Watern_Brone_Disease_9_21_2021(Malaria).ipynb

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    "colab": {
      "name": "Watern_Brone_Disease_9_21_2021(Malaria).ipynb",
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      "collapsed_sections": [],
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hussain0048/Water-borne-diseases/blob/main/Watern_Brone_Disease_9_21_2021(Malaria).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VKWa-lg2Gynj"
      },
      "source": [
        "# **Table of Content**\n",
        "\n",
        "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
        "    <ol>\n",
        "        <li><a href=\"https://co\">Introduction</a></li>\n",
        "        <li><a href=\"#downloading_data\">Importing libraries</a></li>\n",
        "        <li><a href=\"#Processing\">Processing</a></li>\n",
        "        <li><a href=\"#setting_up_tree\">Splitting Training and Test Set</a></li>\n",
        "        <li><a href=\"#modeling\">Modle Training</a></li>\n",
        "        <li><a href=\"#prediction\">Saving model to disk</a></li>\n",
        "        <li><a href=\"#evaluation\">References</a></li>\n",
        "    </ol>\n",
        "</div>\n",
        "<br>\n",
        "<hr>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BwlGIq-fgExg"
      },
      "source": [
        "# **1- Introduction**\n",
        " the water-borne diseases are the most common which transmitted in contaminated fresh water, whether in bathing, washing, drinking, or in the preparation of food.. More than one-third of Earth&#39;s accessible renewable freshwater is consumptively used for agricultural, industrial, and\n",
        "domestic purposes. Water-borne diseases are commonly found throughout the world but the\n",
        "condition is more aggravated in developing countries including Pakistan. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gpjx_5M_Ma7v"
      },
      "source": [
        "#**2-Importing libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-E4TvIVLMa73",
        "scrolled": true
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import seaborn as sns \n",
        "import matplotlib.pyplot as plt\n",
        "from prettytable import PrettyTable\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "from mlxtend.plotting import plot_confusion_matrix \n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BetwIHqiVbt6"
      },
      "source": [
        "import pandas as pd \n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gHoPSZkDivGv"
      },
      "source": [
        "#**3-Processing**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Vt6LxAxbz6v"
      },
      "source": [
        "## **3.1 Data Reading**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K1Cj5cSkCzvl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f3c838c-0625-4143-a25f-16acb1d3bd39"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XGN87XMSivGw"
      },
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Datasets/Water Brone diesease/Malyria_2-21-2021.csv',encoding = 'latin')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B6-L96xXwnvr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "0c1d08d9-24bb-4d2c-9857-d2fbf0aef3fc"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Unnamed: 0</th>\n",
              "      <th>SECTION_NO</th>\n",
              "      <th>MRNO</th>\n",
              "      <th>PATIENT_NAME</th>\n",
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              "      <th>NIC_NEW</th>\n",
              "      <th>REFERRED_FROM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>NO_OF_ADMISSION</th>\n",
              "      <th>DISTRICT</th>\n",
              "      <th>TEHSIL</th>\n",
              "      <th>REPORT_VERIFIED</th>\n",
              "      <th>CPT_ID</th>\n",
              "      <th>RESULT_VALUE</th>\n",
              "      <th>CPT_ID.1</th>\n",
              "      <th>SECTION_NO.1</th>\n",
              "      <th>RESULT_TEXT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
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              "      <td>Saqib Mehmood Khan</td>\n",
              "      <td>MUHAMMAD AFSAR KHAN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.310163e+12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>havelia</td>\n",
              "      <td>11/12/2020 10:46:53 A11P11</td>\n",
              "      <td>1000000000085007</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM20123774</td>\n",
              "      <td>Negative</td>\n",
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              "      <td>2</td>\n",
              "      <td>K04HEM18160758</td>\n",
              "      <td>K0460000001680</td>\n",
              "      <td>Muhammad Iqbal</td>\n",
