EksplorasiData_AndriPradipta/Marketing_Analytics_Shopee.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Marketing Analytics Shopee.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "yEkbgv0oTI_0"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lCwk_QM_TU46",
        "outputId": "69b2623c-2666-45c0-cf1c-f7872e0280f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "train = pd.read_csv('train.csv')\n",
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country_code</th>\n",
              "      <th>grass_date</th>\n",
              "      <th>user_id</th>\n",
              "      <th>subject_line_length</th>\n",
              "      <th>last_open_day</th>\n",
              "      <th>last_login_day</th>\n",
              "      <th>last_checkout_day</th>\n",
              "      <th>open_count_last_10_days</th>\n",
              "      <th>open_count_last_30_days</th>\n",
              "      <th>open_count_last_60_days</th>\n",
              "      <th>login_count_last_10_days</th>\n",
              "      <th>login_count_last_30_days</th>\n",
              "      <th>login_count_last_60_days</th>\n",
              "      <th>checkout_count_last_10_days</th>\n",
              "      <th>checkout_count_last_30_days</th>\n",
              "      <th>checkout_count_last_60_days</th>\n",
              "      <th>open_flag</th>\n",
              "      <th>row_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-07-16 00:00:00+08:00</td>\n",
              "      <td>43</td>\n",
              "      <td>44</td>\n",
              "      <td>19</td>\n",
              "      <td>6</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>43</td>\n",
              "      <td>99</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-07-16 00:00:00+08:00</td>\n",
              "      <td>102</td>\n",
              "      <td>44</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>17</td>\n",
              "      <td>18</td>\n",
              "      <td>48</td>\n",
              "      <td>90</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>2019-07-16 00:00:00+08:00</td>\n",
              "      <td>177</td>\n",
              "      <td>49</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>24</td>\n",
              "      <td>69</td>\n",
              "      <td>119</td>\n",
              "      <td>5</td>\n",
              "      <td>19</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>2019-07-16 00:00:00+08:00</td>\n",
              "      <td>184</td>\n",
              "      <td>49</td>\n",
              "      <td>49</td>\n",
              "      <td>9</td>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>23</td>\n",
              "      <td>69</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>2019-07-16 00:00:00+08:00</td>\n",
              "      <td>221</td>\n",
              "      <td>49</td>\n",
              "      <td>227</td>\n",
              "      <td>6</td>\n",
              "      <td>221</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   country_code                 grass_date  ...  open_flag  row_id\n",
              "0             4  2019-07-16 00:00:00+08:00  ...          0       0\n",
              "1             4  2019-07-16 00:00:00+08:00  ...          1       1\n",
              "2             6  2019-07-16 00:00:00+08:00  ...          0       2\n",
              "3             1  2019-07-16 00:00:00+08:00  ...          0       3\n",
              "4             6  2019-07-16 00:00:00+08:00  ...          0       4\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJzAwSCoZ1xK",
        "outputId": "1abeec0e-3cc4-424e-98e5-97a8ac27ad1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "test = pd.read_csv('test.csv')\n",
        "test.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country_code</th>\n",
              "      <th>grass_date</th>\n",
              "      <th>user_id</th>\n",
              "      <th>subject_line_length</th>\n",
              "      <th>last_open_day</th>\n",
              "      <th>last_login_day</th>\n",
              "      <th>last_checkout_day</th>\n",
              "      <th>open_count_last_10_days</th>\n",
              "      <th>open_count_last_30_days</th>\n",
              "      <th>open_count_last_60_days</th>\n",
              "      <th>login_count_last_10_days</th>\n",
              "      <th>login_count_last_30_days</th>\n",
              "      <th>login_count_last_60_days</th>\n",
              "      <th>checkout_count_last_10_days</th>\n",
              "      <th>checkout_count_last_30_days</th>\n",
