Hack_for_safety.ipynb

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  "metadata": {
    "colab": {
      "name": "Hack_for_safety",
      "provenance": [],
      "mount_file_id": "1SLN9EoCtTAMhhcfUElOTDfCitiZe2tAB",
      "authorship_tag": "ABX9TyPDSjaThu5EGNZ5WgN2zsWR",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NsiriRoua/Zindi_Mentorship/blob/main/Hack_for_safety.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WqTWnFB-dDs-"
      },
      "source": [
        " import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score,  mean_squared_error, f1_score, roc_auc_score\n",
        "import numpy as np\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_val_score,cross_val_predict\n",
        "from sklearn.preprocessing import LabelEncoder,OneHotEncoder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kzoqeeovi-1b"
      },
      "source": [
        "train_df = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/Hack_for_safety/Train (4).csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "PcAq8FwtjDQO",
        "outputId": "ade371a3-2882-4a79-9a35-9b6899d27244"
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>race</th>\n",
              "      <th>dwelling</th>\n",
              "      <th>dwelling_type</th>\n",
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              "      <th>metro_code</th>\n",
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              "      <th>nationality</th>\n",
              "      <th>RTH</th>\n",
              "      <th>marital_st</th>\n",
              "      <th>Lang_inside</th>\n",
              "      <th>Lang_outside</th>\n",
              "      <th>Education</th>\n",
              "      <th>lw_work</th>\n",
              "      <th>lw_business</th>\n",
              "      <th>help_on_household</th>\n",
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              "      <th>nature_of_work</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>ID_00008683</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>Black African</td>\n",
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              "      <td>NW - Non Metro</td>\n",
              "      <td>66710095</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>Son/daughter/stepchild/adopted child of person 01</td>\n",
              "      <td>Single and have never been married/never lived...</td>\n",
              "      <td>Xitsonga</td>\n",
              "      <td>Setswana</td>\n",
              "      <td>Grade 11/Standard 9/Form 4</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Not applicable</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_00061163</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>Black African</td>\n",
              "      <td>Dwelling/house or brick/concrete block structu...</td>\n",
              "      <td>Formal</td>\n",
              "      <td>Limpopo</td>\n",
              "      <td>LP - Non Metro</td>\n",
              "      <td>98510279</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>Other relative (e.g. in-laws or aunt/uncle) of...</td>\n",
              "      <td>Living together like husband and wife</td>\n",
              "      <td>Sepedi</td>\n",
              "      <td>Sepedi</td>\n",
              "      <td>Grade 12/Standard 10/Form 5/Matric (No Exemption)</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Not applicable</td>\n",
              "      <td>Permanent</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ID_00071403</td>\n",
              "      <td>Female</td>\n",
              "      <td>86</td>\n",
              "      <td>Black African</td>\n",
              "      <td>Dwelling/house or brick/concrete block structu...</td>\n",
              "      <td>Formal</td>\n",
              "      <td>Eastern Cape</td>\n",
              "      <td>EC - Non Metro</td>\n",
              "      <td>29210321</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>Father/mother/stepfather/stepmother of person 01</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>IsiXhosa</td>\n",
              "      <td>IsiXhosa</td>\n",
              "      <td>Unspecified</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Not applicable</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ID_00077803</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>Black African</td>\n",
              "      <td>Town house (semi-detached house in complex)</td>\n",
              "      <td>Formal</td>\n",
              "      <td>KwaZulu-Natal</td>\n",
              "      <td>KZN - eThekwini</td>\n",
              "      <td>59913860</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>Son/daughter/stepchild/adopted child of person 01</td>\n",
