Project1.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Goal\n",
    "The goal of this challenge is to build a model that predicts conversion rate and, based on the model, come up with ideas to improve revenue.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/Users/yangzongkun/Desktop/sd/DS Take Home 1-3/1.conversion/conversion_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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</th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>source</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UK</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>Ads</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>China</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316195</th>\n",
       "      <td>US</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Ads</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316196</th>\n",
       "      <td>US</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>Direct</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316197</th>\n",
       "      <td>China</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>Ads</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316198</th>\n",
       "      <td>US</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>Seo</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316199</th>\n",
       "      <td>UK</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>Ads</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>316200 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  age  new_user  source  total_pages_visited  converted\n",
       "0           UK   25         1     Ads                    1          0\n",
       "1           US   23         1     Seo                    5          0\n",
       "2           US   28         1     Seo                    4          0\n",
       "3        China   39         1     Seo                    5          0\n",
       "4           US   30         1     Seo                    6          0\n",
       "...        ...  ...       ...     ...                  ...        ...\n",
       "316195      US   26         1     Ads                    9          0\n",
       "316196      US   27         1  Direct                    6          0\n",
       "316197   China   25         0     Ads                    4          0\n",
       "316198      US   22         0     Seo                    4          0\n",
       "316199      UK   24         0     Ads                    4          0\n",
       "\n",
       "[316200 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country                  4\t ['China', 'Germany', 'UK', 'US']\n",
      "age                     60\t [17, 18, 19, 20, 21]\n",
      "new_user                 2\t [0, 1]\n",
      "source                   3\t ['Ads', 'Direct', 'Seo']\n",
      "total_pages_visited     29\t [1, 2, 3, 4, 5]\n",
      "converted                2\t [0, 1]\n"
     ]
    }
   ],
   "source": [
    "for column in df.columns:\n",
    "    uniques = sorted(df[column].unique())\n",
    "    print('{0:20s} {1:5d}\\t'.format(column, len(uniques)), uniques[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>316200.000000</td>\n",
       "      <td>316200.000000</td>\n",
       "      <td>316200.000000</td>\n",
       "      <td>316200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>30.569858</td>\n",
       "      <td>0.685465</td>\n",
       "      <td>4.872966</td>\n",
       "      <td>0.032258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.271802</td>\n",
       "      <td>0.464331</td>\n",
       "      <td>3.341104</td>\n",
       "      <td>0.176685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>123.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 age       new_user  total_pages_visited      converted\n",
       "count  316200.000000  316200.000000        316200.000000  316200.000000\n",
       "mean       30.569858       0.685465             4.872966       0.032258\n",
       "std         8.271802       0.464331             3.341104       0.176685\n",
       "min        17.000000       0.000000             1.000000       0.000000\n",
       "25%        24.000000       0.000000             2.000000       0.000000\n",
       "50%        30.000000       1.000000             4.000000       0.000000\n",
       "75%        36.000000       1.000000             7.000000       0.000000\n",
       "max       123.000000       1.000000            29.000000       1.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for outlier\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>source</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>90928</th>\n",
       "      <td>Germany</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>Seo</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295581</th>\n",
       "      <td>UK</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>Ads</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  age  new_user source  total_pages_visited  converted\n",
       "90928   Germany  123         0    Seo                   15          1\n",
       "295581       UK  111         0    Ads                   10          1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Typically, age should be below 100. So, first let check outliers and clean the dataset\n",
    "df[df['age']>100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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</th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>source</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UK</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>Ads</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>China</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316195</th>\n",
       "      <td>US</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Ads</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316196</th>\n",
       "      <td>US</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>Direct</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316197</th>\n",
       "      <td>China</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>Ads</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316198</th>\n",
       "      <td>US</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>Seo</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316199</th>\n",
       "      <td>UK</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>Ads</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>316198 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  age  new_user  source  total_pages_visited  converted\n",
       "0           UK   25         1     Ads                    1          0\n",
       "1           US   23         1     Seo                    5          0\n",
       "2           US   28         1     Seo                    4          0\n",
       "3        China   39         1     Seo                    5          0\n",
       "4           US   30         1     Seo                    6          0\n",
       "...        ...  ...       ...     ...                  ...        ...\n",
       "316195      US   26         1     Ads                    9          0\n",
       "316196      US   27         1  Direct                    6          0\n",
       "316197   China   25         0     Ads                    4          0\n",
       "316198      US   22         0     Seo                    4          0\n",
       "316199      UK   24         0     Ads                    4          0\n",
       "\n",
       "[316198 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df[df['age']<=100]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 316198 entries, 0 to 316199\n",
      "Data columns (total 6 columns):\n",
      " #   Column               Non-Null Count   Dtype \n",
      "---  ------               --------------   ----- \n",
      " 0   country              316198 non-null  object\n",
      " 1   age                  316198 non-null  int64 \n",
      " 2   new_user             316198 non-null  int64 \n",
      " 3   source               316198 non-null  object\n",
      " 4   total_pages_visited  316198 non-null  int64 \n",
      " 5   converted            316198 non-null  int64 \n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 16.9+ MB\n"
     ]
    }
   ],
   "source": [