              "      <th>checkout_count_last_60_days</th>\n",
              "      <th>row_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>2019-09-03 00:00:00+08:00</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>27</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>34</td>\n",
              "      <td>134</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>2019-09-03 00:00:00+08:00</td>\n",
              "      <td>130</td>\n",
              "      <td>35</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>383</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>2019-09-03 00:00:00+08:00</td>\n",
              "      <td>150</td>\n",
              "      <td>25</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>19</td>\n",
              "      <td>38</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>2019-09-03 00:00:00+08:00</td>\n",
              "      <td>181</td>\n",
              "      <td>36</td>\n",
              "      <td>63</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>110</td>\n",
              "      <td>173</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2019-09-03 00:00:00+08:00</td>\n",
              "      <td>192</td>\n",
              "      <td>23</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>54</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   country_code                 grass_date  ...  checkout_count_last_60_days  row_id\n",
              "0             6  2019-09-03 00:00:00+08:00  ...                           18       0\n",
              "1             6  2019-09-03 00:00:00+08:00  ...                            0       1\n",
              "2             5  2019-09-03 00:00:00+08:00  ...                            2       2\n",
              "3             1  2019-09-03 00:00:00+08:00  ...                            5       3\n",
              "4             5  2019-09-03 00:00:00+08:00  ...                            2       4\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pgAl3C9DTYWo",
        "outputId": "1211135a-4d25-4fae-e278-e98040f4e269",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 73539 entries, 0 to 73538\n",
            "Data columns (total 18 columns):\n",
            " #   Column                       Non-Null Count  Dtype \n",
            "---  ------                       --------------  ----- \n",
            " 0   country_code                 73539 non-null  int64 \n",
            " 1   grass_date                   73539 non-null  object\n",
            " 2   user_id                      73539 non-null  int64 \n",
            " 3   subject_line_length          73539 non-null  int64 \n",
            " 4   last_open_day                73539 non-null  object\n",
            " 5   last_login_day               73539 non-null  object\n",
            " 6   last_checkout_day            73539 non-null  object\n",
            " 7   open_count_last_10_days      73539 non-null  int64 \n",
            " 8   open_count_last_30_days      73539 non-null  int64 \n",
            " 9   open_count_last_60_days      73539 non-null  int64 \n",
            " 10  login_count_last_10_days     73539 non-null  int64 \n",
            " 11  login_count_last_30_days     73539 non-null  int64 \n",
            " 12  login_count_last_60_days     73539 non-null  int64 \n",
            " 13  checkout_count_last_10_days  73539 non-null  int64 \n",
            " 14  checkout_count_last_30_days  73539 non-null  int64 \n",
            " 15  checkout_count_last_60_days  73539 non-null  int64 \n",
            " 16  open_flag                    73539 non-null  int64 \n",
            " 17  row_id                       73539 non-null  int64 \n",
            "dtypes: int64(14), object(4)\n",
            "memory usage: 10.1+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WYrIdntHTaOa",
        "outputId": "067933ba-7b6a-4c97-ca4d-fdd787894bec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "#train = train.set_index(train['row_id'])\n",
        "train.drop('row_id', axis=1, inplace=True)\n",
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country_code</th>\n",
              "      <th>grass_date</th>\n",
              "      <th>user_id</th>\n",
              "      <th>subject_line_length</th>\n",
              "      <th>last_open_day</th>\n",
              "      <th>last_login_day</th>\n",
              "      <th>last_checkout_day</th>\n",
              "      <th>open_count_last_10_days</th>\n",
              "      <th>open_count_last_30_days</th>\n",
              "      <th>open_count_last_60_days</th>\n",