              "      <td>ABDUR REHMAN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.310134e+12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>9/29/2018 9:26:21 A9P9</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM18160758</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>K04HEM18160758</td>\n",
              "      <td>K0460000001680</td>\n",
              "      <td>Muhammad Iqbal</td>\n",
              "      <td>ABDUR REHMAN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.310134e+12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>9/29/2018 9:26:07 A9P9</td>\n",
              "      <td>1000000000085007</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM18160758</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>K04HEM17056995</td>\n",
              "      <td>K0460000000106</td>\n",
              "      <td>Shahbaz Ahmad</td>\n",
              "      <td>MUMTAZ AHMAD</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.220136e+12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>5/4/2017 9:48:37 A5P5</td>\n",
              "      <td>1000000000085007</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM17056995</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>K04HEM17073233</td>\n",
              "      <td>K0460000000150</td>\n",
              "      <td>Mehfooz Ahmad</td>\n",
              "      <td>HAIDER ZAMAN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.310109e+12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>6/3/2017 11:59:38 A6P6</td>\n",
              "      <td>1000000000085007</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM17073233</td>\n",
              "      <td>Negative</td>\n",
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              "  </tbody>\n",
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              "   Unnamed: 0      SECTION_NO  ...    SECTION_NO.1 RESULT_TEXT\n",
              "0           1  K04HEM20123774  ...  K04HEM20123774    Negative\n",
              "1           2  K04HEM18160758  ...  K04HEM18160758    Negative\n",
              "2           3  K04HEM18160758  ...  K04HEM18160758    Negative\n",
              "3           4  K04HEM17056995  ...  K04HEM17056995    Negative\n",
              "4           5  K04HEM17073233  ...  K04HEM17073233    Negative\n",
              "\n",
              "[5 rows x 19 columns]"
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          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q3oI8qBEgWQF"
      },
      "source": [
        "## 3.2- **Drop Columns**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LUmDSArIT_Gz"
      },
      "source": [
        "df.drop(labels=['Unnamed: 0', 'SECTION_NO', 'MRNO', 'PATIENT_NAME', 'FATHER_NAME',\n",
        "       'HUSBAND_NAME', 'NIC_NEW', 'REFERRED_FROM', 'NO_OF_ADMISSION', 'TEHSIL', 'REPORT_VERIFIED', 'CPT_ID',\n",
        "       'RESULT_VALUE', 'CPT_ID.1', 'SECTION_NO.1'],axis=1,inplace=True)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T885zE0IWN1c",
        "outputId": "cdbce937-d0ef-4323-b418-c37225a7a933",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>34 Year(s)</td>\n",
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              "      <th>1</th>\n",
              "      <td>43 Year(s)</td>\n",
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              "      <td>43 Year(s)</td>\n",
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              "      <td>Negative</td>\n",
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              "      <th>3</th>\n",
              "      <td>28 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>42 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
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              "      <th>...</th>\n",
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              "      <td>13 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Haripur</td>\n",
              "      <td>Negative</td>\n",
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              "    <tr>\n",
              "      <th>22912</th>\n",
              "      <td>20 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22913</th>\n",
              "      <td>21 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22914</th>\n",
              "      <td>45 Year(s)</td>\n",
              "      <td>Female</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22915</th>\n",
              "      <td>22 Year(s)</td>\n",
              "      <td>Female</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22916 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              AGE  GENDER    DISTRICT RESULT_TEXT\n",