    "#check for missing value\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                object\n",
       "age                     int64\n",
       "new_user                int64\n",
       "source                 object\n",
       "total_pages_visited     int64\n",
       "converted               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for datatype\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                0\n",
       "age                    0\n",
       "new_user               0\n",
       "source                 0\n",
       "total_pages_visited    0\n",
       "converted              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to see the category\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "US         178092\n",
       "China       76602\n",
       "UK          48449\n",
       "Germany     13055\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to see rhe category\n",
    "df.country.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seo       155039\n",
       "Ads        88739\n",
       "Direct     72420\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.source.value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18, 6))\n",
    "sns.countplot(x='country', hue='converted', data=df, ax=ax[0])\n",
    "ax[0].set_title('Count Plot of Country', fontsize=16)\n",
    "\n",
    "sns.barplot(x='country', y='converted', data=df, ax=ax[1]);\n",
    "ax[1].set_title('Mean Conversion Rate per Country', fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18, 6))\n",
    "sns.countplot(x='new_user', hue='converted', data=df, ax=ax[0])\n",
    "ax[0].set_title('Count Plot of User Types', fontsize=16)\n",
    "ax[0].set_yscale('log')\n",
    "sns.barplot(x='new_user', y='converted', data=df, ax=ax[1]);\n",
    "ax[1].set_title('Mean Conversion Rate per User Type', fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18, 6))\n",
    "sns.countplot(x='source', hue='converted', data=df, ax=ax[0])\n",
    "ax[0].set_title('Count Plot of Different Sources', fontsize=16)\n",
    "ax[0].set_yscale('log')\n",
    "sns.barplot(x='source', y='converted', data=df, ax=ax[1]);\n",
    "ax[1].set_title('Mean Conversion Rate per Source', fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangzongkun/.conda/envs/untitled/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yangzongkun/.conda/envs/untitled/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped = df[['age', 'converted']].groupby('age').mean().reset_index()\n",
    "\n",
    "hist_kws={'histtype': 'bar', 'edgecolor':'black', 'alpha': 0.2}\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18, 6))\n",
    "sns.distplot(df[df['converted'] == 0]['age'], label='Converted 0', \n",
    "             ax=ax[0], hist_kws=hist_kws)\n",
    "sns.distplot(df[df['converted'] == 1]['age'], label='Converted 1', \n",
    "             ax=ax[0], hist_kws=hist_kws)\n",
    "ax[0].set_title('Count Plot of Age', fontsize=16)\n",
    "ax[0].legend()#右上角的lable\n",
    "\n",
    "\n",
    "ax[1].plot(grouped['age'], grouped['converted'], '.-')\n",
    "#The plot() method is used to create a line plot from one or more sets of data.\n",
    "#using a dot-and-line style for the plot\n",
    "ax[1].set_title('Mean Conversion Rate vs. Age', fontsize=16)\n",
    "ax[1].set_xlabel('age')\n",
    "ax[1].set_ylabel('Mean convertion rate')\n",
    "ax[1].grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>converted</th>\n",
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       "      <th>age</th>\n",
       "      <th></th>\n",
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       "      <th>38</th>\n",
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       "      <th>39</th>\n",
       "      <td>0.017313</td>\n",
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       "      <th>40</th>\n",
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       "      <th>41</th>\n",
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       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.013243</td>\n",
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       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.011623</td>\n",
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       "    <tr>\n",
       "      <th>44</th>\n",
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       "      <th>45</th>\n",
       "      <td>0.012383</td>\n",
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       "      <th>46</th>\n",
       "      <td>0.011356</td>\n",
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       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.007987</td>\n",
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       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.009430</td>\n",
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       "      <th>49</th>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
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       "      <th>52</th>\n",
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       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.003190</td>\n",
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       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0.001923</td>\n",
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       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.007614</td>\n",
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       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>58</th>\n",
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       "      <th>59</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.021277</td>\n",
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       "      <th>61</th>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
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       "      <th>72</th>\n",
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       "      <th>73</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.000000</td>\n",
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      ],
      "text/plain": [
       "     converted\n",
       "age           \n",
       "17    0.071607\n",
       "18    0.066029\n",
       "19    0.058616\n",
       "20    0.057109\n",
       "21    0.053985\n",
       "22    0.053243\n",
       "23    0.047990\n",
       "24    0.045448\n",
       "25    0.038782\n",
       "26    0.039767\n",
       "27    0.033513\n",
       "28    0.032564\n",
       "29    0.033621\n",
       "30    0.028998\n",
       "31    0.027461\n",
       "32    0.024802\n",
       "33    0.023989\n",
       "34    0.021556\n",
       "35    0.018394\n",
       "36    0.019297\n",
       "37    0.018953\n",
       "38    0.014158\n",
       "39    0.017313\n",
       "40    0.014270\n",
       "41    0.012342\n",
       "42    0.013243\n",
       "43    0.011623\n",
       "44    0.009943\n",
       "45    0.012383\n",
       "46    0.011356\n",
       "47    0.007987\n",
       "48    0.009430\n",
       "49    0.007366\n",
       "50    0.005900\n",
       "51    0.006404\n",
       "52    0.005348\n",
       "53    0.003190\n",
       "54    0.001923\n",
       "55    0.007614\n",
       "56    0.000000\n",
       "57    0.000000\n",
       "58    0.012121\n",
       "59    0.000000\n",
       "60    0.021277\n",
       "61    0.014085\n",
       "62    0.000000\n",
       "63    0.000000\n",
       "64    0.000000\n",
       "65    0.000000\n",
       "66    0.000000\n",
       "67    0.000000\n",
       "68    0.000000\n",
       "69    0.000000\n",
       "70    0.000000\n",
       "72    0.000000\n",
       "73    0.000000\n",
       "77    0.000000\n",
       "79    0.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['age', 'converted']].groupby('age').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yangzongkun/.conda/envs/untitled/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/Users/yangzongkun/.conda/envs/untitled/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped = df[['total_pages_visited', 'converted']].groupby('total_pages_visited').mean().reset_index()\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18, 6))\n",
    "sns.distplot(df[df['converted'] == 0]['total_pages_visited'], \n",
    "             label='Converted 0', ax=ax[0], hist_kws=hist_kws)\n",