              "      <th>login_count_last_10_days</th>\n",
              "      <th>login_count_last_30_days</th>\n",
              "      <th>login_count_last_60_days</th>\n",
              "      <th>checkout_count_last_10_days</th>\n",
              "      <th>checkout_count_last_30_days</th>\n",
              "      <th>checkout_count_last_60_days</th>\n",
              "      <th>open_flag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-07-16 00:00:00+08:00</td>\n",
              "      <td>43</td>\n",
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              "      <td>19</td>\n",
              "      <td>6</td>\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>43</td>\n",
              "      <td>99</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-07-16 00:00:00+08:00</td>\n",
              "      <td>102</td>\n",
              "      <td>44</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>17</td>\n",
              "      <td>18</td>\n",
              "      <td>48</td>\n",
              "      <td>90</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>2019-07-16 00:00:00+08:00</td>\n",
              "      <td>177</td>\n",
              "      <td>49</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>24</td>\n",
              "      <td>69</td>\n",
              "      <td>119</td>\n",
              "      <td>5</td>\n",
              "      <td>19</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>2019-07-16 00:00:00+08:00</td>\n",
              "      <td>184</td>\n",
              "      <td>49</td>\n",
              "      <td>49</td>\n",
              "      <td>9</td>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>23</td>\n",
              "      <td>69</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>2019-07-16 00:00:00+08:00</td>\n",
              "      <td>221</td>\n",
              "      <td>49</td>\n",
              "      <td>227</td>\n",
              "      <td>6</td>\n",
              "      <td>221</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   country_code  ... open_flag\n",
              "0             4  ...         0\n",
              "1             4  ...         1\n",
              "2             6  ...         0\n",
              "3             1  ...         0\n",
              "4             6  ...         0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MAbcVJheaJbm",
        "outputId": "f041d0f9-f8aa-4744-a523-27e320f913f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        }
      },
      "source": [
        "#test = test.set_index(test['row_id'])\n",
        "#test.drop('row_id', axis=1, inplace=True)\n",
        "test.drop('grass_date', axis=1, inplace=True)\n",
        "test.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>country_code</th>\n",
              "      <th>user_id</th>\n",
              "      <th>subject_line_length</th>\n",
              "      <th>last_open_day</th>\n",
              "      <th>last_login_day</th>\n",
              "      <th>last_checkout_day</th>\n",
              "      <th>open_count_last_10_days</th>\n",
              "      <th>open_count_last_30_days</th>\n",
              "      <th>open_count_last_60_days</th>\n",
              "      <th>login_count_last_10_days</th>\n",
              "      <th>login_count_last_30_days</th>\n",
              "      <th>login_count_last_60_days</th>\n",
              "      <th>checkout_count_last_10_days</th>\n",
              "      <th>checkout_count_last_30_days</th>\n",
              "      <th>checkout_count_last_60_days</th>\n",
              "      <th>row_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>27</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>34</td>\n",
              "      <td>134</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>130</td>\n",
              "      <td>35</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>383</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>150</td>\n",
              "      <td>25</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>19</td>\n",
              "      <td>38</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>181</td>\n",
              "      <td>36</td>\n",
              "      <td>63</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>110</td>\n",
              "      <td>173</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>192</td>\n",
              "      <td>23</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>54</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   country_code  user_id  ...  checkout_count_last_60_days row_id\n",