              "0      34 Year(s)    Male  Abbottabad    Negative\n",
              "1      43 Year(s)    Male  Abbottabad    Negative\n",
              "2      43 Year(s)    Male  Abbottabad    Negative\n",
              "3      28 Year(s)    Male  Abbottabad    Negative\n",
              "4      42 Year(s)    Male  Abbottabad    Negative\n",
              "...           ...     ...         ...         ...\n",
              "22911  13 Year(s)    Male     Haripur    Negative\n",
              "22912  20 Year(s)    Male  Abbottabad    Negative\n",
              "22913  21 Year(s)    Male  Abbottabad    Negative\n",
              "22914  45 Year(s)  Female  Abbottabad    Negative\n",
              "22915  22 Year(s)  Female  Abbottabad    Negative\n",
              "\n",
              "[22916 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "4z2-2606wuwe",
        "outputId": "ae269bbd-4395-4292-fce7-412d1a6c39c9"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>34 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>43 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>43 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>42 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          AGE GENDER    DISTRICT RESULT_TEXT\n",
              "0  34 Year(s)   Male  Abbottabad    Negative\n",
              "1  43 Year(s)   Male  Abbottabad    Negative\n",
              "2  43 Year(s)   Male  Abbottabad    Negative\n",
              "3  28 Year(s)   Male  Abbottabad    Negative\n",
              "4  42 Year(s)   Male  Abbottabad    Negative"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ai0YcEsvDhbV"
      },
      "source": [
        "## **3.3 Remove string from Age**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_injZ1Ikj9wC"
      },
      "source": [
        "df['Age'] = df['AGE'].str.split().str[0]"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "r2b-QOEWkhoy",
        "outputId": "b564369c-4518-403f-b9e2-dcb6fd636c90"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 10,
      "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>AGE</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>DISTRICT</th>\n",
              "      <th>RESULT_TEXT</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>34 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>43 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>43 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>42 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          AGE GENDER    DISTRICT RESULT_TEXT Age\n",
              "0  34 Year(s)   Male  Abbottabad    Negative  34\n",
              "1  43 Year(s)   Male  Abbottabad    Negative  43\n",
              "2  43 Year(s)   Male  Abbottabad    Negative  43\n",
              "3  28 Year(s)   Male  Abbottabad    Negative  28\n",
              "4  42 Year(s)   Male  Abbottabad    Negative  42"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PTRT-Lxs_hsP"
      },
      "source": [
        "df.drop(['AGE'],axis=1,inplace=True)\n"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "d0UyMcSO_48D",
        "outputId": "6c466979-4cc4-41a2-c4ba-ca36faa4e5db"
      },
      "source": [
        "df"
      ],
      "execution_count": 12,
      "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>GENDER</th>\n",
              "      <th>DISTRICT</th>\n",
              "      <th>RESULT_TEXT</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22911</th>\n",
              "      <td>Male</td>\n",
              "      <td>Haripur</td>\n",
              "      <td>Negative</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22912</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22913</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22914</th>\n",
              "      <td>Female</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22915</th>\n",
              "      <td>Female</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Negative</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22916 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       GENDER    DISTRICT RESULT_TEXT Age\n",
              "0        Male  Abbottabad    Negative  34\n",
              "1        Male  Abbottabad    Negative  43\n",
              "2        Male  Abbottabad    Negative  43\n",
              "3        Male  Abbottabad    Negative  28\n",
              "4        Male  Abbottabad    Negative  42\n",
              "...       ...         ...         ...  ..\n",
              "22911    Male     Haripur    Negative  13\n",
              "22912    Male  Abbottabad    Negative  20\n",
              "22913    Male  Abbottabad    Negative  21\n",
              "22914  Female  Abbottabad    Negative  45\n",
              "22915  Female  Abbottabad    Negative  22\n",