              "      <td>Single and have never been married/never lived...</td>\n",
              "      <td>IsiZulu</td>\n",
              "      <td>IsiZulu</td>\n",
              "      <td>Grade 12/Standard 10/Form 5/Matric (No Exemption)</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Not applicable</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ID_00086763</td>\n",
              "      <td>Female</td>\n",
              "      <td>26</td>\n",
              "      <td>Black African</td>\n",
              "      <td>Town house (semi-detached house in complex)</td>\n",
              "      <td>Formal</td>\n",
              "      <td>Gauteng</td>\n",
              "      <td>GP - City of Johannesburg</td>\n",
              "      <td>79813828</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>Son/daughter/stepchild/adopted child of person 01</td>\n",
              "      <td>Single and have never been married/never lived...</td>\n",
              "      <td>Setswana</td>\n",
              "      <td>English</td>\n",
              "      <td>Grade 12/Standard 10/Form 5/Matric (No Exemption)</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
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              "      <td>0</td>\n",
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            ],
            "text/plain": [
              "            ID  gender  age  ... job_or_business  nature_of_work target\n",
              "0  ID_00008683  Female   20  ...              No  Not applicable      0\n",
              "1  ID_00061163  Female   42  ...  Not applicable       Permanent      0\n",
              "2  ID_00071403  Female   86  ...              No  Not applicable      0\n",
              "3  ID_00077803  Female   20  ...              No  Not applicable      0\n",
              "4  ID_00086763  Female   26  ...              No  Not applicable      0\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LHe58SLbjGax",
        "outputId": "5ed27708-a52d-4e7e-8f41-3dfa5f5c8fe6"
      },
      "source": [
        "train_df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'gender', 'age', 'race', 'dwelling', 'dwelling_type',\n",
              "       'province_code', 'metro_code', 'psu', 'nationality', 'RTH',\n",
              "       'marital_st', 'Lang_inside', 'Lang_outside', 'Education', 'lw_work',\n",
              "       'lw_business', 'help_on_household', 'job_or_business', 'nature_of_work',\n",
              "       'target'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "NbRCewGjjJrY",
        "outputId": "e829d983-12f8-46f8-f3fb-af18d409b842"
      },
      "source": [
        "sns.countplot(train_df[\"target\"])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7fa80f7910>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "SpjCDTSujMaq",
        "outputId": "499a6fd5-b012-408e-8fe7-52a952314529"
      },
      "source": [
        "sns.heatmap(train_df.corr())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7fa80a2bd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BGlNZyDrjThr"
      },
      "source": [
        "label=LabelEncoder()\n",
        "train_df['gender'] = label.fit_transform(train_df['gender'])\n",
        "train_df['race'] = label.fit_transform(train_df['race'])\n",
        "train_df['dwelling'] = label.fit_transform(train_df['dwelling'])\n",
        "train_df['dwelling_type'] = label.fit_transform(train_df['dwelling_type'])\n",
        "train_df['province_code'] = label.fit_transform(train_df['province_code'])\n",
        "train_df['metro_code'] = label.fit_transform(train_df['metro_code'])\n",
        "train_df['nationality'] = label.fit_transform(train_df['nationality'])\n",
        "train_df['RTH'] = label.fit_transform(train_df['RTH'])\n",
        "train_df['marital_st'] = label.fit_transform(train_df['marital_st'])\n",
        "train_df['Lang_inside'] = label.fit_transform(train_df['Lang_inside'])\n",
        "train_df['Lang_outside'] = label.fit_transform(train_df['Lang_outside'])\n",
        "train_df['Education'] = label.fit_transform(train_df['Education'])\n",
        "train_df['lw_work'] = label.fit_transform(train_df['lw_work'])\n",
        "train_df['lw_business'] = label.fit_transform(train_df['lw_business'])\n",
        "train_df['help_on_household'] = label.fit_transform(train_df['help_on_household'])\n",
        "train_df['job_or_business'] = label.fit_transform(train_df['job_or_business'])\n",
        "train_df['nature_of_work'] = label.fit_transform(train_df['nature_of_work'])\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZYfq6ITDFGxM"
      },
      "source": [
        "X = train_df.drop([\"target\", \"ID\"], axis=1) # feature columns\n",
