LoanPrediction_Supervised.ipynb

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    "colab": {
      "name": "LoanPrediction-Supervised.ipynb",
      "provenance": [],
      "mount_file_id": "1GTDv56QU6ebcovUne0wgLnmZb3PqmcCr",
      "authorship_tag": "ABX9TyPmWe/3+YDBj5xdDO/cLCRU",
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      "name": "python3",
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      "cell_type": "markdown",
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        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/samirsharma-github/CodeBook/blob/master/LoanPrediction_Supervised.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TKqNiJcRJN31",
        "outputId": "fd0c62d1-6656-4f52-c57c-0ea7a8880d2a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "7vhMfM2IHugJ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=pd.read_csv(\"train.csv\")"
      ],
      "metadata": {
        "id": "mw52IynlH95I"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()\n"
      ],
      "metadata": {
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          "height": 267
        },
        "id": "ZdwPQrjAJm_G",
        "outputId": "0277af27-5861-404d-91d4-640596a97b19"
      },
      "execution_count": 7,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
              "0  LP001002   Male      No          0      Graduate            No   \n",
              "1  LP001003   Male     Yes          1      Graduate            No   \n",
              "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
              "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
              "4  LP001008   Male      No          0      Graduate            No   \n",
              "\n",
              "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "0             5849                0.0         NaN             360.0   \n",
              "1             4583             1508.0       128.0             360.0   \n",
              "2             3000                0.0        66.0             360.0   \n",
              "3             2583             2358.0       120.0             360.0   \n",
              "4             6000                0.0       141.0             360.0   \n",
              "\n",
              "   Credit_History Property_Area Loan_Status  \n",
              "0             1.0         Urban           Y  \n",
              "1             1.0         Rural           N  \n",
              "2             1.0         Urban           Y  \n",
              "3             1.0         Urban           Y  \n",
              "4             1.0         Urban           Y  "
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              "      <th>Loan_ID</th>\n",
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              "      <td>LP001005</td>\n",
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              "      <td>LP001006</td>\n",
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              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
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              "      <td>Graduate</td>\n",
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              "      <td>Urban</td>\n",
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            "text/plain": [
              "(614, 13)"
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          "execution_count": 8
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 614 entries, 0 to 613\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Loan_ID            614 non-null    object \n",
            " 1   Gender             601 non-null    object \n",
            " 2   Married            611 non-null    object \n",
            " 3   Dependents         599 non-null    object \n",
            " 4   Education          614 non-null    object \n",
            " 5   Self_Employed      582 non-null    object \n",
            " 6   ApplicantIncome    614 non-null    int64  \n",
            " 7   CoapplicantIncome  614 non-null    float64\n",
            " 8   LoanAmount         592 non-null    float64\n",
            " 9   Loan_Amount_Term   600 non-null    float64\n",
            " 10  Credit_History     564 non-null    float64\n",
            " 11  Property_Area      614 non-null    object \n",
            " 12  Loan_Status        614 non-null    object \n",
            "dtypes: float64(4), int64(1), object(8)\n",
            "memory usage: 62.5+ KB\n"
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      "execution_count": 11,
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          "data": {
            "text/plain": [
              "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "count       614.000000         614.000000  592.000000         600.00000   \n",
              "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
              "std        6109.041673        2926.248369   85.587325          65.12041   \n",
              "min         150.000000           0.000000    9.000000          12.00000   \n",
              "25%        2877.500000           0.000000  100.000000         360.00000   \n",
              "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
              "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