              "\n",
              "[22916 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TtDRkJLu27d6"
      },
      "source": [
        "##**3.4 Convert target variable into binary Form**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3NNM8H3QGZ8"
      },
      "source": [
        "df['RESULT_TEXT'].replace({'Negative': 0, 'Positive': 1},inplace = True)\n"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mMCjB_NdHCrW"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idAk1K8xl2IG"
      },
      "source": [
        "## **3.7- Check unique value** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ri0-PU1imPPB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d4723ba-5414-4731-8e6b-280ae0370867"
      },
      "source": [
        "print(df['DISTRICT'].unique())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Abbottabad' 'Mansehra' 'Haripur' 'Kohistan' 'Peshawar' 'Batgram' 'Bolan'\n",
            " 'Shangla' 'RahimYar Khan' 'Mardan' 'MUZAFFARABAD' 'Lower Dir' 'DIAMIR'\n",
            " 'ISLAMABAD' 'Lasbela' 'GILGIT' 'Vehari' 'Swat' 'Swabi' 'Tharparkar'\n",
            " 'BAGH' 'UNKNOWN F.A.T.A' 'Mianwali' 'Attock' 'Dadu' 'Charsadda' 'ORAKZAI'\n",
            " 'MIRPUR' 'Lahore' 'KHYBER' 'Upper Dir' 'Hangu' 'KURRAM' 'Zhob' 'POONCH'\n",
            " 'Nowshera' 'Rawalpindi' 'MOHMAND']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BOhqIxnqmeOQ",
        "outputId": "698a4f9f-cfc3-4e3a-9893-a0d073fe7b13"
      },
      "source": [
        "print(df['GENDER'].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Male' 'Female' 'Neuter']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_emrU7L7CC9"
      },
      "source": [
        "##**3.7 Hot Encoding**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3X2LonDY5Xny"
      },
      "source": [
        "#Label encoding executed manually\n",
        "DISTRICT_dict = { 'Abbottabad':0, 'Mansehra':1, 'Haripur':2, 'Kohistan':3, 'Peshawar':4, 'Batgram':5, 'Bolan':6, 'Shangla':7, 'RahimYar Khan':8, 'Mardan':9, 'MUZAFFARABAD':10, 'Lower Dir':11, 'DIAMIR':12, 'ISLAMABAD':13, 'Lasbela':14, 'GILGIT':15, 'Vehari':16, 'Swat':17, 'Swabi':18, 'Tharparkar':19,'BAGH':20, 'UNKNOWN F.A.T.A':21, 'Mianwali':22, 'Attock':23, 'Dadu':24, 'Charsadda':25, 'ORAKZAI':26, 'MIRPUR':27, 'KHYBER':28, 'Lahore':29, 'Upper Dir':30, 'Hangu':31, 'KURRAM':32, 'Zhob':33, 'POONCH':34,'Nowshera':35, 'Rawalpindi':36, 'MOHMAND':37}"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AqPArdT-AKn7"
      },
      "source": [
        "Gender_dict = { 'Male':1, 'Female':2, 'Neuter':3 }"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nVbwbaquFFEg"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A9iMcH6AFVAM"
      },
      "source": [
        "df['DISTRICT_Encoded']=df['DISTRICT'].map(DISTRICT_dict)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E_S77CXkFnFH",
        "outputId": "4b936515-6a27-45eb-ac48-e1c494e978eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 19,
      "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",
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              "    }\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>GENDER</th>\n",
              "      <th>DISTRICT</th>\n",
              "      <th>RESULT_TEXT</th>\n",
              "      <th>Age</th>\n",
              "      <th>DISTRICT_Encoded</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</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>22911</th>\n",
              "      <td>Male</td>\n",
              "      <td>Haripur</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22912</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22913</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22914</th>\n",
              "      <td>Female</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22915</th>\n",
              "      <td>Female</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22916 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       GENDER    DISTRICT  RESULT_TEXT Age  DISTRICT_Encoded\n",
              "0        Male  Abbottabad            0  34                 0\n",
              "1        Male  Abbottabad            0  43                 0\n",
              "2        Male  Abbottabad            0  43                 0\n",
              "3        Male  Abbottabad            0  28                 0\n",
              "4        Male  Abbottabad            0  42                 0\n",
              "...       ...         ...          ...  ..               ...\n",
              "22911    Male     Haripur            0  13                 2\n",
              "22912    Male  Abbottabad            0  20                 0\n",
              "22913    Male  Abbottabad            0  21                 0\n",