    "sns.distplot(df[df['converted'] == 1]['total_pages_visited'], \n",
    "             label='Converted 1', ax=ax[0], hist_kws=hist_kws)\n",
    "ax[0].set_title('Count Plot of Age', fontsize=16)\n",
    "ax[0].legend()\n",
    "\n",
    "ax[1].plot(grouped['total_pages_visited'], grouped['converted'], '.-')\n",
    "ax[1].set_title('Mean Conversion Rate vs. Total_pages_visited', fontsize=16)\n",
    "ax[1].set_xlabel('total_pages_visited')\n",
    "ax[1].set_ylabel('Mean convertion rate')\n",
    "ax[1].grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0\n",
       "1         0\n",
       "2         0\n",
       "3         0\n",
       "4         0\n",
       "         ..\n",
       "316195    0\n",
       "316196    0\n",
       "316197    0\n",
       "316198    0\n",
       "316199    0\n",
       "Name: converted, Length: 316198, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df['converted']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "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</th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>source</th>\n",
       "      <th>total_pages_visited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UK</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>Ads</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>China</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316195</th>\n",
       "      <td>US</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Ads</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316196</th>\n",
       "      <td>US</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>Direct</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316197</th>\n",
       "      <td>China</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>Ads</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316198</th>\n",
       "      <td>US</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>Seo</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316199</th>\n",
       "      <td>UK</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>Ads</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>316198 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  age  new_user  source  total_pages_visited\n",
       "0           UK   25         1     Ads                    1\n",
       "1           US   23         1     Seo                    5\n",
       "2           US   28         1     Seo                    4\n",
       "3        China   39         1     Seo                    5\n",
       "4           US   30         1     Seo                    6\n",
       "...        ...  ...       ...     ...                  ...\n",
       "316195      US   26         1     Ads                    9\n",
       "316196      US   27         1  Direct                    6\n",
       "316197   China   25         0     Ads                    4\n",
       "316198      US   22         0     Seo                    4\n",
       "316199      UK   24         0     Ads                    4\n",
       "\n",
       "[316198 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_drop=['converted']\n",
    "x=df.drop(to_drop,axis=1)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training data has 237148 observation with 5 features\n",
      "test data has 79050 observation with 5 features\n"
     ]
    }
   ],
   "source": [
    "#split data\n",
    "\n",
    "from sklearn import model_selection\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(x, y, test_size=0.25, stratify = y, random_state=1) #stratified sampling\n",
    "print('training data has ' + str(X_train.shape[0]) + ' observation with ' + str(X_train.shape[1]) + ' features') #shape[0]指的是有多少行，1指的是有多少列\n",
    "print('test data has ' + str(X_test.shape[0]) + ' observation with ' + str(X_test.shape[1]) + ' features')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# One hot encoding\n",
    "# another way: get_dummies\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "def OneHotEncoding(df, enc, categories):  \n",
    "  transformed = pd.DataFrame(enc.transform(df[categories]).toarray(), columns=enc.get_feature_names(categories))\n",
    "  return pd.concat([df.reset_index(drop=True), transformed], axis=1).drop(categories, axis=1)\n",
    "\n",
    "categories = ['country']\n",
    "enc_ohe = OneHotEncoder()\n",
    "enc_ohe.fit(X_train[categories])\n",
    "\n",
    "X_train = OneHotEncoding(X_train, enc_ohe, categories)\n",
    "X_test = OneHotEncoding(X_test, enc_ohe, categories)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>source</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>country_China</th>\n",
       "      <th>country_Germany</th>\n",
       "      <th>country_UK</th>\n",
       "      <th>country_US</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>Direct</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>Ads</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>Seo</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>Seo</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  new_user  source  total_pages_visited  country_China  country_Germany  \\\n",
       "0   44         0  Direct                    8            0.0              0.0   \n",
       "1   23         1     Ads                    4            0.0              0.0   \n",
       "2   22         0     Seo                    2            1.0              0.0   \n",
       "3   21         0     Seo                    1            0.0              0.0   \n",
       "4   29         1     Seo                    2            0.0              0.0   \n",
       "\n",
       "   country_UK  country_US  \n",
       "0         1.0         0.0  \n",
       "1         1.0         0.0  \n",
       "2         0.0         0.0  \n",
       "3         0.0         1.0  \n",
       "4         1.0         0.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\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>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>source</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>country_China</th>\n",
       "      <th>country_Germany</th>\n",
       "      <th>country_UK</th>\n",
       "      <th>country_US</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>Direct</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Seo</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>Direct</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>Seo</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  new_user  source  total_pages_visited  country_China  country_Germany  \\\n",
       "0   23         1  Direct                    2            0.0              0.0   \n",
       "1   28         1     Seo                    2            0.0              0.0   \n",
       "2   35         0     Seo                    6            0.0              0.0   \n",
       "3   24         1  Direct                   11            0.0              0.0   \n",
       "4   45         0     Seo                    5            0.0              0.0   \n",
       "\n",
       "   country_UK  country_US  \n",
       "0         0.0         1.0  \n",
       "1         1.0         0.0  \n",
       "2         0.0         1.0  \n",
       "3         1.0         0.0  \n",
       "4         0.0         1.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One hot encoding\n",
    "# another way: get_dummies\n",
    "\n",
    "def OneHotEncoding(df, enc, categories):  \n",
    "  transformed = pd.DataFrame(enc.transform(df[categories]).toarray(), columns=enc.get_feature_names(categories))\n",
    "  return pd.concat([df.reset_index(drop=True), transformed], axis=1).drop(categories, axis=1)\n",
    "\n",
    "categories = ['source']\n",
    "enc_ohe1 = OneHotEncoder()\n",
    "enc_ohe1.fit(X_train[categories])\n",
    "\n",
    "X_train = OneHotEncoding(X_train, enc_ohe1, categories)\n",
    "X_test = OneHotEncoding(X_test, enc_ohe1, categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>country_China</th>\n",
       "      <th>country_Germany</th>\n",
       "      <th>country_UK</th>\n",
       "      <th>country_US</th>\n",
       "      <th>source_Ads</th>\n",
       "      <th>source_Direct</th>\n",
       "      <th>source_Seo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  new_user  total_pages_visited  country_China  country_Germany  \\\n",