              "max       81000.000000       41667.000000  700.000000         480.00000   \n",
              "\n",
              "       Credit_History  \n",
              "count      564.000000  \n",
              "mean         0.842199  \n",
              "std          0.364878  \n",
              "min          0.000000  \n",
              "25%          1.000000  \n",
              "50%          1.000000  \n",
              "75%          1.000000  \n",
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              "      <th>CoapplicantIncome</th>\n",
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              "      <th>mean</th>\n",
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              "      <th>50%</th>\n",
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              "    }\n",
              "\n",
              "    .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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert: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",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c4627257-4785-4553-8d6f-d7c8fef5505f 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-c4627257-4785-4553-8d6f-d7c8fef5505f');\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",
              "            '<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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(dataset['Credit_History'],dataset['Loan_Status'],margins=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "1XIMVFoCKIv8",
        "outputId": "5a55d0c8-4eba-4277-b9a9-d6eb1db93d1a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_Status       N    Y  All\n",
              "Credit_History               \n",
              "0.0              82    7   89\n",
              "1.0              97  378  475\n",
              "All             179  385  564"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-28ba190a-1269-4bee-8197-0774b5c6ed0f\">\n",
              "    <div 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",
              "    }\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>Loan_Status</th>\n",
              "      <th>N</th>\n",
              "      <th>Y</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Credit_History</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>82</td>\n",
              "      <td>7</td>\n",
              "      <td>89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>97</td>\n",
              "      <td>378</td>\n",
              "      <td>475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>179</td>\n",
              "      <td>385</td>\n",
              "      <td>564</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-28ba190a-1269-4bee-8197-0774b5c6ed0f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\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-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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert: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",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-28ba190a-1269-4bee-8197-0774b5c6ed0f 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-28ba190a-1269-4bee-8197-0774b5c6ed0f');\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",
              "            '<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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.boxplot(column='ApplicantIncome')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "31YTD6LeK1ot",
        "outputId": "4a22c09f-be63-468b-e4b3-e70af5ff2b82"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdc413c9c90>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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b0gCwl9IfdyJil6T7gGeAEeCGiBgFkPR5oBdoANZHxK7jaJfZjDvnnHP4wQ9+wEMPPXRYN5FZPZhSMoiIAlBI5ecozQQaX+e3wCePcHwPcNj0ioh4EHhwKm0xm23cTWT17HjuDMwseemll9iwYQNr166lWCySyWT46le/ynXXXVfrpplVxD9HYVYFmUyGRYsW8fTTT7N161aefvppFi1aRCaTqXXTzCriZGBWBd3d3WSzWfr6+hgZGaGvr49sNkt3d3etm2ZWEXcTmVXB2E9OlHcT9fT0+KcorG6oXge5WlpaYseOHbVuhtlhCoWCHzSzWUnSzohomWifu4nMzMzJwMzMnAzMqsbrGVg98wCyWRV4PQOrd74zMKsCr2dg9c7JwKwKisUira2th8RaW1spFos1apHZ1DgZmFVBJpOhv7//kFh/f7+fQLa64WRgVgV+AtnqnQeQzarATyBbvfMTyGZV5ieQbbbyE8hmZnZUkyYDSfMlPSbpZ5J2SfqLFF8q6VFJA5K+k5asJC1r+Z0Uf1TSkrJzfSnFn5V0WVl8RYoNSLq5+h/TzMyOppI7g3eASyLiY8D5wApJy4GvAHdExAeB14Fsqp8FXk/xO1I9JJ1HaQnMjwIrgG9KapDUAHwDWAmcB3SlumZmNkMmTQZRMpw256ZXAJcA96f4RuCKVF6Vtkn7OyQpxTdFxDsR8TwwQGnZzIuAgYh4LiL2A5tSXTMzmyEVzSZK/3rfCXyQ0r/i/wF4IyJGUpVBYGEqLwT2AETEiKR9wFkpvq3stOXH7BkXv/gI7VgDrAFoamqiUChU0nyzGTU8POzvptWdipJBRIwC50s6Hfg+8JFpbdWR27EOWAel2USesWGzkWcTWT2a0myiiHgD6AP+ADhd0lgyWQQMpfIQsBgg7T8N+FV5fNwxR4qbmdkMqWQ20fvTHQGSTgIuBYqUksJVqdpq4IFU3py2SfsfidLDDJuBq9Nso6XAMuAxYDuwLM1OmkdpkHlzNT6cmZlVppJuogXAxjRuMAe4LyJ+KOkZYJOkvwQeB3Kpfg74tqQBYC+lP+5ExC5J9wHPACPADan7CUmfB3qBBmB9ROyq2ic0M7NJTZoMIuJJ4F9OEH+O0kyg8fHfAp88wrl6gMN+0zciHgQerKC9ZmY2DfwEspmZORmYmZmTgZmZ4WRgZmY4GZiZGU4GZmaGk4FZ1eTzeZqbm+no6KC5uZl8Pl/rJplVzMtemlVBPp+nu7ubXC7H6OgoDQ0NZLOlX3X30pdWD3xnYFYFPT095HI52tvbaWxspL29nVwuR0/PYc9Yms1KTgZmVVAsFmltbT0k1traSrFYrFGLzKbGycCsCjKZDP39/YfE+vv7yWQyNWqR2dQ4GZhVQXd3N9lslr6+PkZGRujr6yObzdLd3V3rpplVxAPIZlUwNki8du1aisUimUyGnp4eDx5b3fCdgZmZORmYVUM+n+fGG2/krbfeIiJ46623uPHGG/2sgdWNSlY6WyypT9IzknZJujHFz5S0RdLu9H5GikvSnZIGJD0p6YKyc61O9XdLWl0Wv1DSU+mYOyVpOj6s2XS56aabGB4eZmhoiIhgaGiI4eFhbrrpplo3zawildwZjAB/HhHnAcuBGySdB9wMbI