              "0             6        0  ...                           18      0\n",
              "1             6      130  ...                            0      1\n",
              "2             5      150  ...                            2      2\n",
              "3             1      181  ...                            5      3\n",
              "4             5      192  ...                            2      4\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tQuFTgbbaS1W"
      },
      "source": [
        "test = test.drop(['user_id','country_code','subject_line_length'],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eov5UXIZTcU8"
      },
      "source": [
        "train = train.drop(['user_id','country_code','subject_line_length'],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Om_mad9DaiQx"
      },
      "source": [
        "test['last_open_day'] = test['last_open_day'].replace(['Never open'],'0')\n",
        "test['last_login_day'] = test['last_login_day'].replace(['Never login'],'0')\n",
        "test['last_checkout_day'] = test['last_checkout_day'].replace(['Never checkout'],'0')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZYxETkPuTeDX"
      },
      "source": [
        "train['last_open_day'] = train['last_open_day'].replace(['Never open'],'0')\n",
        "train['last_login_day'] = train['last_login_day'].replace(['Never login'],'0')\n",
        "train['last_checkout_day'] = train['last_checkout_day'].replace(['Never checkout'],'0')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wFz90uBXann5",
        "outputId": "fbd1accb-faba-4402-dfa3-e2f70da65a34",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "test[\"last_open_day\"] = test[\"last_checkout_day\"].astype(str).astype(int)\n",
        "test[\"last_login_day\"] = test[\"last_checkout_day\"].astype(str).astype(int)\n",
        "test[\"last_checkout_day\"] = test[\"last_checkout_day\"].astype(str).astype(int)\n",
        "print(test.dtypes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "last_open_day                  int64\n",
            "last_login_day                 int64\n",
            "last_checkout_day              int64\n",
            "open_count_last_10_days        int64\n",
            "open_count_last_30_days        int64\n",
            "open_count_last_60_days        int64\n",
            "login_count_last_10_days       int64\n",
            "login_count_last_30_days       int64\n",
            "login_count_last_60_days       int64\n",
            "checkout_count_last_10_days    int64\n",
            "checkout_count_last_30_days    int64\n",
            "checkout_count_last_60_days    int64\n",
            "row_id                         int64\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qcSpkiQcTgBp",
        "outputId": "de6cf775-c1fa-4145-fb54-3712801d872b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train[\"last_open_day\"] = train[\"last_checkout_day\"].astype(str).astype(int)\n",
        "train[\"last_login_day\"] = train[\"last_checkout_day\"].astype(str).astype(int)\n",
        "train[\"last_checkout_day\"] = train[\"last_checkout_day\"].astype(str).astype(int)\n",
        "print(train.dtypes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "grass_date                     object\n",
            "last_open_day                   int64\n",
            "last_login_day                  int64\n",
            "last_checkout_day               int64\n",
            "open_count_last_10_days         int64\n",
            "open_count_last_30_days         int64\n",
            "open_count_last_60_days         int64\n",
            "login_count_last_10_days        int64\n",
            "login_count_last_30_days        int64\n",
            "login_count_last_60_days        int64\n",
            "checkout_count_last_10_days     int64\n",
            "checkout_count_last_30_days     int64\n",
            "checkout_count_last_60_days     int64\n",
            "open_flag                       int64\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khnEyVOVTigM",
        "outputId": "272bd37d-0a65-4386-9cdc-b246ee162dd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        }
      },
      "source": [
        "sns.heatmap(data=train.corr(),annot=True)\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(15,5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NBiUbVAOUjNL",
        "outputId": "0d9547c1-77b0-46e6-c537-54d0b1c654ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "train.describe()"
      ],
      "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>last_open_day</th>\n",