       "0   44         0                    8            0.0              0.0   \n",
       "1   23         1                    4            0.0              0.0   \n",
       "2   22         0                    2            1.0              0.0   \n",
       "3   21         0                    1            0.0              0.0   \n",
       "4   29         1                    2            0.0              0.0   \n",
       "\n",
       "   country_UK  country_US  source_Ads  source_Direct  source_Seo  \n",
       "0         1.0         0.0         0.0            1.0         0.0  \n",
       "1         1.0         0.0         1.0            0.0         0.0  \n",
       "2         0.0         0.0         0.0            0.0         1.0  \n",
       "3         0.0         1.0         0.0            0.0         1.0  \n",
       "4         1.0         0.0         0.0            0.0         1.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>country_China</th>\n",
       "      <th>country_Germany</th>\n",
       "      <th>country_UK</th>\n",
       "      <th>country_US</th>\n",
       "      <th>source_Ads</th>\n",
       "      <th>source_Direct</th>\n",
       "      <th>source_Seo</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  new_user  total_pages_visited  country_China  country_Germany  \\\n",
       "0   23         1                    2            0.0              0.0   \n",
       "1   28         1                    2            0.0              0.0   \n",
       "2   35         0                    6            0.0              0.0   \n",
       "3   24         1                   11            0.0              0.0   \n",
       "4   45         0                    5            0.0              0.0   \n",
       "\n",
       "   country_UK  country_US  source_Ads  source_Direct  source_Seo  \n",
       "0         0.0         1.0         0.0            1.0         0.0  \n",
       "1         1.0         0.0         0.0            0.0         1.0  \n",
       "2         0.0         1.0         0.0            0.0         1.0  \n",
       "3         1.0         0.0         0.0            1.0         0.0  \n",
       "4         0.0         1.0         0.0            0.0         1.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'new_user', 'total_pages_visited'], dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols=x.columns[x.dtypes == 'int64']\n",
    "num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train[num_cols])\n",
    "X_train[num_cols] = scaler.transform(X_train[num_cols])\n",
    "X_test[num_cols] = scaler.transform(X_test[num_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>country_China</th>\n",
       "      <th>country_Germany</th>\n",
       "      <th>country_UK</th>\n",
       "      <th>country_US</th>\n",
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       "             age  new_user  total_pages_visited  country_China  \\\n",
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       "\n",
       "        country_Germany  country_UK  country_US  source_Ads  source_Direct  \\\n",
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       "1                   0.0         1.0         0.0         1.0            0.0   \n",
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       "3                   0.0         0.0         1.0         0.0            0.0   \n",
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       "...                 ...         ...         ...         ...            ...   \n",
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       "237147              0.0         0.0         1.0         1.0            0.0   \n",
       "\n",
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       "\n",
       "[237148 rows x 10 columns]"
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     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "            age  new_user  total_pages_visited  country_China  \\\n",
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       "2      0.536038 -1.473597             0.336477            0.0   \n",
       "3     -0.794606  0.678612             1.832274            0.0   \n",
       "4      1.745714 -1.473597             0.037317            0.0   \n",
       "...         ...       ...                  ...            ...   \n",
       "79045  1.261844  0.678612            -1.159320            0.0   \n",
       "79046  0.173135 -1.473597             0.037317            0.0   \n",
       "79047  1.019908 -1.473597            -0.261842            0.0   \n",
       "79048  0.415070 -1.473597            -1.159320            0.0   \n",
       "79049 -0.310736  0.678612            -0.561002            1.0   \n",
       "\n",
       "       country_Germany  country_UK  country_US  source_Ads  source_Direct  \\\n",
       "0                  0.0         0.0         1.0         0.0            1.0   \n",
       "1                  0.0         1.0         0.0         0.0            0.0   \n",
       "2                  0.0         0.0         1.0         0.0            0.0   \n",
       "3                  0.0         1.0         0.0         0.0            1.0   \n",
       "4                  0.0         0.0         1.0         0.0            0.0   \n",
       "...                ...         ...         ...         ...            ...   \n",
       "79045              0.0         0.0         1.0         0.0            0.0   \n",
       "79046              0.0         1.0         0.0         0.0            0.0   \n",
       "79047              0.0         0.0         1.0         1.0            0.0   \n",
       "79048              0.0         1.0         0.0         0.0            0.0   \n",
       "79049              0.0         0.0         0.0         1.0            0.0   \n",
       "\n",
       "       source_Seo  \n",
       "0             0.0  \n",
       "1             1.0  \n",
       "2             1.0  \n",
       "3             0.0  \n",
       "4             1.0  \n",
       "...           ...  \n",
       "79045         1.0  \n",
       "79046         1.0  \n",
       "79047         0.0  \n",
       "79048         1.0  \n",
       "79049         0.0  \n",
       "\n",
       "[79050 rows x 10 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training and result evaluation\n",
    "\n",
    "Two methods:\n",
    "\n",
    "1.from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "2.H20"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Method 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier_RF = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train the model\n",
    "classifier_RF.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction of test data\n",
    "classifier_RF.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9838203668564199"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_RF.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.98425047 0.98429264 0.98519924 0.98492484 0.98551519]\n",
      "Model accuracy of Random Forest is 0.9848364766604518\n"
     ]
    }
   ],
   "source": [
    "# Use 5-fold Cross Validation to get the accuracy for model\n",
    "model_names = ['Random Forest']\n",
    "model_list = [ classifier_RF]\n",
    "count = 0\n",
    "\n",
    "for classifier in model_list:\n",
    "    cv_score = model_selection.cross_val_score(classifier, X_train, y_train, cv=5)\n",
    "    print(cv_score)\n",
    "    print('Model accuracy of ' + model_names[count] + ' is ' + str(cv_score.mean()))\n",
    "    count += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>new_user</th>\n",
       "      <th>total_pages_visited</th>\n",
       "      <th>country_China</th>\n",
       "      <th>country_Germany</th>\n",
       "      <th>country_UK</th>\n",
       "      <th>country_US</th>\n",
       "      <th>source_Ads</th>\n",
       "      <th>source_Direct</th>\n",
       "      <th>source_Seo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  new_user  total_pages_visited  country_China  country_Germany  \\\n",
       "0   25         1                    1            0.0              0.0   \n",
       "1   23         1                    5            0.0              0.0   \n",
       "2   28         1                    4            0.0              0.0   \n",