2IZcDWtA2wktKSlsuANcBdUEoewC3AxZQWxbllLIGkOp8tO27F8X80s5kzODjI22+/zVlnncWcOXM466yzePvttxkcHKx108wqMmkyiIiXI+KnqfxrSusfLwRWARtTtY3AFam8CrgnSrYBp0taAFwGbImIvRHxOrAFWJH2nRoR29JayfeUncusbsyfP5/58+cfVjarB1OaTSRpCaUlMB8FmiLi5bTrFaAplRcCe8oOG0yxo8UHJ4hPdP01lO42aGpqolAoTKX5ZtPqnXfeYeXKlVxyySU88sgjfOtb3wLw99TqQsXJQNLJwHeBL0TEm+Xd+hERkmIa2neIiFgHrANoaWmJtra26b6kWcUaGxu5++67ueuuu5g7dy6NjY0cOHAAf0+tHlQ0m0jSXEqJ4N6I+F4Kv5q6eEjvr6X4ELC47PBFKXa0+KIJ4mZ15cCBA4yOjgIwOjrKgQMHatwis8pVMptIQA4oRsT/KNu1GRibEbQaeKAsfm2aVbQc2Je6k3qBTklnpIHjTqA37XtT0vJ0rWvLzmVWFxoaGgA4ePDgIe9jcbPZrpJuok8A/xZ4StITKfZl4HbgPklZ4AXgU2nfg8DlwADwG+AzABGxV9JtwPZU79aI2JvKnwM2ACcBD6WXWd0YHR1FEnPmzHn3J6wPHjz47p2C2Wyn0gSe+tPS0hI7duyodTPMAJDEvHnziAgOHDjA3LlzkcT+/fup1//H7J8eSTsjomWiff5tIrMq2b9//7tljxdYvfHPUZiZmZOBWTXNmTPnkHezeuFvrFkVnX322cyZM4ezzz671k0xmxKPGZhV0SuvvHLIu1m98J2BmZk5GZiZmZOBWdWMf9rYTx9bPXEyMKuSgwcPMnfuXADmzp377k9SmNUDDyCbVcnY08fgh86s/vjOwMzMnAzMzMzJwMzMcDIwMzOcDMzMDCcDMzOjsmUv10t6TdLTZbEzJW2RtDu9n5HiknSnpAFJT0q6oOyY1an+bkmry+IXSnoqHXNnWvrSrC7Nnz//kHezelHJncEGYMW42M3A1ohYBmxN2wArgWXptQa4C0rJA7gFuBi4CLhlLIGkOp8tO278tczqxtgCN+UL3ZjVg0mTQUT8BNg7LrwK2JjKG4EryuL3RMk24HRJC4DLgC0RsTciXge2ACvSvlMjYluU1ga8p+xcZjUnqaLXmLGnjsufPp7qOcxq4VifQG6KiJdT+RWgKZUXAnvK6g2m2NHigxPEJyRpDaU7DpqamigUCsfYfLPK9PX1VVSvvb39uM/h77PV0nH/HEVEhKQZWfE7ItYB6wBaWlqira1tJi5rNqnOzk4efvhh5syZw8GDB9997+zsxN9TqwfHOpvo1dTFQ3p/LcWHgMVl9Ral2NHiiyaIm9WV3t5eOjs7KfV2ln6nqLOzk97e3hq3zKwyx5oMNgNjM4JWAw+Uxa9Ns4qWA/tSd1Iv0CnpjDRw3An0pn1vSlqeZhFdW3Yus7rS29vLwYMH+cAXf8jBgwedCKyuTNpNJCkPtAHvkzRIaVbQ7cB9krLAC8CnUvUHgcuBAeA3wGcAImKvpNuA7anerRExNij9OUozlk4CHkovMzObQZMmg4joOsKujgnqBnDDEc6zHlg/QXwH0DxZO8zMbPr4CWQzM3MyMDMzJwMzM8PJwMzM8BrIdoL52F88zL63p3994iU3/2haz3/aSXP52S2d03oNO7E4GdgJZd/bB/jF7X88rdcoFArT/tTxdCcbO/G4m8jMzJwMzMzMycDMzPCYgZ1gTsnczO9vvHnyisdr4+RVjscpGYDpHfuwE4uTgZ1Qfl283QPIZhNwMrATzoz8If3x9E8tNasmJwM7oUz3XQGUks1MXMesmjyAbGZmTgZmZjaLkoGkFZKelTQgaQame5iZ2ZhZkQwkNQDfAFYC5wFdks6rbavMzE4csyIZABcBAxHxXETsBzYBq2rcJjOzE8ZsmU20ENhTtj0IXDy+kqQ1wBqApqYmCoXCjDTOTlzt7e3HdJy+MvVj+vr6julaZtUwW5JBRSJiHbAOoKWlJab7wR6z0rLeUzMTD52ZVdts6SYaAhaXbS9KMTMzmwGzJRlsB5ZJWippHnA1sLnGbTIzO2HMim6iiBiR9HmgF2gA1kfErho3y8zshDErkgFARDwIPFjrdpiZnYhmSzeRmZnVkJOBmZk5GZiZmZOBmZkBOpaHamYDSb8EXqh1O8wm8D7gH2vdCLMJfCAi3j/RjrpNBmazlaQdEdFS63aYTYW7iczMzMnAzMycDMymw7paN8BsqjxmYGZmvjMwMzMnAzMzw8nA6pCkKySFpI8cxzk2SLoqle+u9prbkr48bnu4muc3qzYnA6tHXUB/ej9uEfHvIuKZapyrzJcnr2I2ezgZWF2RdDLQCmQpLYKEpDZJP5H0I0nPSvqWpDlp37CkOyTtkrRV0mFPX0oqSGpJ5RWSfirpZ5K2pthFkv5e0uOS/k7Sh1P8Oknfk/RjSbslfTXFbwdOkvSEpHvHXastXe9+ST+XdK8kpX0fT+f/maTHJJ0iab6kv5b0VLp+e9m1fyBpi6RfSPq8pP+c6myTdGaq989T+3ZK+tvjuZuyf+Iiwi+/6uYFXAPkUvnvgAuBNuC3wD+jtDjSFuCqVCeAa1L5vwJfT+UNZXUKQAvwfmAPsDTFz0zvpwKNqfxHwHdT+TrgOeA0YD6ln0dZnPYNj2v3cHpvA/ZRWtp1DvD3lJLbvHSuj5dfE/hzSos9AXwEeDFd6zpgADgltXsf8B9SvTuAL6TyVmBZKl8MPFLr/4Z+zc7XrFncxqxCXcDXUnlT2v4h8FhEPAcgKU/pD+z9wEHgO6n+/wa+d5RzLwd+EhHPA0TE3hQ/DdgoaRml5DK37JitEbEvXfcZ4AOUEsrRPBYRg+mYJ4AllP6YvxwR29O130z7W4G/SrGfS3oB+FA6T19E/Br4taR9wP9N8aeAf5Huov4V8Dfp5gPgPZO0zU5QTgZWN1LXxyXA70sKSncBAfwovZc70gM0x/JgzW2U/vBeKWkJpTuJMe+UlUep7P+pYzlmsvMcLNs+mM45B3gjIs4/xvPbCcRjBlZPrgK+HREfiIglEbEYeB7418BFkpamsYI/ozTADKXv+FWp/G/K4hPZBvyhpKXwbvKB0p3BUCpfV2FbD0iaO3m1dz0LLJD08XTtUyQ1An9LqWsMSR8Czk11J5XuLp6X9Ml0vCR9bAptshOIk4HVky7g++Ni303x7cDXgSKlBDFW7y1KieJpSncVtx7p5BHxS2AN8D1JP+N33UtfBf6bpMep/F/x64Anxw8gH+Xa+yklsb9K195CaWzgm8AcSU+l9lwXEe8c+UyHuQbIpnPuAlZN4Vg7gfjnKKzuSWoD/ktE/MkE+4Yj4uSZb5VZffGdgZmZ+c7AzMx8Z2BmZjgZmJkZTgZmZoaTgZmZ4WRgZmbA/we7cBvtIytKJgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['ApplicantIncome'].hist(bins=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "UTY_1STtLniY",
        "outputId": "d5be73b3-2a52-4392-ce95-a20b5038bb0a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdc40e88410>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['CoapplicantIncome'].hist(bins=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "BtmnNc5sLxRP",
        "outputId": "f6b27328-6bdb-4fa6-d17e-f981369b8b15"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdc40d79a50>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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5MnAf8Fb6l6Ve+WLj4LG9Wo+2/jTgRxx7/cbF24B3RcQe+pdMLgG+wLjVZ6VvWiziZsda+je6zuVXNyXeuNLzWobjnuTwG6p/xeE3Cz/X2ldw+M3CB1v/GcD36N8oPL21z2jr5t4s3LTSx3uMtQn618E/P6ffGh1ej7OBda19CvBvwDuBf+TwG4Yfbu1rOPyG4R2t/UYOv2H4HP2bhWXem8A0v7qhOlb1WfHiLbLwm+h/IuJZ4NMrPZ9lON6vAvuB/6V/nW4r/Wt79wLPAP86EEJB/3+a8iywG5gaeJ0/pn9zZwb40ED/FPB42+ZvaN9gHpcH8Af0L7k8BjzaHpus0a/V6XeBR1qdHgf+vPW/nv4fr5kWZCe1/pPb8kxb//qB1/p0q8XTDHxyqMp7c064j1V9/PkBSSponK+5S5LmYbhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQV9P+1JmkrEXxnigAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.boxplot(column='ApplicantIncome',by='Education')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "Z9XCkQkJL3qw",
        "outputId": "eb6c8026-5981-4c67-8cde-6f739fad2002"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdc40c90050>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.boxplot(column='LoanAmount')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "lNm2YxrLMAA6",
        "outputId": "0fd914af-de52-4b45-c54c-2e196b4e4cb1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdc40ba8250>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['LoanAmount'].hist(bins=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "6UxFGHPsMJuz",