              "      <th>last_login_day</th>\n",
              "      <th>last_checkout_day</th>\n",
              "      <th>open_count_last_10_days</th>\n",
              "      <th>open_count_last_30_days</th>\n",
              "      <th>open_count_last_60_days</th>\n",
              "      <th>login_count_last_10_days</th>\n",
              "      <th>login_count_last_30_days</th>\n",
              "      <th>login_count_last_60_days</th>\n",
              "      <th>checkout_count_last_10_days</th>\n",
              "      <th>checkout_count_last_30_days</th>\n",
              "      <th>checkout_count_last_60_days</th>\n",
              "      <th>open_flag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>73539.000000</td>\n",
              "      <td>73539.000000</td>\n",
              "      <td>73539.000000</td>\n",
              "      <td>73539.000000</td>\n",
              "      <td>73539.000000</td>\n",
              "      <td>73539.000000</td>\n",
              "      <td>73539.000000</td>\n",
              "      <td>73539.000000</td>\n",
              "      <td>73539.000000</td>\n",
              "      <td>73539.000000</td>\n",
              "      <td>73539.000000</td>\n",
              "      <td>73539.000000</td>\n",
              "      <td>73539.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>56.716191</td>\n",
              "      <td>56.716191</td>\n",
              "      <td>56.716191</td>\n",
              "      <td>0.911829</td>\n",
              "      <td>2.780457</td>\n",
              "      <td>5.019364</td>\n",
              "      <td>8.382287</td>\n",
              "      <td>25.284053</td>\n",
              "      <td>49.010430</td>\n",
              "      <td>0.909898</td>\n",
              "      <td>2.796040</td>\n",
              "      <td>5.420960</td>\n",
              "      <td>0.155781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>117.721606</td>\n",
              "      <td>117.721606</td>\n",
              "      <td>117.721606</td>\n",
              "      <td>1.754052</td>\n",
              "      <td>4.570239</td>\n",
              "      <td>7.902313</td>\n",
              "      <td>13.424436</td>\n",
              "      <td>37.853189</td>\n",
              "      <td>71.819327</td>\n",
              "      <td>2.887416</td>\n",
              "      <td>7.686064</td>\n",
              "      <td>13.980182</td>\n",
              "      <td>0.362650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>12.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1445.000000</td>\n",
              "      <td>1445.000000</td>\n",
              "      <td>1445.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>135.000000</td>\n",
              "      <td>254.000000</td>\n",
              "      <td>735.000000</td>\n",
              "      <td>1260.000000</td>\n",
              "      <td>157.000000</td>\n",
              "      <td>531.000000</td>\n",
              "      <td>783.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       last_open_day  last_login_day  ...  checkout_count_last_60_days     open_flag\n",
              "count   73539.000000    73539.000000  ...                 73539.000000  73539.000000\n",
              "mean       56.716191       56.716191  ...                     5.420960      0.155781\n",
              "std       117.721606      117.721606  ...                    13.980182      0.362650\n",
              "min         0.000000        0.000000  ...                     0.000000      0.000000\n",
              "25%         1.000000        1.000000  ...                     0.000000      0.000000\n",
              "50%        12.000000       12.000000  ...                     1.000000      0.000000\n",
              "75%        51.000000       51.000000  ...                     6.000000      0.000000\n",
              "max      1445.000000     1445.000000  ...                   783.000000      1.000000\n",
              "\n",
              "[8 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4asHkM3jUlNB",
        "outputId": "1734b452-8549-426b-b2d5-9988e48af3fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.countplot(data=train,x='open_flag')\n",
        "plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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": {
        "id": "TFIYkR2NWNhH"
      },
      "source": [
        "# X = data feature\n",
        "x_train = train.drop(['last_open_day', 'last_login_day',\n",
        "       'last_checkout_day', 'login_count_last_10_days',\n",