              "22914  Female  Abbottabad            0  45                 0\n",
              "22915  Female  Abbottabad            0  22                 0\n",
              "\n",
              "[22916 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pxzBKRE0F4ue"
      },
      "source": [
        "df['Gender_Encoded']=df['GENDER'].map(Gender_dict)\n"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "EitkY5CIGKYg",
        "outputId": "1a509c87-66d5-4ab6-d4ab-bb68c30c5638"
      },
      "source": [
        "df"
      ],
      "execution_count": 21,
      "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>GENDER</th>\n",
              "      <th>DISTRICT</th>\n",
              "      <th>RESULT_TEXT</th>\n",
              "      <th>Age</th>\n",
              "      <th>DISTRICT_Encoded</th>\n",
              "      <th>Gender_Encoded</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22911</th>\n",
              "      <td>Male</td>\n",
              "      <td>Haripur</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22912</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22913</th>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22914</th>\n",
              "      <td>Female</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22915</th>\n",
              "      <td>Female</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22916 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       GENDER    DISTRICT  RESULT_TEXT Age  DISTRICT_Encoded  Gender_Encoded\n",
              "0        Male  Abbottabad            0  34                 0               1\n",
              "1        Male  Abbottabad            0  43                 0               1\n",
              "2        Male  Abbottabad            0  43                 0               1\n",
              "3        Male  Abbottabad            0  28                 0               1\n",
              "4        Male  Abbottabad            0  42                 0               1\n",
              "...       ...         ...          ...  ..               ...             ...\n",
              "22911    Male     Haripur            0  13                 2               1\n",
              "22912    Male  Abbottabad            0  20                 0               1\n",
              "22913    Male  Abbottabad            0  21                 0               1\n",
              "22914  Female  Abbottabad            0  45                 0               2\n",
              "22915  Female  Abbottabad            0  22                 0               2\n",
              "\n",
              "[22916 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4kX3ghmTGXMF"
      },
      "source": [
        "df = df.drop(['GENDER','DISTRICT'],axis=1)\n"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a6v0cyV-Gl1I"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tiOLigktJvaY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "19b6f162-1841-4479-b3db-4e23c3061860"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22916, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q8nUvo04gXwk"
      },
      "source": [
        "## **3.8 Imbalance problem**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDB8ZCGkgd-r",
        "outputId": "2202cb1c-794a-40b7-e321-23bc49928f2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "from sklearn.utils import resample\n",
        "no = df[df.RESULT_TEXT == 0]\n",
        "yes = df[df.RESULT_TEXT == 1]\n",
        "yes_oversampled = resample(yes, replace=True, n_samples=len(no), random_state=123)\n",
        "oversampled = pd.concat([no, yes_oversampled])\n",
        "fig = plt.figure(figsize = (8,5))\n",
        "oversampled.RESULT_TEXT.value_counts(normalize = True).plot(kind='bar', color= ['skyblue','navy'], alpha = 0.9, rot=0)\n",
        "plt.title('Waterbrone Disease status Nagative(0) and Postive(1) after Oversampling (Balanced Dataset)')\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "zTBeqc9BK2Ea",
        "outputId": "2c1f4714-11f7-45d4-ffd6-2c45b0b0fd7a"
      },
      "source": [
        "oversampled"
      ],
      "execution_count": null,
      "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>RESULT_TEXT</th>\n",
              "      <th>Age</th>\n",
              "      <th>DISTRICT_Encoded</th>\n",
              "      <th>Gender_Encoded</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13513</th>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8974</th>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18480</th>\n",
              "      <td>1</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20294</th>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1407</th>\n",
              "      <td>1</td>\n",