        "outputId": "0e8ee23b-ff01-49fc-af97-81f9f5153ff4"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdc40b72910>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['LoanAmount_log']=np.log(dataset['LoanAmount'])\n",
        "dataset['LoanAmount_log'].hist(bins=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "MdJU0IgsMQAY",
        "outputId": "4d0f75fe-36ed-415b-8cb5-37ad4a7ae797"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdc4086dc10>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAARdUlEQVR4nO3dbYxcZ3nG8f9FzIuJaQyEriLbqqMSpaJxC2GVglKhcdJSQxDOB4SCUnBoKqtqoGlxBYZ+QP2AGlQFGqQWyU0oRk1j0gBKRHiLQqYIqQ7Y4cV5gWIFA7acGEQIbIigpnc/7EFaeTexd2Z2Z/bx/yetds5zzpxz69bM5eNnzpxNVSFJasszxl2AJGn0DHdJapDhLkkNMtwlqUGGuyQ1aNW4CwA4++yza+PGjTzxxBOceeaZ4y5n4tiX+ezJwuzLwlrty/79+39UVS9aaN1EhPvGjRvZt28f/X6fXq837nImjn2Zz54szL4srNW+JPneU61zWkaSGnTScE/ykSTHkty/wLodSSrJ2d1yknwoycEk30xy4VIULUl6eqdy5v5RYMuJg0k2AK8Gvj9n+DXAed3PduDDw5coSVqsk4Z7VX0J+PECqz4IvBOYe/+CrcDHatZeYG2Sc0ZSqSTplA30gWqSrcCRqvpGkrmr1gE/mLN8uBs7usA+tjN7ds/U1BT9fp+ZmRn6/f4gJTXNvsxnTxZmXxZ2OvZl0eGe5LnAe5idkhlYVe0CdgFMT09Xr9dr9hPtYdmX+ezJwuzLwk7Hvgxy5v7bwLnAr8/a1wP3JbkIOAJsmLPt+m5MkrSMFn0pZFUdqKrfrKqNVbWR2amXC6vqEeAO4C3dVTOvAB6vqnlTMpKkpXUql0LeAvw3cH6Sw0mufprNPwM8DBwE/hX4y5FUKUlalJNOy1TVm06yfuOcxwVcM3xZUhs27rxz4Oceuu6yEVai043fUJWkBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkho00B/rkLT0BrkvzY5Nx7lq553el0aeuUtSiwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoNOGu5JPpLkWJL754z9Y5JvJflmkk8lWTtn3buTHEzy7SR/slSFS5Ke2qmcuX8U2HLC2F3ABVX1e8D/AO8GSPIS4Argd7vn/EuSM0ZWrSTplJw03KvqS8CPTxj7QlUd7xb3Auu7x1uBPVX1i6r6LnAQuGiE9UqSTsEobhz2Z8DHu8frmA37Xzvcjc2TZDuwHWBqaop+v8/MzAz9fn8EJbXFvsy3UnqyY9Pxk280QlOrZ4+5EnqznFbK62WUhgr3JH8HHAduXuxzq2oXsAtgenq6er0e/X6fXq83TElNsi/zrZSeXDXAnR2HsWPTca4/sIpDV/aW9biTbqW8XkZp4HBPchXwOuDSqqpu+AiwYc5m67sxSdIyGuhSyCRbgHcCr6+qn89ZdQdwRZJnJzkXOA/4yvBlSpIW46Rn7kluAXrA2UkOA+9l9uqYZwN3JQHYW1V/UVUPJLkVeJDZ6ZprqupXS1W8JGlhJw33qnrTAsM3Pc327wPeN0xRkqTh+A1VSWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAadNNyTfCTJsST3zxl7QZK7knyn+/38bjxJPpTkYJJvJrlwKYuXJC3sVM7cPwpsOWFsJ3B3VZ0H3N0tA7wGOK/72Q58eDRlSpIW46ThXlVfAn58wvBWYHf3eDdw+Zzxj9WsvcDaJOeMqlhJ0qkZdM59qqqOdo8fAaa6x+uAH8zZ7nA3JklaRquG3UFVVZJa7POSbGd26oapqSn6/T4zMzP0+/1hS2qOfZlvpfRkx6bjy3q8qdWzx1wJvVlOK+X1MkqDhvujSc6pqqPdtMuxbvwIsGHOduu7sXmqahewC2B6erp6vR79fp9erzdgSe2yL/OtlJ5ctfPOZT3ejk3Huf7AKjjwxMD7OHTdZSOsaDKslNfLKA06LXMHsK17vA24fc74W7qrZl4BPD5n+kaStExOeuae5BagB5yd5DDwXuA64NYkVwPfA97Ybf4Z4LXAQeDnwFuXoGZJ0kmcNNyr6k1PserSBbYt4Jphi5IkDcdvqEpSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYNFe5J/ibJA0nuT3JLkuckOTfJvUkOJvl4kmeNqlhJ0qkZONyTrAP+CpiuqguAM4ArgPcDH6yqFwOPAVePolBJ0qkbdlpmFbA6ySrgucBR4BLgtm79buDyIY8hSVqkVNXgT06uBd4HPAl8AbgW2NudtZNkA/DZ7sz+xOduB7YDTE1NvXzPnj3MzMywZs2agetplX2Zb6X05MCRx5f1eFOr4dEnh9vHpnVnjaaYCbJSXi+LtXnz5v1VNb3QulWD7jTJ84GtwLnAT4D/BLac6vOrahewC2B6erp6vR79fp9erzdoSc2yL/OtlJ5ctfPOZT3ejk3Huf7AwG9rAA5d2RtNMRNkpbxeRmmYaZk/Ar5bVT+sqv8FPglcDKztpmkA1gNHhqxRkrRIw4T794FXJHlukgCXAg8C9wBv6LbZBtw+XImSpMUaONyr6l5mPzi9DzjQ7WsX8C7gHUkOAi8EbhpBnZKkRRhqcq6q3gu894Thh4GLhtmvJGk4fkNVkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNGuoPZEung4077xx3CdKiDXXmnmRtktuSfCvJQ0lemeQFSe5K8p3u9/NHVawk6dQMOy1zA/C5qvod4PeBh4CdwN1VdR5wd7csSVpGA4d7krOAVwE3AVTVL6vqJ8BWYHe32W7g8mGLlCQtTqpqsCcmLwV2AQ8ye9a+H7gWOFJVa7ttAjz26+UTnr8d2A4wNTX18j179jAzM8OaNWsGqqdl9mW+5ezJgSOPL8txRmFqNTz65HD72LTurNEUM0FafQ9t3rx5f1VNL7RumHCfBvYCF1fVvUluAH4KvH1umCd5rKqedt59enq69u3bR7/fp9frDVRPy+zLfMvZk5X0geqOTce5/sBw10kcuu6yEVUzOVp9DyV5ynAfZs79MHC4qu7tlm8DLgQeTXJOd+BzgGNDHEOSNICBw72qHgF+kOT8buhSZqdo7gC2dWPbgNuHqlCStGjDXuf+duDmJM8CHgbeyuw/GLcmuRr4HvDGIY8hSVqkocK9qr4OLDTfc+kw+5UkDcfbD0hSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1aOhwT3JGkq8l+XS3fG6Se5McTPLxJM8avkxJ0mKM4sz9WuChOcvvBz5YVS8GHgOuHsExJEmLMFS4J1kPXAbc2C0HuAS4rdtkN3D5MMeQJC1eqmrwJye3Af8APA/4W+AqYG931k6SDcBnq+qCBZ67HdgOMDU19fI9e/YwMzPDmjVrBq6nVfZlvuXsyYEjjy/LcUZhajU8+uRw+9i07qzRFDNBWn0Pbd68eX9VTS+0btWgO03yOuBYVe1P0lvs86tqF7ALYHp6unq9Hv1+n15v0btqnn2Zbzl7ctXOO5flOKOwY9Nxrj8w8NsagENX9kZTzAQ5Hd9Dw7wKLgZen+S1wHOA3wBuANYmWVVVx4H1wJHhy5QkLcbAc+5V9e6qWl9VG4ErgC9W1ZXAPcAbus22AbcPXaUkaVGW4jr3dwHvSHIQeCFw0xIcQ5L0NIabnOtUVR/od48fBi4axX4lSYPxG6qS1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQSO5FFKaZBtX0O0DJsGw/Tp03WUjqkTD8MxdkhpkuEtSgwx3SWqQ4S5JDfIDVUkjNcwHsn4YOzqeuUtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMGDvckG5Lck+TBJA8kubYbf0GSu5J8p/v9/NGVK0k6FcOcuR8HdlTVS4BXANckeQmwE7i7qs4D7u6WJUnLaOBwr6qjVXVf9/hnwEPAOmArsLvbbDdw+bBFSpIWZyRz7kk2Ai8D7gWmqupot+oRYGoUx5AknbpU1XA7SNYA/wW8r6o+meQnVbV2zvrHqmrevHuS7cB2gKmpqZfv2bOHmZkZ1qxZM1Q9LbIv8y2mJweOPL7E1UyOqdXw6JPjrmJwm9adtST7bfU9tHnz5v1VNb3QuqHCPckzgU8Dn6+qD3Rj3wZ6VXU0yTlAv6rOf7r9TE9P1759++j3+/R6vYHraZV9mW8xPTmd/obqjk3Huf7Ayr2T91Ld8rfV91CSpwz3Ya6WCXAT8NCvg71zB7Cte7wNuH3QY0iSBjPMP/EXA28GDiT5ejf2HuA64NYkVwPfA944XImSpMUaONyr6stAnmL1pYPuV1rIiVMrOzYd56rTaLpFWiy/oSpJDTLcJalBhrskNchwl6QGGe6S1KCV+20HSc0Z5gtnS/UFqJXKM3dJapBn7lo2p9NtAKRx88xdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchLISWd9oa9THcSv0DlmbskNcgzd0lNeLqz79Pxj7sY7pI0pEm8J47TMpLUIM/ctSjeH0ZaGQz3FajFT/YljZbTMpLUoCU7c0+yBbgBOAO4saquW6pjaXGcWpHatyThnuQM4J+BPwYOA19NckdVPTjqY41zimISPyGXJFi6aZmLgINV9XBV/RLYA2xdomNJkk6Qqhr9TpM3AFuq6s+75TcDf1BVb5uzzXZge7d4PvBt4GzgRyMvaOWzL/PZk4XZl4W12pffqqoXLbRibFfLVNUuYNfcsST7qmp6TCVNLPsynz1ZmH1Z2OnYl6WaljkCbJizvL4bkyQtg6UK968C5yU5N8mzgCuAO5boWJKkEyzJtExVHU/yNuDzzF4K+ZGqeuAUnrrr5JucluzLfPZkYfZlYaddX5bkA1VJ0nj5DVVJapDhLkkNGnu4J9mQ5J4kDyZ5IMm1465pEiR5TpKvJPlG15e/H3dNkyTJGUm+luTT465lUiQ5lORAkq8n2TfueiZFkrVJbkvyrSQPJXnluGtaDpNwV8jjwI6qui/J84D9Se5ailsVrDC/AC6pqpkkzwS+nOSzVbV33IVNiGuBh4DfGHchE2ZzVbX4ZZ1h3AB8rqre0F2999xxF7Qcxn7mXlVHq+q+7vHPmH3DrhtvVeNXs2a6xWd2P376DSRZD1wG3DjuWjTZkpwFvAq4CaCqfllVPxlvVctj7OE+V5KNwMuAe8dbyWToph6+DhwD7qoq+zLrn4B3Av837kImTAFfSLK/u72H4Fzgh8C/ddN4NyY5c9xFLYeJCfcka4BPAH9dVT8ddz2ToKp+VVUvZfYbvhcluWDcNY1bktcBx6pq/7hrmUB/WFUXAq8BrknyqnEXNAFWARcCH66qlwFPADvHW9LymIhw7+aUPwHcXFWfHHc9k6b7b+Q9wJZx1zIBLgZen+QQs3cbvSTJv4+3pMlQVUe638eATzF7d9bT3WHg8Jz/9d7GbNg3b+zhniTMzoc9VFUfGHc9kyLJi5Ks7R6vZvbe+N8ab1XjV1Xvrqr1VbWR2dtafLGq/nTMZY1dkjO7CxLoph1eDdw/3qrGr6oeAX6Q5Pxu6FLgtLhYYxKulrkYeDNwoJtfBnhPVX1mjDVNgnOA3d0fPnkGcGtVedmfnsoU8KnZcyVWAf9RVZ8bb0kT4+3Azd2VMg8Dbx1zPcvC2w9IUoPGPi0jSRo9w12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ16P8BSgwBfEMJAHkAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MvwCVH5TMeQg",
        "outputId": "6f27b82c-2b65-47b5-d658-cb65adc4f018"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID               0\n",
              "Gender               13\n",
              "Married               3\n",
              "Dependents           15\n",
              "Education             0\n",
              "Self_Employed        32\n",
              "ApplicantIncome       0\n",
              "CoapplicantIncome     0\n",
              "LoanAmount           22\n",
              "Loan_Amount_Term     14\n",
              "Credit_History       50\n",
              "Property_Area         0\n",
              "Loan_Status           0\n",
              "LoanAmount_log       22\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Gender'].fillna(dataset['Gender'].mode()[0],inplace=True)\n",
        "dataset['Married'].fillna(dataset['Married'].mode()[0],inplace=True)\n",
        "dataset['Dependents'].fillna(dataset['Dependents'].mode()[0],inplace=True)\n",
        "dataset['Self_Employed'].fillna(dataset['Self_Employed'].mode()[0],inplace=True)\n",
        "\n",
        "dataset.LoanAmount=dataset.LoanAmount.fillna(dataset.LoanAmount.mean())\n",
        "dataset.LoanAmount_log=dataset.LoanAmount_log.fillna(dataset.LoanAmount_log.mean())\n",
        "\n",
        "dataset['Loan_Amount_Term'].fillna(dataset['Loan_Amount_Term'].mode()[0],inplace=True)\n",
        "dataset['Credit_History'].fillna(dataset['Credit_History'].mode()[0],inplace=True)\n"
      ],
      "metadata": {
        "id": "y1UYbNgbMjDi"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4iVp7xp0Nxe2",
        "outputId": "92b792da-8ae7-4a68-8b2a-bb522418d43b"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID              0\n",
              "Gender               0\n",
              "Married              0\n",
              "Dependents           0\n",
              "Education            0\n",
              "Self_Employed        0\n",
              "ApplicantIncome      0\n",
              "CoapplicantIncome    0\n",
              "LoanAmount           0\n",
              "Loan_Amount_Term     0\n",
              "Credit_History       0\n",
              "Property_Area        0\n",
              "Loan_Status          0\n",
              "LoanAmount_log       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GMcKbv_UOBQJ",
        "outputId": "ce36e5bc-e4e6-44c3-f145-960adaa3287a"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(614, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['TotalIncome']=dataset[\"ApplicantIncome\"]+dataset[\"CoapplicantIncome\"]\n",
        "dataset['TotalIncome_log']=np.log(dataset['TotalIncome'])"
      ],
      "metadata": {
        "id": "UM6gfdIaOK49"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['TotalIncome_log'].hist(bins=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "2dozMKX4OqxD",
        "outputId": "1b71d3fc-4f9c-4c7a-a65b-2c2e32ca14a1"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdc40d657d0>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "NDwzDivbOxD7",
        "outputId": "4d38f08a-4f97-4268-849b-e6f033c0fdb7"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
              "0  LP001002   Male      No          0      Graduate            No   \n",
              "1  LP001003   Male     Yes          1      Graduate            No   \n",
              "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
              "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
              "4  LP001008   Male      No          0      Graduate            No   \n",
              "\n",
              "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "0             5849                0.0  146.412162             360.0   \n",
              "1             4583             1508.0  128.000000             360.0   \n",
              "2             3000                0.0   66.000000             360.0   \n",
              "3             2583             2358.0  120.000000             360.0   \n",
              "4             6000                0.0  141.000000             360.0   \n",
              "\n",
              "   Credit_History Property_Area Loan_Status  LoanAmount_log  TotalIncome  \\\n",
              "0             1.0         Urban           Y        4.857444       5849.0   \n",
              "1             1.0         Rural           N        4.852030       6091.0   \n",