        "y = train_df[\"target\"] # the label/target column"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GzZSauHRFRdN",
        "outputId": "3aa35ffb-5c33-455e-fcd6-78879bda0c0e"
      },
      "source": [
        "cv=KFold(n_splits=5,shuffle=True,random_state=1)\n",
        "#Create the model :\n",
        "s=SVC()\n",
        "scores=cross_val_score(cls,X,y,cv=cv,scoring='accuracy')\n",
        "print(scores)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.9250646  0.91855204 0.93018746 0.90239173 0.91790562]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xb5eJpybmt4r"
      },
      "source": [
        "\n",
        "#Evaluate the model :\n",
        "y_pred=cross_val_predict(cls,X,y,cv=cv)\n",
        "final_model=cls.fit(X,y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QMKMxfbaC3ov"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Aui2lU3o74v5",
        "outputId": "8ed81897-5050-4283-984d-dd7e5d8db418"
      },
      "source": [
        "# export to \n",
        "import sklearn.externals\n",
        "import joblib\n",
        "\n",
        "joblib.dump(final_model, 'model.joblib')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['model.joblib']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "0ARIvWKo8DTb",
        "outputId": "2109a028-6ac6-49fd-c563-0173fde8c491"
      },
      "source": [
        "test_df=pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Hack_for_safety/Test (3).csv')\n",
        "#Preprocessing \n",
        "test_df.head()"
      ],
      "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>ID</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>race</th>\n",
              "      <th>dwelling</th>\n",
              "      <th>dwelling_type</th>\n",
              "      <th>province_code</th>\n",
              "      <th>metro_code</th>\n",
              "      <th>psu</th>\n",
              "      <th>nationality</th>\n",
              "      <th>RTH</th>\n",
              "      <th>marital_st</th>\n",
              "      <th>Lang_inside</th>\n",
              "      <th>Lang_outside</th>\n",
              "      <th>Education</th>\n",
              "      <th>lw_work</th>\n",
              "      <th>lw_business</th>\n",
              "      <th>help_on_household</th>\n",
              "      <th>job_or_business</th>\n",
              "      <th>nature_of_work</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID_00241643</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>Coloured</td>\n",
              "      <td>Dwelling/house or brick/concrete block structu...</td>\n",
              "      <td>Formal</td>\n",
              "      <td>Northern Cape</td>\n",
              "      <td>NC - Non Metro</td>\n",
              "      <td>36410104</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>Head/acting head</td>\n",
              "      <td>Single and have never been married/never lived...</td>\n",
              "      <td>Afrikaans</td>\n",
              "      <td>Afrikaans</td>\n",
              "      <td>Grade 10/Standard 8/Form 3</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Not applicable</td>\n",
              "      <td>Permanent</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_00312043</td>\n",
              "      <td>Female</td>\n",
              "      <td>64</td>\n",
              "      <td>Black African</td>\n",
              "      <td>Dwelling/house or brick/concrete block structu...</td>\n",
              "      <td>Formal</td>\n",
              "      <td>Limpopo</td>\n",
              "      <td>LP - Non Metro</td>\n",
              "      <td>97410477</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>Head/acting head</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>Afrikaans</td>\n",
              "      <td>Afrikaans</td>\n",
              "      <td>Grade 12/Standard 10/Form 5/Matric (No Exemption)</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Not applicable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ID_00345323</td>\n",
              "      <td>Female</td>\n",
              "      <td>18</td>\n",
              "      <td>Black African</td>\n",
              "      <td>Unspecified</td>\n",
              "      <td>Unspecified</td>\n",
              "      <td>Eastern Cape</td>\n",
              "      <td>EC - Buffalo City</td>\n",
              "      <td>26011572</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>Son/daughter/stepchild/adopted child of person 01</td>\n",
              "      <td>Single and have never been married/never lived...</td>\n",
              "      <td>IsiXhosa</td>\n",
              "      <td>IsiXhosa</td>\n",
              "      <td>Grade 11/Standard 9/Form 4</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Not applicable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ID_00369643</td>\n",