       "3   39         1                    5            1.0              0.0   \n",
       "4   30         1                    6            0.0              0.0   \n",
       "\n",
       "   country_UK  country_US  source_Ads  source_Direct  source_Seo  \n",
       "0         1.0         0.0         1.0            0.0         0.0  \n",
       "1         0.0         1.0         0.0            0.0         1.0  \n",
       "2         0.0         1.0         0.0            0.0         1.0  \n",
       "3         0.0         0.0         0.0            0.0         1.0  \n",
       "4         0.0         1.0         0.0            0.0         1.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_RF = x.copy()\n",
    "\n",
    "X_RF = OneHotEncoding(X_RF, enc_ohe, ['country'])\n",
    "X_RF = OneHotEncoding(X_RF, enc_ohe1, ['source'])\n",
    "\n",
    "X_RF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importance ranking by Random Forest Model:\n",
      "total_pages_visited : 0.8217\n",
      "age : 0.1009\n",
      "new_user : 0.0386\n",
      "country_China : 0.0232\n",
      "country_US : 0.0038\n"
     ]
    }
   ],
   "source": [
    "# check feature importance of random forest for feature selection\n",
    "\n",
    "forest = RandomForestClassifier()\n",
    "forest.fit(X_RF, y)\n",
    "\n",
    "importances = forest.feature_importances_\n",
    "\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Print the feature ranking\n",
    "print(\"Feature importance ranking by Random Forest Model:\")\n",
    "for ind in range(x.shape[1]):\n",
    "  print (\"{0} : {1}\".format(X_RF.columns[indices[ind]],round(importances[indices[ind]], 4)))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Method 2: H20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h2o\n",
    "from h2o.frame import H2OFrame\n",
    "from h2o.estimators.random_forest import H2ORandomForestEstimator\n",
    "from h2o.grid.grid_search import H2OGridSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321 . connected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "\n",
       "#h2o-table-9.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-9 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-9 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-9 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-9 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-9 .h2o-table th,\n",
       "#h2o-table-9 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-9 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-9\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption></caption>\n",
       "    <thead></thead>\n",
       "    <tbody><tr><td>H2O_cluster_uptime:</td>\n",
       "<td>22 hours 40 mins</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>America/Los_Angeles</td></tr>\n",
       "<tr><td>H2O_data_parsing_timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.38.0.3</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>20 days </td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>H2O_from_python_yangzongkun_wzd7cz</td></tr>\n",
       "<tr><td>H2O_cluster_total_nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O_cluster_free_memory:</td>\n",
       "<td>1.430 Gb</td></tr>\n",
       "<tr><td>H2O_cluster_total_cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O_cluster_status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O_connection_url:</td>\n",
       "<td>http://localhost:54321</td></tr>\n",
       "<tr><td>H2O_connection_proxy:</td>\n",
       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
       "<tr><td>H2O_internal_security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>Python_version:</td>\n",
       "<td>3.9.6 final</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n"
      ],
      "text/plain": [
       "--------------------------  ----------------------------------\n",
       "H2O_cluster_uptime:         22 hours 40 mins\n",
       "H2O_cluster_timezone:       America/Los_Angeles\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.38.0.3\n",
       "H2O_cluster_version_age:    20 days\n",
       "H2O_cluster_name:           H2O_from_python_yangzongkun_wzd7cz\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    1.430 Gb\n",
       "H2O_cluster_total_cores:    8\n",
       "H2O_cluster_allowed_cores:  8\n",
       "H2O_cluster_status:         locked, healthy\n",
       "H2O_connection_url:         http://localhost:54321\n",
       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
       "H2O_internal_security:      False\n",
       "Python_version:             3.9.6 final\n",
       "--------------------------  ----------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize H2O cluster\n",
    "import h2o\n",
    "h2o.init()\n",
    "h2o.remove_all()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/kj/gjbr9xrs6ns8dc9jgs1f619h0000gn/T/ipykernel_29041/429283149.py:7: H2ODeprecationWarning: ``summary()`` is deprecated, please use ``show_summary()`` or ``get_summary()`` instead\n",
      "  h2o_df.summary()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class='dataframe'>\n",
       "<thead>\n",
       "<tr><th>       </th><th>country  </th><th>age              </th><th>new_user  </th><th>source  </th><th>total_pages_visited  </th><th>converted  </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>type   </td><td>enum     </td><td>int              </td><td>enum      </td><td>enum    </td><td>int                  </td><td>enum       </td></tr>\n",
       "<tr><td>mins   </td><td>         </td><td>17.0             </td><td>          </td><td>        </td><td>1.0                  </td><td>           </td></tr>\n",
       "<tr><td>mean   </td><td>         </td><td>30.56931100133461</td><td>          </td><td>        </td><td>4.872918234777001    </td><td>           </td></tr>\n",
       "<tr><td>maxs   </td><td>         </td><td>79.0             </td><td>          </td><td>        </td><td>29.0                 </td><td>           </td></tr>\n",
       "<tr><td>sigma  </td><td>         </td><td>8.268957596421435</td><td>          </td><td>        </td><td>3.3410533442156285   </td><td>           </td></tr>\n",
       "<tr><td>zeros  </td><td>         </td><td>0                </td><td>          </td><td>        </td><td>0                    </td><td>           </td></tr>\n",
       "<tr><td>missing</td><td>0        </td><td>0                </td><td>0         </td><td>0       </td><td>0                    </td><td>0          </td></tr>\n",
       "<tr><td>0      </td><td>UK       </td><td>25.0             </td><td>1         </td><td>Ads     </td><td>1.0                  </td><td>0          </td></tr>\n",
       "<tr><td>1      </td><td>US       </td><td>23.0             </td><td>1         </td><td>Seo     </td><td>5.0                  </td><td>0          </td></tr>\n",
       "<tr><td>2      </td><td>US       </td><td>28.0             </td><td>1         </td><td>Seo     </td><td>4.0                  </td><td>0          </td></tr>\n",
       "<tr><td>3      </td><td>China    </td><td>39.0             </td><td>1         </td><td>Seo     </td><td>5.0                  </td><td>0          </td></tr>\n",
       "<tr><td>4      </td><td>US       </td><td>30.0             </td><td>1         </td><td>Seo     </td><td>6.0                  </td><td>0          </td></tr>\n",
       "<tr><td>5      </td><td>US       </td><td>31.0             </td><td>0         </td><td>Seo     </td><td>1.0                  </td><td>0          </td></tr>\n",
       "<tr><td>6      </td><td>China    </td><td>27.0             </td><td>1         </td><td>Seo     </td><td>4.0                  </td><td>0          </td></tr>\n",
       "<tr><td>7      </td><td>US       </td><td>23.0             </td><td>0         </td><td>Ads     </td><td>4.0                  </td><td>0          </td></tr>\n",
       "<tr><td>8      </td><td>UK       </td><td>29.0             </td><td>0         </td><td>Direct  </td><td>4.0                  </td><td>0          </td></tr>\n",
       "<tr><td>9      </td><td>US       </td><td>25.0             </td><td>0         </td><td>Ads     </td><td>2.0                  </td><td>0          </td></tr>\n",
       "</tbody>\n",
       "</table><pre style='font-size: smaller; margin-bottom: 1em;'>[316198 rows x 6 columns]</pre>"
      ],
      "text/plain": [