              "2             1.0         Urban           Y        4.189655       3000.0   \n",
              "3             1.0         Urban           Y        4.787492       4941.0   \n",
              "4             1.0         Urban           Y        4.948760       6000.0   \n",
              "\n",
              "   TotalIncome_log  \n",
              "0         8.674026  \n",
              "1         8.714568  \n",
              "2         8.006368  \n",
              "3         8.505323  \n",
              "4         8.699515  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f796c19c-11b6-41b7-88b0-6bf60d626037\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
              "      <th>Self_Employed</th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Property_Area</th>\n",
              "      <th>Loan_Status</th>\n",
              "      <th>LoanAmount_log</th>\n",
              "      <th>TotalIncome</th>\n",
              "      <th>TotalIncome_log</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LP001002</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>5849</td>\n",
              "      <td>0.0</td>\n",
              "      <td>146.412162</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "      <td>4.857444</td>\n",
              "      <td>5849.0</td>\n",
              "      <td>8.674026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LP001003</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4583</td>\n",
              "      <td>1508.0</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>N</td>\n",
              "      <td>4.852030</td>\n",
              "      <td>6091.0</td>\n",
              "      <td>8.714568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001005</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>3000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "      <td>4.189655</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>8.006368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2583</td>\n",
              "      <td>2358.0</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "      <td>4.787492</td>\n",
              "      <td>4941.0</td>\n",
              "      <td>8.505323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>6000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.000000</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "      <td>4.948760</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>8.699515</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    .colab-df-convert {\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-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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert: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",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f796c19c-11b6-41b7-88b0-6bf60d626037 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-f796c19c-11b6-41b7-88b0-6bf60d626037');\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",
              "            '<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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=dataset.iloc[:,np.r_[1:5,9:11,13:15]].values\n",
        "Y=dataset.iloc[:,12].values"
      ],
      "metadata": {
        "id": "VRkxuV9YO11B"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iXRJs8wePo1-",
        "outputId": "7fc29dac-dbfc-4c6b-eb21-78d3be229061"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([['Male', 'No', '0', ..., 1.0, 4.857444178729353, 5849.0],\n",
              "       ['Male', 'Yes', '1', ..., 1.0, 4.852030263919617, 6091.0],\n",
              "       ['Male', 'Yes', '0', ..., 1.0, 4.189654742026425, 3000.0],\n",
              "       ...,\n",
              "       ['Male', 'Yes', '1', ..., 1.0, 5.53338948872752, 8312.0],\n",
              "       ['Male', 'Yes', '2', ..., 1.0, 5.231108616854587, 7583.0],\n",
              "       ['Female', 'No', '0', ..., 0.0, 4.890349128221754, 4583.0]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "edW0pHGyPqI4",
        "outputId": "6484a9f9-22f7-4e77-cf09-285707da0607"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y',\n",
              "       'N', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'N', 'Y', 'N', 'N', 'N', 'Y',\n",
              "       'Y', 'Y', 'N', 'Y', 'N', 'N', 'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y',\n",
              "       'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
              "       'N', 'N', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'N',\n",
              "       'N', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'N', 'N',\n",
              "       'N', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'N', 'N', 'N', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'N', 'N', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'N',\n",
              "       'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'N', 'N', 'N',\n",
              "       'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y',\n",
              "       'N', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'N', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'N',\n",
              "       'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y',\n",