              "      <td>82</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>45512 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       RESULT_TEXT Age  DISTRICT_Encoded  Gender_Encoded\n",
              "0                0  34                 0               1\n",
              "1                0  43                 0               1\n",
              "2                0  43                 0               1\n",
              "3                0  28                 0               1\n",
              "4                0  42                 0               1\n",
              "...            ...  ..               ...             ...\n",
              "13513            1   8                 0               1\n",
              "8974             1  16                 0               1\n",
              "18480            1  25                 0               1\n",
              "20294            1   7                 1               2\n",
              "1407             1  82                 5               1\n",
              "\n",
              "[45512 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "81IRMYGxupOV"
      },
      "source": [
        "# **4-Splitting Training and Test Set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5pcJs0LfeI4Q"
      },
      "source": [
        "X=oversampled.drop('RESULT_TEXT',axis=1)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52fY-DzqfFV1"
      },
      "source": [
        "y=oversampled[['RESULT_TEXT']]"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k1fyz1VYfhP7"
      },
      "source": [
        "# y\n",
        "X"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CXR6isVQgl8O"
      },
      "source": [
        "y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qX6xRBwGivHV"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=100)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vOY-lsTIvAvL"
      },
      "source": [
        "# **5-Modle Training**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BmhGaJq6ivHY"
      },
      "source": [
        "## **5.2-Random Forest**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYz_jvGQivHY"
      },
      "source": [
        "Ran_For= RandomForestClassifier(n_estimators=100,max_depth=15, random_state=11,max_leaf_nodes=10)\n",
        "Ran_For= Ran_For.fit(X_train , y_train)\n",
        "Ran_For"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uKbH4wscivHZ",
        "outputId": "542963d5-b5e3-4700-811b-14e072f6e25f"
      },
      "source": [
        "y_pred1 = Ran_For.predict(X_test)\n",
        "rn=Ran_For.score(X_test, y_test)\n",
        "print('Accuracy score= {:.4f}'.format(Ran_For.score(X_test, y_test)))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score= 0.7484\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pYOWzX56OxS9"
      },
      "source": [
        "## **5.2 Model optimization** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k_fegqUc83x3"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "\n"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JFjpStKO6VCS"
      },
      "source": [
        "n_samples = 45512\n",
        "n_features = 3"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v1l1xDWj8Tob"
      },
      "source": [
        "params = {'n_estimators': [20,50,100],\n",
        "          'max_depth': [None, 2, 5,],\n",
        "          'min_samples_split': [2, 0.5, n_samples//2, ],\n",
        "          'min_samples_leaf': [1, 0.5, n_samples//2, ],\n",
        "          'max_features': [None, 'sqrt', 'auto', 'log2', 0.3,0.5, n_features//2, ],\n",
        "          'bootstrap':[True, False]\n",
        "         }"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qqi78aQ8aX1",
        "outputId": "d14a1f2b-8bc1-4aeb-abdb-05c066a03015",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "ef_classifier_grid = GridSearchCV(ExtraTreesClassifier(random_state=1), param_grid=params, n_jobs=-1, cv=3, verbose=1)\n",
        "ef_classifier_grid.fit(X_train,y_train)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 1134 candidates, totalling 3402 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   12.5s\n",
            "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:   46.4s\n",
            "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:  1.7min\n",
            "[Parallel(n_jobs=-1)]: Done 796 tasks      | elapsed:  3.0min\n",
            "[Parallel(n_jobs=-1)]: Done 1246 tasks      | elapsed:  4.6min\n",
            "[Parallel(n_jobs=-1)]: Done 1796 tasks      | elapsed:  6.5min\n",
            "[Parallel(n_jobs=-1)]: Done 2446 tasks      | elapsed:  8.5min\n",
            "[Parallel(n_jobs=-1)]: Done 3196 tasks      | elapsed: 10.7min\n",