       "         country    age                new_user    source    total_pages_visited    converted\n",
       "-------  ---------  -----------------  ----------  --------  ---------------------  -----------\n",
       "type     enum       int                enum        enum      int                    enum\n",
       "mins                17.0                                     1.0\n",
       "mean                30.56931100133461                        4.872918234777001\n",
       "maxs                79.0                                     29.0\n",
       "sigma               8.268957596421435                        3.3410533442156285\n",
       "zeros               0                                        0\n",
       "missing  0          0                  0           0         0                      0\n",
       "0        UK         25.0               1           Ads       1.0                    0\n",
       "1        US         23.0               1           Seo       5.0                    0\n",
       "2        US         28.0               1           Seo       4.0                    0\n",
       "3        China      39.0               1           Seo       5.0                    0\n",
       "4        US         30.0               1           Seo       6.0                    0\n",
       "5        US         31.0               0           Seo       1.0                    0\n",
       "6        China      27.0               1           Seo       4.0                    0\n",
       "7        US         23.0               0           Ads       4.0                    0\n",
       "8        UK         29.0               0           Direct    4.0                    0\n",
       "9        US         25.0               0           Ads       2.0                    0\n",
       "[316198 rows x 6 columns]\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Transform to H2O Frame, and make sure the target variable is categorical\n",
    "h2o_df = H2OFrame(df)\n",
    "\n",
    "h2o_df['new_user'] = h2o_df['new_user'].asfactor()\n",
    "h2o_df['converted'] = h2o_df['converted'].asfactor()\n",
    "\n",
    "h2o_df.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split into 75% training and 25% test dataset\n",
    "strat_split = h2o_df['converted'].stratified_split(test_frac=0.25, seed=42)\n",
    "\n",
    "train = h2o_df[strat_split == 'train']\n",
    "test = h2o_df[strat_split == 'test']\n",
    "\n",
    "feature = ['country', 'age', 'new_user', 'source', 'total_pages_visited']\n",
    "target = 'converted'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drf Model Build progress: |██████████████████████████████████████████████████████| (done) 100%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style='margin: 1em 0 1em 0;'>Model Details\n",
       "=============\n",
       "H2ORandomForestEstimator : Distributed Random Forest\n",
       "Model Key: DRF_model_python_1670902059772_204\n",
       "</pre>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-13.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-13 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-13 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-13 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-13 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-13 .h2o-table th,\n",
       "#h2o-table-13 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-13 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-13\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Model Summary: </caption>\n",
       "    <thead><tr><th></th>\n",
       "<th>number_of_trees</th>\n",
       "<th>number_of_internal_trees</th>\n",
       "<th>model_size_in_bytes</th>\n",
       "<th>min_depth</th>\n",
       "<th>max_depth</th>\n",
       "<th>mean_depth</th>\n",
       "<th>min_leaves</th>\n",
       "<th>max_leaves</th>\n",
       "<th>mean_leaves</th></tr></thead>\n",
       "    <tbody><tr><td></td>\n",
       "<td>100.0</td>\n",
       "<td>100.0</td>\n",
       "<td>3204083.0</td>\n",
       "<td>19.0</td>\n",
       "<td>20.0</td>\n",
       "<td>19.99</td>\n",
       "<td>1677.0</td>\n",
       "<td>2989.0</td>\n",
       "<td>2542.26</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'><pre style='margin: 1em 0 1em 0;'>ModelMetricsBinomial: drf\n",
       "** Reported on train data. **\n",
       "\n",
       "MSE: 0.11846613631537281\n",
       "RMSE: 0.3441890996463613\n",
       "LogLoss: 0.3764399306371099\n",
       "Mean Per-Class Error: 0.04077296202014859\n",
       "AUC: 0.9928548908904044\n",
       "AUCPR: 0.9916492588930318\n",
       "Gini: 0.9857097817808087</pre>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-14.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-14 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-14 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-14 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-14 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-14 .h2o-table th,\n",
       "#h2o-table-14 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-14 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-14\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.02780413173063362</caption>\n",
       "    <thead><tr><th></th>\n",
       "<th>0</th>\n",
       "<th>1</th>\n",
       "<th>Error</th>\n",
       "<th>Rate</th></tr></thead>\n",
       "    <tbody><tr><td>0</td>\n",
       "<td>216822.0</td>\n",
       "<td>12678.0</td>\n",
       "<td>0.0552</td>\n",
       "<td> (12678.0/229500.0)</td></tr>\n",
       "<tr><td>1</td>\n",
       "<td>6037.0</td>\n",
       "<td>223471.0</td>\n",
       "<td>0.0263</td>\n",
       "<td> (6037.0/229508.0)</td></tr>\n",
       "<tr><td>Total</td>\n",
       "<td>222859.0</td>\n",
       "<td>236149.0</td>\n",
       "<td>0.0408</td>\n",
       "<td> (18715.0/459008.0)</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-15.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-15 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-15 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-15 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-15 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-15 .h2o-table th,\n",
       "#h2o-table-15 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-15 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-15\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Maximum Metrics: Maximum metrics at their respective thresholds</caption>\n",
       "    <thead><tr><th>metric</th>\n",
       "<th>threshold</th>\n",
       "<th>value</th>\n",
       "<th>idx</th></tr></thead>\n",
       "    <tbody><tr><td>max f1</td>\n",
       "<td>0.0278041</td>\n",
       "<td>0.9598095</td>\n",
       "<td>378.0</td></tr>\n",
       "<tr><td>max f2</td>\n",
       "<td>0.0096906</td>\n",
       "<td>0.9775724</td>\n",
       "<td>389.0</td></tr>\n",
       "<tr><td>max f0point5</td>\n",
       "<td>0.0865393</td>\n",
       "<td>0.9619801</td>\n",
       "<td>352.0</td></tr>\n",
       "<tr><td>max accuracy</td>\n",
       "<td>0.0292331</td>\n",
       "<td>0.9592534</td>\n",
       "<td>377.0</td></tr>\n",
       "<tr><td>max precision</td>\n",
       "<td>0.9907184</td>\n",
       "<td>0.9994933</td>\n",
       "<td>6.0</td></tr>\n",
       "<tr><td>max recall</td>\n",
       "<td>0.0027311</td>\n",
       "<td>1.0</td>\n",
       "<td>394.0</td></tr>\n",
       "<tr><td>max specificity</td>\n",
       "<td>0.9994815</td>\n",
       "<td>0.9999956</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max absolute_mcc</td>\n",
       "<td>0.0278041</td>\n",
       "<td>0.9188393</td>\n",
       "<td>378.0</td></tr>\n",
       "<tr><td>max min_per_class_accuracy</td>\n",
       "<td>0.0452861</td>\n",
       "<td>0.9577052</td>\n",
       "<td>370.0</td></tr>\n",
       "<tr><td>max mean_per_class_accuracy</td>\n",
       "<td>0.0292331</td>\n",
       "<td>0.9592532</td>\n",
       "<td>377.0</td></tr>\n",
       "<tr><td>max tns</td>\n",
       "<td>0.9994815</td>\n",
       "<td>229499.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fns</td>\n",
       "<td>0.9994815</td>\n",
       "<td>228280.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fps</td>\n",
       "<td>0.0000684</td>\n",
       "<td>229500.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max tps</td>\n",