              "       'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'N', 'Y', 'N', 'N', 'N', 'N',\n",
              "       'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y',\n",
              "       'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N',\n",
              "       'N', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'N', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'N', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
              "       'N', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'N', 'Y', 'Y', 'Y',\n",
              "       'Y', 'N', 'N', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
              "       'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'N', 'N', 'Y',\n",
              "       'Y', 'N', 'Y', 'Y', 'Y', 'N', 'N', 'N', 'Y', 'N', 'Y', 'N', 'Y',\n",
              "       'N', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y',\n",
              "       'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'N', 'N', 'Y', 'N', 'Y', 'Y',\n",
              "       'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y',\n",
              "       'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
              "       'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
              "       'N', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'N', 'N', 'N',\n",
              "       'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N',\n",
              "       'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
              "       'N', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'N', 'N', 'Y', 'N',\n",
              "       'Y', 'N', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'N',\n",
              "       'N', 'Y', 'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'N',\n",
              "       'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
              "       'Y', 'Y', 'N'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.3,random_state=0)"
      ],
      "metadata": {
        "id": "UkPCWS52Pz95"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U7hmx4VQQMHO",
        "outputId": "74d77f1f-decd-4ef2-9af5-0cd539093ff0"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['Male' 'No' '0' ... 1.0 5.730099782973574 12917.0]\n",
            " ['Male' 'Yes' '0' ... 1.0 4.955827057601261 7159.0]\n",
            " ['Male' 'Yes' '0' ... 1.0 4.74493212836325 4567.0]\n",
            " ...\n",
            " ['Male' 'Yes' '3+' ... 1.0 5.298317366548036 8334.0]\n",
            " ['Male' 'Yes' '0' ... 1.0 5.075173815233827 6033.0]\n",
            " ['Female' 'Yes' '0' ... 1.0 5.204006687076795 6486.0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "labelencoder_X= LabelEncoder()"
      ],
      "metadata": {
        "id": "ZirSJiAGQOtv"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(0,5):\n",
        "  X_train[:,i]=labelencoder_X.fit_transform(X_train[:,i])"
      ],
      "metadata": {
        "id": "VcZ8TwaxSCBh"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[:,7]=labelencoder_X.fit_transform(X_train[:,7])"
      ],
      "metadata": {
        "id": "mt4gMXUDTI5J"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RXEzTUczS6w1",
        "outputId": "3513fc26-6776-4f15-c7e8-cabf826e26c2"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 0, 0, ..., 1.0, 5.730099782973574, 367],\n",
              "       [1, 1, 0, ..., 1.0, 4.955827057601261, 295],\n",
              "       [1, 1, 0, ..., 1.0, 4.74493212836325, 131],\n",
              "       ...,\n",
              "       [1, 1, 3, ..., 1.0, 5.298317366548036, 321],\n",
              "       [1, 1, 0, ..., 1.0, 5.075173815233827, 241],\n",
              "       [0, 1, 0, ..., 1.0, 5.204006687076795, 268]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labelencoder_Y= LabelEncoder()\n",
        "Y_train=labelencoder_Y.fit_transform(Y_train)"
      ],
      "metadata": {
        "id": "NNO17FWYTqxV"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gCx_H00_Uovv",
        "outputId": "fd3e50e4-dd7c-4209-899a-e6502452a1f5"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1,\n",
              "       1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0,\n",
              "       0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
              "       1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0,\n",
              "       1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
              "       0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0,\n",
              "       1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0,\n",
              "       1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0,\n",
              "       0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0,\n",
              "       0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
              "       1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0,\n",
              "       1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1,\n",
              "       1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0,\n",
              "       0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0,\n",
              "       0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(0,5):\n",
        "  X_test[:,i]=labelencoder_X.fit_transform(X_test[:,i])"
