ZHNdfK7zVNYiSnVtqZTWxWL3ESXDxV7PMe0XbRrHaHUrdNp/7mLeiO118TlXZcOqxdPGFGaTwvyYlOb2sKUQoVgi2di4iLZiithcrR7rYs0l2+vic52nrSNDhS0l0/4bKcyPSWldLLYUIhNTJBsbF9FWbBGbiyyushZHp2yvi4v3LLN1ZOi0lpKpXz6F+TG+r5dLbClEJqZINjYuoq2UIrZOuWhx2F6XEHtkjKwYwPqVA9NaEK11qGKX0p4ibClEJrZINiYuoq2UIjYbvbY4urkuVc9VGds3WbgO1fBFC6L8cp0plbk9rBRyseQQ13FdoW6UfR69fE51nI3tQgrXpa6r88aGlQLi6sevayRrY7bPo9fPJJWIrWqxXxe2oqvBMQXE1Y+fUt+jLzF9HqEwA+10dRwPihFbCogvAok9YvMtts+jajG1XGPCVnQ12FIAIxCXXES4Tf882FIqxlZ0NdhSACMQV1xFuE3/PJreUirT9FZ0FVgpII3MixS4yg5J+fNwkcXGDDQKiZVCjhFI71xGuCl+HmwpUR2wUphFLPMXUtBNhFun68uWEtUBK4USzAKxYxvh1u36Nr2lRPXASiFXFLFyBqUd2wi3bte36S2lbvEaxIWVAswRq2lrxKJffMrYRLh1y7JpekupG7wG8WGlALd75VLn6pZlU9eWks9I3tU1YGvDHVYKsI9MTya8r3VM6phlU7eWku9IPsSucVSOM5phjkxNLQLTDlBkp+kzVFOYue17dnWIXeO60aS1qFgpIItY583tm3Zs3tw+bFi1uPB4ypFsbEZWDODx0bV4YdvVeHx0bWMqBMB838V0f/luzbi4Br7L2GqJTB49BsWplkhdKwZWCjBHrFtHhhodyZJfKbSUfE0BH/AAAAadSURBVLdmQuwaZ6tpa1GJJtw/Pjw8rOPj46GLQVRbpn2RY6q8fJdx6eguFH1LCoAXtl3d8+uHICITqjpc9DMONFMlXGWHMMukWinMrvZdxrplyc2GLQXyzlUkl0LUSvVTx/uurKUQ1ZiCiFwlIs+JyPMiMhq6POSGqz7ZpvXtUhxSGPtxKZruIxHpA/CXAP4dgMMAfiQiD6vqT8OWjHrlKjskhbx+qqcmrUUVU0vhMgDPq+rPVfUdAPcCuDZwmcgBV9khKeT1E6UupkphAMChKY8P58emEZFNIjIuIuNHjhyprHDUPVf5+Cnk9ROlLpruo06p6nYA24FsoDlwcagDrrJDUsiEIUpdTJXCJIDFUx4vyo9RDbjqk21S3y5RCDF1H/0IwAdEZKmInAngBgAPBy4TEVGjRNNSUNUTIvIHAB4F0Afg66r6k8DFIiJqlGgqBQBQ1UcAPBK6HERETRVT9xEREQXGSoGIiNqSXvtIRI4AOFjR250P4F8qeq/QmnKuTTlPoDnn2pTzBHo714tUtb/oB0lXClUSkXHTAlJ105Rzbcp5As0516acJ+DvXNl9REREbawUiIiojZVC57aHLkCFmnKuTTlPoDnn2pTzBDydK8cUiIiojS0FIiJqY6VARERtrBRmEJHFIrJHRH4qIj8RkU/nxxeIyHdF5J/yv88NXdZeichZIvJDEfk/+bn+SX58qYjszbdFvS9foDB5ItInIvtE5Dv547qe5wER2S8iT4nIeH6sdvcvAIjIfBF5QESeFZFnROQ363auIrIs/yxbf94UkVt8nScrhdOdAPBZVf0ggNUAfl9EPghgFMBjqvoBAI/lj1P3NoC1qnoJgEsBXCUiqwF8GcBXVPXXALwO4OaAZXTp0wCemfK4rucJAFeo6qVT8tjreP8CwB0A/l5VlwO4BNnnW6tzVdXn8s/yUgArAbwF4G/h6zxVlX9K/gB4CNm+0c8BWJgfWwjgudBlc3yeZwN4EsAqZLMk5+THfxPAo6HL5+D8FuW/OGsBfAeA1PE883M5AOD8Gcdqd/8CeD+AF5AnzNT5XKec278H8LjP82RLoYSILAGwAsBeABeo6kv5j14GcEGgYjmVd6k8BeBVAN8F8DMAR1X1RP6Uwm1RE/RnAP4IwLv54/NQz/MEAAXwDyIyISKb8mN1vH+XAjgC4K/zbsG7ROQ9qOe5ttwAYEf+by/nyUrBQETeC2AngFtU9c2pP9Osaq5FLq+qntSsWboIwGUAlgcuknMicg2AV1V1InRZKvIRVf0wgI8i6/78rak/rNH9OwfAhwHcqaorAPwSM7pQanSuyMe8Pg7g2zN/5vI8WSkUEJG5yCqEb6rqg/nhV0RkYf7zhcgi69pQ1aMA9iDrRpkvIq29NuqwLeoaAB8XkQMA7kXWhXQH6neeAABVncz/fhVZ3/NlqOf9exjAYVXdmz9+AFklUcdzBbJK/klVfSV/7OU8WSnMICIC4GsAnlHVP53yo4cBbMz/vRHZWEPSRKRfRObn/56HbOzkGWSVwyfypyV/rqp6q6ouUtUlyJrfu1X1d1Gz8wQAEXmPiLyv9W9kfdA/Rg3vX1V9GcAhEVmWH7oSwE9Rw3PNbcCpriPA03lyRvMMIvIRAN8HsB+n+p+/gGxc4X4Ag8iW675eVV8LUkhHROTXAdyNbPvTMwDcr6pfEpGLkUXUCwDsA3Cjqr4drqTuiMjlAD6nqtfU8Tzzc/rb/OEcAN9S1f8mIuehZvcvAIjIpQDuAnAmgJ8DuAn5vYwanWtewb8I4GJVfSM/5uUzZaVARERt7D4iIqI2VgpERNTGSoGIiNpYKRARURsrBSIiamOlQOSRiHxcRJJekI2ahSmpRETUxpYCkSURWSIiP57y+HMi8kUR+cN8H46nReTe/GefFJGv5v/+nXz/hn0i8o8iUqeF2qgm5sz+FCLq0CiApar6dmv5kBl+AGC1qqqIfArZqq2frbSERLNgpUDkztMAvikiYwDGCn6+CMB9+eJlZyLbC4AoKuw+IrJ3AtN/d87K/74awF8iW6nzR1NWYG35CwBfVdUhAL835f8RRYOVApG9VwD8GxE5T0R+BcA1yH6XFqvqHgCfR7Yr2Htn/L/349Ty3BtBFCF2HxFZUtXjIvIlAD9E9iX/LLKVZu8Rkfcj2+rzz1X1aLYSe9sXAXxbRF4HsBvZzmFEUWFKKhERtbH7iIiI2lgpEBFRGysFIiJqY6VARERtrBSIiKiNlQIREbWxUiAiorb/D8EhLGjxLfAIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AdrXwxE3-fd8",
        "outputId": "4c3e6073-ffea-4a1c-8d57-6b179c8c55dc"
      },
      "source": [
        "pendapatan = dataset.iloc[:,4]\n",
        "pendapatan"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      29\n",
              "1      30\n",
              "2      30\n",
              "3      33\n",
              "4      33\n",
              "       ..\n",
              "195    87\n",
              "196    87\n",
              "197    87\n",
              "198    88\n",
              "199    88\n",
              "Name: Pendapatan (juta Rp), Length: 200, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "MPwar0qC_p5Z",
        "outputId": "00be1216-4daa-4783-e7e9-be99d1920f88"
      },
      "source": [
        "plt.scatter(usia,pendapatan)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fc2392f1a50>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VCKRhrke_u9U"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}