              "      <td>Female</td>\n",
              "      <td>19</td>\n",
              "      <td>Black African</td>\n",
              "      <td>Dwelling/house or brick/concrete block structu...</td>\n",
              "      <td>Formal</td>\n",
              "      <td>Mpumalanga</td>\n",
              "      <td>MP - Non Metro</td>\n",
              "      <td>87710674</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>Son/daughter/stepchild/adopted child of person 01</td>\n",
              "      <td>Single and have never been married/never lived...</td>\n",
              "      <td>Xitsonga</td>\n",
              "      <td>Sepedi</td>\n",
              "      <td>Grade 12/Standard 10/Form 5/Matric (No Exemption)</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Not applicable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ID_00381163</td>\n",
              "      <td>Female</td>\n",
              "      <td>24</td>\n",
              "      <td>Black African</td>\n",
              "      <td>Dwelling/house or brick/concrete block structu...</td>\n",
              "      <td>Formal</td>\n",
              "      <td>Mpumalanga</td>\n",
              "      <td>MP - Non Metro</td>\n",
              "      <td>87411106</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>Head/acting head</td>\n",
              "      <td>Single and have never been married/never lived...</td>\n",
              "      <td>SiSwati</td>\n",
              "      <td>SiSwati</td>\n",
              "      <td>Grade 11/Standard 9/Form 4</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Not applicable</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            ID  gender  age  ... help_on_household job_or_business  nature_of_work\n",
              "0  ID_00241643  Female   23  ...                No  Not applicable       Permanent\n",
              "1  ID_00312043  Female   64  ...                No              No  Not applicable\n",
              "2  ID_00345323  Female   18  ...                No              No  Not applicable\n",
              "3  ID_00369643  Female   19  ...                No              No  Not applicable\n",
              "4  ID_00381163  Female   24  ...                No              No  Not applicable\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JHoNnqza8Zu9"
      },
      "source": [
        "# preprocessing\n",
        "test_df['gender'] = label.fit_transform(test_df['gender'])\n",
        "test_df['race'] = label.fit_transform(test_df['race'])\n",
        "\n",
        "# preprocess all the othe columns as well\n",
        "test_df['dwelling'] = label.fit_transform(test_df['dwelling'])\n",
        "test_df['dwelling_type'] = label.fit_transform(test_df['dwelling_type'])\n",
        "test_df['province_code'] = label.fit_transform(test_df['province_code'])\n",
        "test_df['metro_code'] = label.fit_transform(test_df['metro_code'])\n",
        "test_df['nationality'] = label.fit_transform(test_df['nationality'])\n",
        "test_df['RTH'] = label.fit_transform(test_df['RTH'])\n",
        "test_df['marital_st'] = label.fit_transform(test_df['marital_st'])\n",
        "test_df['Lang_inside'] = label.fit_transform(test_df['Lang_inside'])\n",
        "test_df['Lang_outside'] = label.fit_transform(test_df['Lang_outside'])\n",
        "test_df['Education'] = label.fit_transform(test_df['Education'])\n",
        "test_df['lw_work'] = label.fit_transform(test_df['lw_work'])\n",
        "test_df['lw_business'] = label.fit_transform(test_df['lw_business'])\n",
        "test_df['help_on_household'] = label.fit_transform(test_df['help_on_household'])\n",
        "test_df['job_or_business'] = label.fit_transform(test_df['job_or_business'])\n",
        "test_df['nature_of_work'] = label.fit_transform(test_df['nature_of_work'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZMQROWAK8qb0"
      },
      "source": [
        "test_ = test_df.drop([\"ID\"], axis=1)\n",
        "test_predictions = final_model.predict(test_)\n",
        "submission_df = pd.DataFrame()\n",
        "submission_df[\"ID\"] = test_df[\"ID\"]\n",
        "submission_df[\"target\"] = test_predictions\n",
        "# file\n",
        "submission_df.to_csv(\"submission.csv\", index=False)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HBzgKjbAxHar",
        "outputId": "86db9597-6c5c-447d-c045-cd7869b1ac84"
      },
      "source": [
        "score"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.92312661, 0.92113769, 0.93148028, 0.90239173, 0.91855204])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GhJwHTAj_01R"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}