        "       'login_count_last_30_days', 'login_count_last_60_days',\n",
        "       'checkout_count_last_10_days', 'checkout_count_last_30_days',\n",
        "       'checkout_count_last_60_days','open_flag','grass_date'],axis=1)\n",
        "y_train = train['open_flag']\n",
        "x_test = test[['open_count_last_10_days','open_count_last_30_days','open_count_last_60_days']]\n",
        "# y = data target\n",
        "#Y = train['open_flag']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_iyiVqVh1IWD",
        "outputId": "1cb23520-ef00-472b-8d8b-c632780c7512",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "x_test.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>open_count_last_10_days</th>\n",
              "      <th>open_count_last_30_days</th>\n",
              "      <th>open_count_last_60_days</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   open_count_last_10_days  open_count_last_30_days  open_count_last_60_days\n",
              "0                        2                        3                        4\n",
              "1                        1                        1                        1\n",
              "2                        0                        0                        0\n",
              "3                        0                        0                        0\n",
              "4                        0                        0                        0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PpQXlfN9yl20"
      },
      "source": [
        "y_train.fillna(train['open_flag'].mean(), inplace=True)\n",
        "x_train['open_count_last_10_days'].fillna(train['open_count_last_10_days'].mean(), inplace=True)\n",
        "x_train['open_count_last_30_days'].fillna(train['open_count_last_30_days'].mean(), inplace=True)\n",
        "x_train['open_count_last_60_days'].fillna(train['open_count_last_60_days'].mean(), inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nDRnG7ElfwYF",
        "outputId": "0a01c1d9-6617-4b89-9fc7-d29cb898e14a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "x_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(54466, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2HIHgsqzFWp",
        "outputId": "6e8cc7fb-257a-4775-8728-fa6170913241",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# count the empty values in each column\n",
        "y_train.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "snlztvXP9OIT"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC, LinearSVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.linear_model import Perceptron\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3Qfiukw_iB9",
        "outputId": "66e3fd50-8704-4faa-9cf7-2bbd27fe0138",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "logreg = LogisticRegression()\n",
        "logreg.fit(x_train, y_train)\n",
        "Y_pred = logreg.predict(x_test)\n",
        "acc_log = round(logreg.score(x_train, y_train) * 100, 2)\n",
        "acc_log"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "87.52"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJmcJg2E_tBB",
        "outputId": "10531c9f-de90-4262-fb23-490d9a846280",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "svc = SVC()\n",
        "svc.fit(x_train, y_train)\n",
        "Y_pred = svc.predict(x_test)\n",
        "acc_svc = round(svc.score(x_train, y_train) * 100, 2)\n",
        "acc_svc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "87.61"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PmAaNVzd_0a3",
        "outputId": "86e87b05-8a8e-420f-8924-62a2d15a9abf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "knn = KNeighborsClassifier(n_neighbors = 3)\n",
        "knn.fit(x_train, y_train)\n",
        "Y_pred = knn.predict(x_test)\n",
        "acc_knn = round(knn.score(x_train, y_train) * 100, 2)\n",
        "acc_knn"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "86.84"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rst2caVBf3PD",
        "outputId": "c1429908-bf53-4fa0-9c5f-9aca838f389c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "random_forest = RandomForestClassifier(n_estimators=100)\n",
        "random_forest.fit(x_train, y_train)\n",
        "y_pred = random_forest.predict(x_test)\n",
        "acc_rf = round(random_forest.score(x_train,y_train)*100,2)\n",
        "acc_rf"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "88.84"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NE5O4nJMg191",
        "outputId": "20a0249c-82ef-4307-ab2b-0cf5d7fb270d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "decision_tree = DecisionTreeClassifier()\n",