            "[Parallel(n_jobs=-1)]: Done 3402 out of 3402 | elapsed: 11.3min finished\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3, error_score=nan,\n",
              "             estimator=ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0,\n",
              "                                            class_weight=None, criterion='gini',\n",
              "                                            max_depth=None, max_features='auto',\n",
              "                                            max_leaf_nodes=None,\n",
              "                                            max_samples=None,\n",
              "                                            min_impurity_decrease=0.0,\n",
              "                                            min_impurity_split=None,\n",
              "                                            min_samples_leaf=1,\n",
              "                                            min_samples_split=2,\n",
              "                                            min_weight_fraction_leaf=0.0,\n",
              "                                            n_estimators=100, n_jobs=None,\n",
              "                                            o...e=1,\n",
              "                                            verbose=0, warm_start=False),\n",
              "             iid='deprecated', n_jobs=-1,\n",
              "             param_grid={'bootstrap': [True, False], 'max_depth': [None, 2, 5],\n",
              "                         'max_features': [None, 'sqrt', 'auto', 'log2', 0.3,\n",
              "                                          0.5, 1],\n",
              "                         'min_samples_leaf': [1, 0.5, 22756],\n",
              "                         'min_samples_split': [2, 0.5, 22756],\n",
              "                         'n_estimators': [20, 50, 100]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=1)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PpI446Vo8jlE",
        "outputId": "b9466d80-c4a2-46c5-951e-c8e5737bdce3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print('Train Accuracy : %.3f'%ef_classifier_grid.best_estimator_.score(X_train, y_train))\n",
        "print('Test Accurqacy : %.3f'%ef_classifier_grid.best_estimator_.score(X_test, y_test))\n",
        "print('Best Accuracy Through Grid Search : %.3f'%ef_classifier_grid.best_score_)\n",
        "print('Best Parameters : ',ef_classifier_grid.best_params_)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy : 0.840\n",
            "Test Accurqacy : 0.845\n",
            "Best Accuracy Through Grid Search : 0.839\n",
            "Best Parameters :  {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 20}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vf3lwlPXivHa"
      },
      "source": [
        "# **6-Saving model to disk**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NOiornCyviuZ"
      },
      "source": [
        "import pickle\n",
        "pickle.dump(ef_classifier_grid, open('model_rf_grid.pkl','wb'))\n"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aV2BUqhFwC_j"
      },
      "source": [
        "model = pickle.load(open('/content/model_rf_grid.pkl','rb'))"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-vgj53ULwGow"
      },
      "source": [
        "print(model.predict([[43, 1, 1]]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pK4c8nU4uXqE"
      },
      "source": [
        "#**References**\n",
        "\n",
        "[Random Forest Classification with H2O](https://medium.com/tech-vision/random-forest-classification-with-h2o-python-for-beginners-b31f6e4ccf3c)\n",
        "\n",
        "[Build and deploy your first machine learning web app](https://towardsdatascience.com/build-and-deploy-your-first-machine-learning-web-app-e020db344a99)\n",
        "\n",
        "[krishnaik06/Car-Price-Prediction](https://github.com/krishnaik06/Car-Price-Prediction/blob/master/Untitled.ipynb)\n",
        "\n",
        "[Deploy Machine Learning Model using Flask](https://www.youtube.com/watch?v=UbCWoMf80PY&ab_channel=KrishNaik)\n",
        "\n",
        "[Live- Implementation of End To End Kaggle Machine Learning Project With Deployment\n",
        "](https://www.youtube.com/watch?v=p_tpQSY1aTs&ab_channel=KrishNaik)\n",
        "\n",
        "[Deployment-flask](https://github.com/krishnaik06/Deployment-flask)\n",
        "\n",
        "[Flight-Fare-Prediction-Web-App\n",
        "Public](https://github.com/prakash100198/Flight-Fare-Prediction-Web-App)\n",
        "\n",
        "[Scikit-Learn - Ensemble Learning : Bootstrap Aggregation(Bagging) & Random Forests](https://coderzcolumn.com/tutorials/machine-learning/scikit-learn-sklearn-ensemble-learning-bagging-and-random-forests#2)\n"
      ]
    }
  ]
}