       "<td>0.0027311</td>\n",
       "<td>229508.0</td>\n",
       "<td>394.0</td></tr>\n",
       "<tr><td>max tnr</td>\n",
       "<td>0.9994815</td>\n",
       "<td>0.9999956</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fnr</td>\n",
       "<td>0.9994815</td>\n",
       "<td>0.9946494</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max fpr</td>\n",
       "<td>0.0000684</td>\n",
       "<td>1.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max tpr</td>\n",
       "<td>0.0027311</td>\n",
       "<td>1.0</td>\n",
       "<td>394.0</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-16.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-16 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-16 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-16 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-16 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-16 .h2o-table th,\n",
       "#h2o-table-16 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-16 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-16\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Gains/Lift Table: Avg response rate: 50.00 %, avg score: 31.94 %</caption>\n",
       "    <thead><tr><th>group</th>\n",
       "<th>cumulative_data_fraction</th>\n",
       "<th>lower_threshold</th>\n",
       "<th>lift</th>\n",
       "<th>cumulative_lift</th>\n",
       "<th>response_rate</th>\n",
       "<th>score</th>\n",
       "<th>cumulative_response_rate</th>\n",
       "<th>cumulative_score</th>\n",
       "<th>capture_rate</th>\n",
       "<th>cumulative_capture_rate</th>\n",
       "<th>gain</th>\n",
       "<th>cumulative_gain</th>\n",
       "<th>kolmogorov_smirnov</th></tr></thead>\n",
       "    <tbody><tr><td>1</td>\n",
       "<td>0.0100085</td>\n",
       "<td>0.9948433</td>\n",
       "<td>1.9982238</td>\n",
       "<td>1.9982238</td>\n",
       "<td>0.9991293</td>\n",
       "<td>0.9975616</td>\n",
       "<td>0.9991293</td>\n",
       "<td>0.9975616</td>\n",
       "<td>0.0199993</td>\n",
       "<td>0.0199993</td>\n",
       "<td>99.8223771</td>\n",
       "<td>99.8223771</td>\n",
       "<td>0.0199819</td></tr>\n",
       "<tr><td>2</td>\n",
       "<td>0.0200018</td>\n",
       "<td>0.9906644</td>\n",
       "<td>1.9995291</td>\n",
       "<td>1.9988760</td>\n",
       "<td>0.9997820</td>\n",
       "<td>0.9927525</td>\n",
       "<td>0.9994554</td>\n",
       "<td>0.9951589</td>\n",
       "<td>0.0199819</td>\n",
       "<td>0.0399812</td>\n",
       "<td>99.9529136</td>\n",
       "<td>99.8875956</td>\n",
       "<td>0.0399594</td></tr>\n",
       "<tr><td>3</td>\n",
       "<td>0.0300038</td>\n",
       "<td>0.9859295</td>\n",
       "<td>1.9977870</td>\n",
       "<td>1.9985129</td>\n",
       "<td>0.9989109</td>\n",
       "<td>0.9883177</td>\n",
       "<td>0.9992739</td>\n",
       "<td>0.9928783</td>\n",
       "<td>0.0199819</td>\n",
       "<td>0.0599631</td>\n",
       "<td>99.7787006</td>\n",
       "<td>99.8512946</td>\n",
       "<td>0.0599195</td></tr>\n",
       "<tr><td>4</td>\n",
       "<td>0.0400015</td>\n",
       "<td>0.9814199</td>\n",
       "<td>1.9986577</td>\n",
       "<td>1.9985491</td>\n",
       "<td>0.9993463</td>\n",
       "<td>0.9836415</td>\n",
       "<td>0.9992920</td>\n",
       "<td>0.9905698</td>\n",
       "<td>0.0199819</td>\n",
       "<td>0.0799449</td>\n",
       "<td>99.8657691</td>\n",
       "<td>99.8549123</td>\n",
       "<td>0.0798883</td></tr>\n",
       "<tr><td>5</td>\n",
       "<td>0.0500013</td>\n",
       "<td>0.9769216</td>\n",
       "<td>1.9982223</td>\n",
       "<td>1.9984838</td>\n",
       "<td>0.9991285</td>\n",
       "<td>0.9792072</td>\n",
       "<td>0.9992593</td>\n",
       "<td>0.9882973</td>\n",
       "<td>0.0199819</td>\n",
       "<td>0.0999268</td>\n",
       "<td>99.8222254</td>\n",
       "<td>99.8483752</td>\n",
       "<td>0.0998527</td></tr>\n",
       "<tr><td>6</td>\n",
       "<td>0.1000004</td>\n",
       "<td>0.9359620</td>\n",
       "<td>1.9954336</td>\n",
       "<td>1.9969587</td>\n",
       "<td>0.9977342</td>\n",
       "<td>0.9602038</td>\n",
       "<td>0.9984968</td>\n",
       "<td>0.9742509</td>\n",
       "<td>0.0997699</td>\n",
       "<td>0.1996967</td>\n",
       "<td>99.5433631</td>\n",
       "<td>99.6958725</td>\n",
       "<td>0.1993961</td></tr>\n",
       "<tr><td>7</td>\n",
       "<td>0.1500017</td>\n",
       "<td>0.8637004</td>\n",
       "<td>1.9922968</td>\n",
       "<td>1.9954047</td>\n",
       "<td>0.9961657</td>\n",
       "<td>0.9007729</td>\n",
       "<td>0.9977197</td>\n",
       "<td>0.9497579</td>\n",
       "<td>0.0996174</td>\n",
       "<td>0.2993142</td>\n",
       "<td>99.2296765</td>\n",
       "<td>99.5404716</td>\n",
       "<td>0.2986301</td></tr>\n",
       "<tr><td>8</td>\n",
       "<td>0.2000009</td>\n",
       "<td>0.8016984</td>\n",
       "<td>1.9931679</td>\n",
       "<td>1.9948455</td>\n",
       "<td>0.9966013</td>\n",
       "<td>0.8316166</td>\n",
       "<td>0.9974401</td>\n",
       "<td>0.9202232</td>\n",
       "<td>0.0996567</td>\n",
       "<td>0.3989708</td>\n",
       "<td>99.3167876</td>\n",
       "<td>99.4845518</td>\n",
       "<td>0.3979469</td></tr>\n",
       "<tr><td>9</td>\n",
       "<td>0.3000013</td>\n",
       "<td>0.6165588</td>\n",
       "<td>1.9872423</td>\n",
       "<td>1.9923111</td>\n",
       "<td>0.9936385</td>\n",
       "<td>0.7134582</td>\n",
       "<td>0.9961729</td>\n",
       "<td>0.8513015</td>\n",
       "<td>0.1987251</td>\n",
       "<td>0.5976959</td>\n",
       "<td>98.7242330</td>\n",
       "<td>99.2311122</td>\n",
       "<td>0.5953996</td></tr>\n",
       "<tr><td>10</td>\n",
       "<td>0.3999996</td>\n",
       "<td>0.3033928</td>\n",
       "<td>1.9482013</td>\n",
       "<td>1.9812839</td>\n",
       "<td>0.9741176</td>\n",
       "<td>0.4757438</td>\n",
       "<td>0.9906592</td>\n",
       "<td>0.7574136</td>\n",
       "<td>0.1948167</td>\n",
       "<td>0.7925127</td>\n",
       "<td>94.8201339</td>\n",
       "<td>98.1283856</td>\n",
       "<td>0.7850399</td></tr>\n",
       "<tr><td>11</td>\n",
       "<td>0.5</td>\n",
       "<td>0.0434454</td>\n",
       "<td>1.6580178</td>\n",
       "<td>1.9166304</td>\n",
       "<td>0.8290233</td>\n",
       "<td>0.1516947</td>\n",
       "<td>0.9583319</td>\n",
       "<td>0.6362693</td>\n",
       "<td>0.1658025</td>\n",
       "<td>0.9583152</td>\n",
       "<td>65.8017768</td>\n",
       "<td>91.6630357</td>\n",
       "<td>0.9166463</td></tr>\n",
       "<tr><td>12</td>\n",
       "<td>0.6000004</td>\n",
       "<td>0.0011933</td>\n",
       "<td>0.4168464</td>\n",
       "<td>1.6666655</td>\n",
       "<td>0.2084268</td>\n",
       "<td>0.0119604</td>\n",
       "<td>0.8333473</td>\n",
       "<td>0.5322175</td>\n",
       "<td>0.0416848</td>\n",
       "<td>1.0</td>\n",
       "<td>-58.3153602</td>\n",
       "<td>66.6665456</td>\n",
       "<td>0.8000131</td></tr>\n",
       "<tr><td>13</td>\n",
       "<td>0.6999987</td>\n",
       "<td>0.0004117</td>\n",
       "<td>0.0</td>\n",
       "<td>1.4285741</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0006878</td>\n",
       "<td>0.7142995</td>\n",
       "<td>0.4562858</td>\n",
       "<td>0.0</td>\n",
       "<td>1.0</td>\n",
       "<td>-100.0</td>\n",
       "<td>42.8574096</td>\n",
       "<td>0.6000131</td></tr>\n",
       "<tr><td>14</td>\n",
       "<td>0.7999991</td>\n",
       "<td>0.0001462</td>\n",
       "<td>0.0</td>\n",
       "<td>1.2500014</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0002746</td>\n",
       "<td>0.6250116</td>\n",
       "<td>0.3992841</td>\n",
       "<td>0.0</td>\n",
       "<td>1.0</td>\n",
       "<td>-100.0</td>\n",
       "<td>25.0001362</td>\n",
       "<td>0.4000087</td></tr>\n",
       "<tr><td>15</td>\n",
       "<td>0.8999996</td>\n",
       "<td>0.0000534</td>\n",
       "<td>0.0</td>\n",
       "<td>1.1111116</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0000882</td>\n",
       "<td>0.5555655</td>\n",
       "<td>0.3549288</td>\n",
       "<td>0.0</td>\n",
       "<td>1.0</td>\n",
       "<td>-100.0</td>\n",
       "<td>11.1111649</td>\n",
       "<td>0.2000044</td></tr>\n",
       "<tr><td>16</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0000265</td>\n",
       "<td>0.5000087</td>\n",