        "decision_tree.fit(x_train, y_train)\n",
        "Y_pred = decision_tree.predict(x_test)\n",
        "acc_decision_tree = round(decision_tree.score(x_train, y_train) * 100, 2)\n",
        "acc_decision_tree"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "88.84"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GzawfimaBeU6",
        "outputId": "a029a901-9e9f-416e-9c70-8dbd4da73741",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "models = pd.DataFrame({\n",
        "    'Model': ['Logistic Regression', 'Support Vector Machines', 'KNN',  \n",
        "              'Random Forest','Decision Tree'],\n",
        "    'Score': [acc_log, acc_svc, acc_knn,  \n",
        "              acc_rf, acc_decision_tree]})\n",
        "models.sort_values(by='Score', ascending=False)"
      ],
      "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>Model</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>88.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>88.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Support Vector Machines</td>\n",
              "      <td>87.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>87.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>KNN</td>\n",
              "      <td>86.84</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Model  Score\n",
              "3            Random Forest  88.84\n",
              "4            Decision Tree  88.84\n",
              "1  Support Vector Machines  87.61\n",
              "0      Logistic Regression  87.52\n",
              "2                      KNN  86.84"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVmfTQW-ZhLT",
        "outputId": "15a8cb12-4e12-49ff-b745-600aaed62934",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "submission = pd.DataFrame({\n",
        "        \"raw_id\": test[\"row_id\"],\n",
        "        \"pred\": y_pred\n",
        "    })\n",
        "submission.head()\n",
        "#submission.to_excel('/content/submission.xlsx')"
      ],
      "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>raw_id</th>\n",
              "      <th>pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   raw_id  pred\n",
              "0       0     0\n",
              "1       1     0\n",
              "2       2     0\n",
              "3       3     0\n",
              "4       4     0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5rZHthO4iZFd",
        "outputId": "92180b1e-3c64-47a0-b4fa-6b7d12e8a266",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.countplot(data=submission,x='pred')\n",
        "plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAR4UlEQVR4nO3df6xfdX3H8eeLVhR/IEU6ZC2zRDtdZZNBA3X6h4OtFDeFGCGwOSojdgm4uGzZhssyNpREMzcnTkm6UWnViahzdAzXdYgzGhEuyPgp4Q5htAF7pQj+CJqy9/64n5pvym252Pu938+9fT6Sk+857/M55/s5yU1e+Zzv556TqkKSpN4cNOoOSJI0FQNKktQlA0qS1CUDSpLUJQNKktSlhaPuwGw74ogjatmyZaPuhiSpueWWW75TVYv3rB9wAbVs2TLGxsZG3Q1JUpPkwanq3uKTJHXJgJIkdcmAkiR1yYCSJHXJgJIkdcmAkiR1yYCSJHXJgJIkdcmAkiR1yYCSJHXpgHvU0Uw44Y83jboLmsNu+etzR90FaU5wBCVJ6pIBJUnqkgElSerSUAMqyQNJ7khyW5KxVjs8ydYk97XPRa2eJJclGU9ye5LjB86ztrW/L8nagfoJ7fzj7dgM83okSbNnNkZQv1pVx1XVyrZ9EXB9VS0Hrm/bAKcBy9uyDrgcJgMNuBg4CTgRuHh3qLU27xg4bs3wL0eSNBtGcYvvdGBjW98InDFQ31STbgQOS3IUcCqwtap2VtVjwFZgTdt3aFXdWFUFbBo4lyRpjht2QBXwH0luSbKu1Y6sqofb+iPAkW19CfDQwLHbWm1f9W1T1J8mybokY0nGJiYm9ud6JEmzZNj/B/X6qtqe5GeArUm+ObizqipJDbkPVNV6YD3AypUrh/59kqT9N9QRVFVtb587gM8z+RvSt9vtOdrnjtZ8O3D0wOFLW21f9aVT1CVJ88DQAirJC5K8aPc6sBq4E9gM7J6Jtxa4pq1vBs5ts/lWAY+3W4FbgNVJFrXJEauBLW3fE0lWtdl75w6cS5I0xw3zFt+RwOfbzO+FwD9V1b8nuRm4Osn5wIPAWa39dcAbgXHgh8B5AFW1M8l7gJtbu0uqamdbvwC4EjgE+EJbJEnzwNACqqruB14zRf1R4JQp6gVcuJdzbQA2TFEfA47d785KkrrjkyQkSV0yoCRJXTKgJEldMqAkSV0yoCRJXTKgJEldMqAkSV0yoCRJXTKgJEldMqAkSV0yoCRJXTKgJEldMqAkSV0yoCRJXTKgJEldMqAkSV0yoCRJXTKgJEldMqAkSV0yoCRJXTKgJEldMqAkSV0yoCRJXTKgJEldMqAkSV0yoCRJXTKgJEldMqAkSV0yoCRJXTKgJEldMqAkSV0yoCRJXTKgJEldMqAkSV0aekAlWZDkG0mubdvHJPl6kvEkn05ycKs/t22Pt/3LBs7x7la/N8mpA/U1rTae5KJhX4skafbMxgjqXcA9A9vvBz5YVa8AHgPOb/Xzgcda/YOtHUlWAGcDrwbWAB9tobcA+AhwGrACOKe1lSTNA0MNqCRLgd8A/rFtBzgZ+GxrshE4o62f3rZp+09p7U8HrqqqH1XVt4Bx4MS2jFfV/VX1Y+Cq1laSNA8MewT1d8CfAP/Xtl8CfLeqdrXtbcCStr4EeAig7X+8tf9JfY9j9laXJM0DQwuoJL8J7KiqW4b1Hc+iL+uSjCUZm5iYGHV3JEnTMMwR1OuANyd5gMnbbycDHwIOS7KwtVkKbG/r24GjAdr+FwOPDtb3OGZv9aepqvVVtbKqVi5evHj/r0ySNHRDC6iqendVLa2qZUxOcvhiVf02cAPw1tZsLXBNW9/ctmn7v1hV1epnt1l+xwDLgZuAm4HlbVbgwe07Ng/reiRJs2vhMzeZcX8KXJXkvcA3gCta/Qrg40nGgZ1MBg5VdVeSq4G7gV3AhVX1FECSdwJbgAXAhqq6a1avRJI0NLMSUFX1JeBLbf1+Jmfg7dnmSeDMvRx/KXDpFPXrgOtmsKuSpE74JAlJUpcMKElSlwwoSVKXDChJUpcMKElSlwwoSVKXDChJUpcMKElSlwwoSVKXDChJUpcMKElSlwwoSVKXDChJUpcMKElSlwwoSVKXDChJUpcMKElSlwwoSVKXDChJUpcMKElSlwwoSVKXDChJUpcMKElSlwwoSVKXDChJUpcMKElSlwwoSVKXDChJUpcMKElSlwwoSVKXDChJUpcMKElSlwwoSVKXDChJUpcMKElSl4YWUEmel+SmJP+d5K4kf9XqxyT5epLxJJ9OcnCrP7dtj7f9ywbO9e5WvzfJqQP1Na02nuSiYV2LJGn2DXME9SPg5Kp6DXAcsCbJKuD9wAer6hXAY8D5rf35wGOt/sHWjiQrgLOBVwNrgI8mWZBkAfAR4DRgBXBOaytJmgeGFlA16ftt8zltKeBk4LOtvhE4o62f3rZp+09Jkla/qqp+VFXfAsaBE9syXlX3V9WPgataW0nSPDDU36DaSOc2YAewFfgf4LtVtas12QYsaetLgIcA2v7HgZcM1vc4Zm/1qfqxLslYkrGJiYmZuDRJ0pBNK6CSXD+d2p6q6qmqOg5YyuSI51XPuoczoKrWV9XKqlq5ePHiUXRBkvQsLdzXziTPA54PHJFkEZC261D2MlqZSlV9N8kNwGuBw5IsbKOkpcD21mw7cDSwLclC4MXAowP13QaP2VtdkjTHPdMI6veAW5gc+dwysFwD/P2+DkyyOMlhbf0Q4NeBe4AbgLe2ZmvbuQA2t23a/i9WVbX62W2W3zHAcuAm4GZgeZsVeDCTEyk2T+eiJUn92+cIqqo+BHwoye9X1Yef5bmPAja22XYHAVdX1bVJ7gauSvJe4BvAFa39FcDHk4wDO5kMHKrqriRXA3cDu4ALq+opgCTvBLYAC4ANVXXXs+yjJKlT+wyo3arqw0l+BVg2eExVbdrHMbcDvzxF/X4mf4/as/4kcOZeznUpcOkU9euA6575CiRJc820AirJx4GXA7cBT7VyAXsNKEmS9se0AgpYCaxovwlJkjR00/0/qDuBlw6zI5IkDZruCOoI4O4kNzH5CCMAqurNQ+mVJOmAN92A+sthdkKSpD1Ndxbffw27I5IkDZruLL7vMTlrD+BgJh/8+oOqOnRYHZMkHdimO4J60e71gSeMrxpWpyRJetZPM2+v0fgX4NRnbCxJ0k9purf43jKweRCT/xf15FB6JEkS05/F96aB9V3AA/hyQEnSEE33N6jzht0RSZIGTfeFhUuTfD7JjrZ8LsnSYXdOknTgmu4kiY8x+a6ln23Lv7aaJElDMd2AWlxVH6uqXW25EvDd6ZKkoZluQD2a5G1JFrTlbUy+jl2SpKGYbkD9LnAW8AjwMJOvZH/7kPokSdK0p5lfAqytqscAkhwOfIDJ4JIkacZNdwT1S7vDCaCqdjLF69wlSZop0w2og5Is2r3RRlDTHX1JkvSsTTdk/gb4WpLPtO0zgUuH0yVJkqb/JIlNScaAk1vpLVV19/C6JUk60E37Nl0LJENJkjQrnvXrNiRJmg0GlCSpSwaUJKlLBpQkqUsGlCSpSwaUJKlLBpQkqUsGlCSpSwaUJKlLBpQkqUsGlCSpS0MLqCRHJ7khyd1J7kryrlY/PMnWJPe1z0WtniSXJRlPcnuS4wfOtba1vy/J2oH6CUnuaMdcliTDuh5J0uwa5ghqF/BHVbUCWAVcmGQFcBFwfVUtB65v2wCnAcvbsg64HH7y7qmLgZOAE4GLB95NdTnwjoHj1gzxeiRJs2hoAVVVD1fVrW39e8A9wBLgdGBja7YROKOtnw5sqkk3AoclOQo4FdhaVTvbW323AmvavkOr6saqKmDTwLkkSXPcrPwGlWQZk6+I/zpwZFU93HY9AhzZ1pcADw0ctq3V9lXfNkV9qu9fl2QsydjExMR+XYskaXYMPaCSvBD4HPAHVfXE4L428qlh96Gq1lfVyqpauXjx4mF/nSRpBgw1oJI8h8lw+mRV/XMrf7vdnqN97mj17cDRA4cvbbV91ZdOUZckzQPDnMUX4Argnqr624Fdm4HdM/HWAtcM1M9ts/lWAY+3W4FbgNVJFrXJEauBLW3fE0lWte86d+BckqQ5btqvfP8pvA74HeCOJLe12p8B7wOuTnI+8CBwVtt3HfBGYBz4IXAeQFXtTPIe4ObW7pKq2tnWLwCuBA4BvtAWSdI8MLSAqqqvAHv7v6RTpmhfwIV7OdcGYMMU9THg2P3opiSpUz5JQpLUJQNKktQlA0qS1CUDSpLUJQNKktQlA0qS1CUDSpLUJQNKktQlA0qS1CUDSpLUJQNKktQlA0qS1CUDSpLUJQNKktQlA0qS1CUDSpLUJQNKktQlA0qS1CUDSpLUJQNKktQlA0qS1CUDSpLUJQNKktQlA0qS1CUDSpLUJQNKktQlA0qS1CUDSpLUJQNKktQlA0qS1CUDSpLUJQNKktQlA0qS1CUDSpLUpaEFVJINSXYkuXOgdniSrUnua5+LWj1JLksynuT2JMcPHLO2tb8vydqB+glJ7mjHXJYkw7oWSdLsG+YI6kpgzR61i4Drq2o5cH3bBjgNWN6WdcDlMBlowMXAScCJwMW7Q621ecfAcXt+lyRpDhtaQFXVl4Gde5RPBza29Y3AGQP1TTXpRuCwJEcBpwJbq2pnVT0GbAXWtH2HVtWNVVXApoFzSZLmgdn+DerIqnq4rT8CHNnWlwAPDbTb1mr7qm+boj6lJOuSjCUZm5iY2L8rkCTNipFNkmgjn5ql71pfVSurauXixYtn4yslSftptgPq2+32HO1zR6tvB44eaLe01fZVXzpFXZI0T8x2QG0Gds/EWwtcM1A/t83mWwU83m4FbgFWJ1nUJkesBra0fU8kWdVm7507cC5J0jywcFgnTvIp4A3AEUm2MTkb733A1UnOBx4EzmrNrwPeCIwDPwTOA6iqnUneA9zc2l1SVbsnXlzA5EzBQ4AvtEWSNE8MLaCq6py97DplirYFXLiX82wANkxRHwOO3Z8+SpL65ZMkJEldMqAkSV0yoCRJXTKgJEldMqAkSV0yoCRJXTKgJEldMqAkSV0yoCRJXTKgJEldMqAkSV0yoCRJXTKgJEldMqAkSV0yoCRJXTKgJEldMqAkSV0yoCRJXTKgJEldMqAkSV1aOOoOSBqd/73kF0fdBc1hP/cXdwz1/I6gJEldMqAkSV0yoCRJXTKgJEldMqAkSV0yoCRJXTKgJEldMqAkSV0yoCRJXTKgJEldMqAkSV0yoCRJXTKgJEldMqAkSV2a8wGVZE2Se5OMJ7lo1P2RJM2MOR1QSRYAHwFOA1YA5yRZMdpeSZJmwpwOKOBEYLyq7q+qHwNXAaePuE+SpBkw19+ouwR4aGB7G3DSno2SrAPWtc3vJ7l3Fvp2IDsC+M6oO9GrfGDtqLug6fNveV8uzkyd6WVTFed6QE1LVa0H1o+6HweKJGNVtXLU/ZD2l3/LozXXb/FtB44e2F7aapKkOW6uB9TNwPIkxyQ5GDgb2DziPkmSZsCcvsVXVbuSvBPYAiwANlTVXSPulrydqvnDv+URSlWNug+SJD3NXL/FJ0mapwwoSVKXDCjNKB89pfkgyYYkO5LcOeq+HMgMKM0YHz2leeRKYM2oO3GgM6A0k3z0lOaFqvoysHPU/TjQGVCaSVM9emrJiPoiaY4zoCRJXTKgNJN89JSkGWNAaSb56ClJM8aA0oypql3A7kdP3QNc7aOnNBcl+RTwNeCVSbYlOX/UfToQ+agjSVKXHEFJkrpkQEmSumRASZK6ZEBJkrpkQEmSumRASfNUkjckuXbU/ZB+WgaUNMe0p8ZL854BJXUkybIk30zyyST3JPlskucneSDJ+5PcCpyZZHWSryW5NclnkrywHb+mHX8r8JbRXo20fwwoqT+vBD5aVb8APAFc0OqPVtXxwH8Cfw78WtseA/4wyfOAfwDeBJwAvHTWey7NIANK6s9DVfXVtv4J4PVt/dPtcxWTL4T8apLbgLXAy4BXAd+qqvtq8hExn5jFPkszbuGoOyDpafZ8/tju7R+0zwBbq+qcwUZJjht2x6TZ5AhK6s/PJXltW/8t4Ct77L8ReF2SVwAkeUGSnwe+CSxL8vLW7hykOcyAkvpzL3BhknuARcDlgzuragJ4O/CpJLcz+dTtV1XVk8A64N/aJIkds9praYb5NHOpI0mWAddW1bEj7oo0co6gJEldcgQlSeqSIyhJUpcMKElSlwwoSVKXDChJUpcMKElSl/4fv0ilf1WimP8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntt0p0tWJQyH",
        "outputId": "3ed036c6-9dd5-49b4-cb61-d981d4a7b4a4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "submission['pred'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    51751\n",
              "1     4219\n",
              "Name: pred, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OahZQCx5KCip"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}