AV_CatBoost without CV.ipynb

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    "colab": {
      "name": "AV Boi",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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        "id": "uC1VRZ_S4foP",
        "outputId": "54fe247e-f818-4caf-f890-419e93065066"
      },
      "source": [
        "!pip install catboost"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting catboost\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/96/3b/bb419654adcf7efff42ed8a3f84e50c8f236424b7ed1cc8ccd290852e003/catboost-0.24.4-cp37-none-manylinux1_x86_64.whl (65.7MB)\n",
            "\u001b[K     |████████████████████████████████| 65.7MB 47kB/s \n",
            "\u001b[?25hRequirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from catboost) (4.4.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from catboost) (1.4.1)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.1.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.19.5)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost) (1.3.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-0.24.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
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          "height": 160
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        "id": "kfsFgHdr5E-G",
        "outputId": "fe77adde-d9fe-4d5b-dde8-1e7a0e2f38e0"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "     <input type=\"file\" id=\"files-ecd073ce-8911-48bd-9ec9-8fba0860d204\" name=\"files[]\" multiple disabled\n",
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              "     <output id=\"result-ecd073ce-8911-48bd-9ec9-8fba0860d204\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
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              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving sample_submission_QrCyCoT.csv to sample_submission_QrCyCoT.csv\n",
            "Saving test_YCcRUnU.csv to test_YCcRUnU.csv\n",
            "Saving train_Df64byy.csv to train_Df64byy.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5uVwB7gV5lki"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from catboost import CatBoostClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import *\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mv3XWq8q5fEn"
      },
      "source": [
        "train_df = pd.read_csv('/content/train_Df64byy.csv')\n",
        "test_df = pd.read_csv('/content/test_YCcRUnU.csv')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2o3BolhXASzV"
      },
      "source": [
        "def make_df_ready(df):\n",
        "  df = df.set_index('ID')\n",
        "  df['Holding_Policy_Duration'] = df['Holding_Policy_Duration'].replace('14+', '15.0').astype(float)\n",
        "  df['Is_Spouse'] = df['Is_Spouse'].map({'No': 1, 'Yes': 0})\n",
        "  df['Accomodation_Type'] = df['Accomodation_Type'].map({'Owned': 1, 'Rented': 0})\n",
        "  df['Reco_Insurance_Type'] = df['Reco_Insurance_Type'].map({'Individual': 1, 'Joint': 0})\n",
        "  df['Holding_Policy_Type'] = df['Holding_Policy_Type'].astype(str)\n",
        "  df['Reco_Policy_Cat'] = df['Reco_Policy_Cat'].astype(str)\n",
        "  df['Region_Code'] = df['Region_Code'].astype(str)\n",
        "  df['Health Indicator'] = df['Health Indicator'].astype(str)\n",
        "  return df"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2uc2Utn76ezG"
      },
      "source": [
        "train_df = train_df.set_index('ID')"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xfB4aQ1d6jgT"
      },
      "source": [
        "train_df['Holding_Policy_Duration'] = train_df['Holding_Policy_Duration'].replace('14+', '15.0').astype(float)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pMhwrL9M6dYe"
      },
      "source": [
        "train_df['Is_Spouse'] = train_df['Is_Spouse'].map({'No': 1, 'Yes': 0})"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JV1rN387GES"
      },
      "source": [
        "train_df['Accomodation_Type'] = train_df['Accomodation_Type'].map({'Owned': 1, 'Rented': 0})\n",
        "train_df['Reco_Insurance_Type'] = train_df['Reco_Insurance_Type'].map({'Individual': 1, 'Joint': 0})"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qmHkASOn7U2N"
      },
      "source": [
        "train_df['Holding_Policy_Type'] = train_df['Holding_Policy_Type'].astype(str)\n",
        "train_df['Reco_Policy_Cat'] = train_df['Reco_Policy_Cat'].astype(str)\n",
        "train_df['Region_Code'] = train_df['Region_Code'].astype(str)\n",
        "train_df['Health Indicator'] = train_df['Health Indicator'].astype(str)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3382jipp7pRQ"
      },
      "source": [
        "X = train_df.loc[:, [i for i in train_df.columns if i != 'Response']]\n",
        "y = train_df.loc[:, 'Response']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=21)"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEGsK33b8cl-"
      },
      "source": [
        "clf = CatBoostClassifier(iterations=200, task_type='GPU')"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6G9jHNmQ8WQt",
        "outputId": "453e36b3-06d8-4727-9d97-b8d0abd49afa"
      },
      "source": [
        "clf.fit(X=X_train, y=y_train, cat_features=['City_Code', 'Region_Code', 'Health Indicator', 'Holding_Policy_Type', 'Reco_Policy_Cat'])"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Learning rate set to 0.116284\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostClassifier at 0x7f66fa4f4710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y6gSS-xG8zc5"
      },
      "source": [
        "preds_test = clf.predict(X_test)"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "movvpGii_JRt",
        "outputId": "d784d950-f54f-4326-9773-79eabe8c5d4f"
      },
      "source": [
        "print(classification_report(y_test.values, preds_test))"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.93      0.86      7668\n",
            "           1       0.57      0.30      0.39      2509\n",
            "\n",
            "    accuracy                           0.77     10177\n",
            "   macro avg       0.68      0.61      0.63     10177\n",
            "weighted avg       0.74      0.77      0.74     10177\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4-rBbNgP_RLE",
        "outputId": "f9b0061a-4814-4b5b-9cba-64ee09b7348e"
      },
      "source": [
        "roc_auc_score(y_test.values, preds_test)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6122944878874237"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g3Eobf_3BPnT"
      },
      "source": [
        "test_to_predict = make_df_ready(test_df)"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hfS5Rs-4Bu0h"
      },
      "source": [
        "submission_preds = clf.predict(test_to_predict)"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W4c3vez1B9E4"
      },
      "source": [
        "sample_df = pd.read_csv('/content/sample_submission_QrCyCoT.csv')"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r4lX9eHsB-aT"
      },
      "source": [
        "sample_df['Response'] = submission_preds"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VF2-lroqCL1m",
        "outputId": "735cd529-cc57-41b5-9b10-dc4f0e3f70af"
      },
      "source": [
        "train_df['Response'].value_counts(normalize=True)"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.760053\n",
              "1    0.239947\n",
              "Name: Response, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MrMmEJyHCGMf"
      },
      "source": [
        "sample_df.to_csv('my_submission.csv', index=False)"
      ],
      "execution_count": 90,
      "outputs": []
    }
  ]
}