      ],
      "metadata": {
        "id": "YOIwsu_JUtjx"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test[:,7]=labelencoder_X.fit_transform(X_test[:,7])"
      ],
      "metadata": {
        "id": "l-Wf78kgVTDG"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JWjdJNY_VZmr",
        "outputId": "295f16f4-489e-42b4-f349-9b2829bb987c"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 0, 0, ..., 1.0, 4.430816798843313, 126],\n",
              "       [0, 0, 0, ..., 1.0, 4.718498871295094, 46],\n",
              "       [1, 1, 0, ..., 1.0, 5.780743515792329, 153],\n",
              "       ...,\n",
              "       [0, 1, 0, ..., 1.0, 3.58351893845611, 12],\n",
              "       [1, 1, 0, ..., 1.0, 5.135798437050262, 16],\n",
              "       [1, 0, 0, ..., 1.0, 4.624972813284271, 74]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labelencoder_Y= LabelEncoder()\n",
        "Y_test=labelencoder_Y.fit_transform(Y_test)"
      ],
      "metadata": {
        "id": "7jFnkapMV6os"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C7devwrcWBG5",
        "outputId": "c83f4a1f-e9de-4590-9321-ce4de26b261c"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1,\n",
              "       1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
              "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1,\n",
              "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1,\n",
              "       1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
              "       1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1,\n",
              "       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0,\n",
              "       1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "ss=StandardScaler()\n",
        "X_train=ss.fit_transform(X_train)\n",
        "X_test=ss.fit_transform(X_test)"
      ],
      "metadata": {
        "id": "0Gc__cUbWIIs"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "DTC=DecisionTreeClassifier(criterion='entropy',random_state=0)\n",
        "DTC.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l5gGHZ7bWvEm",
        "outputId": "5815005b-fd07-41ff-b0c7-f1ffec28c6da"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred=DTC.predict(X_test)\n",
        "Y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pD8Kpp5uXXDJ",
        "outputId": "23508a4c-a06a-4bb4-9565-26971b0a3861"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1,\n",
              "       1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0,\n",
              "       0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1,\n",
              "       1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1,\n",
              "       1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,\n",
              "       1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1,\n",
              "       1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0,\n",
              "       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1,\n",
              "       1, 1, 0, 1, 1, 0, 1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "print(\"Accuracy,DT:\", metrics.accuracy_score(Y_pred,Y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "17CEZ8zyXfT5",
        "outputId": "74dc3b7a-f0ec-4ecf-d81c-05b6437bd985"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy,DT: 0.7135135135135136\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "NBC= GaussianNB()\n",
        "NBC.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJWGxPcYX2ng",
        "outputId": "45460246-b837-4dce-f987-c614d92f5276"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred=NBC.predict(X_test)"
      ],
      "metadata": {
        "id": "qSe0Ni_9YMdw"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gAowxu9mYQ0R",
        "outputId": "e80ad3b4-7fce-46f6-c414-8ef2ef8cab5e"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
              "       1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
              "       1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy,NB:\", metrics.accuracy_score(Y_pred,Y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GHGYDyiVYSTI",
        "outputId": "09704119-4989-43f0-fce1-e94eb4df585a"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy,NB: 0.8216216216216217\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "RFC=RandomForestClassifier()\n",
        "RFC.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9nUM_f83YaHZ",
        "outputId": "079b9f5a-42a0-4287-dc74-449f8793b523"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred=RFC.predict(X_test)"
      ],
      "metadata": {
        "id": "L_--DBHxY3tC"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A3bkcl3EY7pd",
        "outputId": "1ca9a725-719a-4a12-f510-8f0ac4079a54"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
              "       1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
              "       1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 0, 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy,RF:\", metrics.accuracy_score(Y_pred,Y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XJF41G9oZAIG",
        "outputId": "5c781888-6603-4d56-cd92-0519c20ce898"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy,RF: 0.8\n"
          ]
        }
      ]
    }
  ]
}