       "<td>0.3194384</td>\n",
       "<td>0.0</td>\n",
       "<td>1.0</td>\n",
       "<td>-100.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div></div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-17.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-17 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-17 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-17 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-17 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-17 .h2o-table th,\n",
       "#h2o-table-17 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-17 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-17\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Scoring History: </caption>\n",
       "    <thead><tr><th></th>\n",
       "<th>timestamp</th>\n",
       "<th>duration</th>\n",
       "<th>number_of_trees</th>\n",
       "<th>training_rmse</th>\n",
       "<th>training_logloss</th>\n",
       "<th>training_auc</th>\n",
       "<th>training_pr_auc</th>\n",
       "<th>training_lift</th>\n",
       "<th>training_classification_error</th></tr></thead>\n",
       "    <tbody><tr><td></td>\n",
       "<td>2022-12-13 18:12:13</td>\n",
       "<td> 0.030 sec</td>\n",
       "<td>0.0</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2022-12-13 18:12:13</td>\n",
       "<td> 0.344 sec</td>\n",
       "<td>1.0</td>\n",
       "<td>0.3162110</td>\n",
       "<td>0.3588505</td>\n",
       "<td>0.9911986</td>\n",
       "<td>0.9883076</td>\n",
       "<td>1.9887309</td>\n",
       "<td>0.0438738</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2022-12-13 18:12:14</td>\n",
       "<td> 0.551 sec</td>\n",
       "<td>2.0</td>\n",
       "<td>0.3159232</td>\n",
       "<td>0.3580166</td>\n",
       "<td>0.9913582</td>\n",
       "<td>0.9887040</td>\n",
       "<td>1.9896579</td>\n",
       "<td>0.0437469</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2022-12-13 18:12:14</td>\n",
       "<td> 0.880 sec</td>\n",
       "<td>3.0</td>\n",
       "<td>0.3313859</td>\n",
       "<td>0.3729711</td>\n",
       "<td>0.9911432</td>\n",
       "<td>0.9886011</td>\n",
       "<td>1.9905182</td>\n",
       "<td>0.0436225</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2022-12-13 18:12:14</td>\n",
       "<td> 1.258 sec</td>\n",
       "<td>4.0</td>\n",
       "<td>0.3288490</td>\n",
       "<td>0.3685114</td>\n",
       "<td>0.9913374</td>\n",
       "<td>0.9886992</td>\n",
       "<td>1.9899657</td>\n",
       "<td>0.0433002</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2022-12-13 18:12:15</td>\n",
       "<td> 1.788 sec</td>\n",
       "<td>5.0</td>\n",
       "<td>0.3280259</td>\n",
       "<td>0.3635734</td>\n",
       "<td>0.9916428</td>\n",
       "<td>0.9892951</td>\n",
       "<td>1.9911390</td>\n",
       "<td>0.0428413</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2022-12-13 18:12:15</td>\n",
       "<td> 2.164 sec</td>\n",
       "<td>6.0</td>\n",
       "<td>0.3271493</td>\n",
       "<td>0.3585915</td>\n",
       "<td>0.9918819</td>\n",
       "<td>0.9896843</td>\n",
       "<td>1.9918526</td>\n",
       "<td>0.0423741</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2022-12-13 18:12:16</td>\n",
       "<td> 2.573 sec</td>\n",
       "<td>7.0</td>\n",
       "<td>0.3262285</td>\n",
       "<td>0.3552593</td>\n",
       "<td>0.9920296</td>\n",
       "<td>0.9899171</td>\n",
       "<td>1.9922184</td>\n",
       "<td>0.0422072</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2022-12-13 18:12:16</td>\n",
       "<td> 3.001 sec</td>\n",
       "<td>8.0</td>\n",
       "<td>0.3252960</td>\n",
       "<td>0.3516888</td>\n",
       "<td>0.9921699</td>\n",
       "<td>0.9901296</td>\n",
       "<td>1.9925431</td>\n",
       "<td>0.0419912</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2022-12-13 18:12:17</td>\n",
       "<td> 3.385 sec</td>\n",
       "<td>9.0</td>\n",
       "<td>0.3262821</td>\n",
       "<td>0.3537217</td>\n",
       "<td>0.9921888</td>\n",
       "<td>0.9901381</td>\n",
       "<td>1.9924148</td>\n",
       "<td>0.0417552</td></tr>\n",
       "<tr><td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td>\n",
       "<td>---</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2022-12-13 18:12:44</td>\n",
       "<td>30.705 sec</td>\n",
       "<td>91.0</td>\n",
       "<td>0.3460256</td>\n",
       "<td>0.3794722</td>\n",
       "<td>0.9928385</td>\n",
       "<td>0.9916029</td>\n",
       "<td>1.9990939</td>\n",
       "<td>0.0408141</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2022-12-13 18:12:44</td>\n",
       "<td>31.038 sec</td>\n",
       "<td>92.0</td>\n",
       "<td>0.3458157</td>\n",
       "<td>0.3791215</td>\n",
       "<td>0.9928385</td>\n",
       "<td>0.9916044</td>\n",
       "<td>1.9990941</td>\n",
       "<td>0.0408228</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2022-12-13 18:12:45</td>\n",
       "<td>31.366 sec</td>\n",
       "<td>93.0</td>\n",
       "<td>0.3454589</td>\n",
       "<td>0.3784573</td>\n",
       "<td>0.9928453</td>\n",
       "<td>0.9916141</td>\n",
       "<td>1.9990939</td>\n",
       "<td>0.0408250</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2022-12-13 18:12:45</td>\n",
       "<td>31.702 sec</td>\n",
       "<td>94.0</td>\n",
       "<td>0.3453832</td>\n",
       "<td>0.3783277</td>\n",
       "<td>0.9928416</td>\n",
       "<td>0.9916135</td>\n",
       "<td>1.9990939</td>\n",
       "<td>0.0408446</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2022-12-13 18:12:45</td>\n",
       "<td>32.111 sec</td>\n",
       "<td>95.0</td>\n",
       "<td>0.3452747</td>\n",
       "<td>0.3781736</td>\n",
       "<td>0.9928447</td>\n",
       "<td>0.9916242</td>\n",
       "<td>1.9990939</td>\n",
       "<td>0.0408163</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2022-12-13 18:12:46</td>\n",
       "<td>32.449 sec</td>\n",
       "<td>96.0</td>\n",
       "<td>0.3449550</td>\n",
       "<td>0.3775565</td>\n",
       "<td>0.9928463</td>\n",
       "<td>0.9916321</td>\n",
       "<td>1.9990941</td>\n",
       "<td>0.0408664</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2022-12-13 18:12:46</td>\n",
       "<td>32.775 sec</td>\n",
       "<td>97.0</td>\n",
       "<td>0.3447268</td>\n",
       "<td>0.3771976</td>\n",
       "<td>0.9928598</td>\n",
       "<td>0.9916429</td>\n",
       "<td>1.9990939</td>\n",
       "<td>0.0407444</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2022-12-13 18:12:46</td>\n",
       "<td>33.067 sec</td>\n",
       "<td>98.0</td>\n",
       "<td>0.3444350</td>\n",
       "<td>0.3767315</td>\n",
       "<td>0.9928568</td>\n",
       "<td>0.9916389</td>\n",
       "<td>1.9990939</td>\n",
       "<td>0.0407640</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2022-12-13 18:12:47</td>\n",
       "<td>33.374 sec</td>\n",
       "<td>99.0</td>\n",
       "<td>0.3441849</td>\n",
       "<td>0.3763018</td>\n",
       "<td>0.9928567</td>\n",
       "<td>0.9916553</td>\n",
       "<td>1.9986583</td>\n",
       "<td>0.0409296</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2022-12-13 18:12:47</td>\n",
       "<td>33.664 sec</td>\n",
       "<td>100.0</td>\n",
       "<td>0.3441891</td>\n",
       "<td>0.3764399</td>\n",
       "<td>0.9928549</td>\n",
       "<td>0.9916493</td>\n",
       "<td>1.9982238</td>\n",
       "<td>0.0407727</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "<pre style='font-size: smaller; margin-bottom: 1em;'>[101 rows x 10 columns]</pre></div>\n",
       "<div style='margin: 1em 0 1em 0;'>\n",
       "<style>\n",
       "\n",
       "#h2o-table-18.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-18 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-18 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-18 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-18 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-18 .h2o-table th,\n",
       "#h2o-table-18 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-18 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-18\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption>Variable Importances: </caption>\n",
       "    <thead><tr><th>variable</th>\n",
       "<th>relative_importance</th>\n",
       "<th>scaled_importance</th>\n",
       "<th>percentage</th></tr></thead>\n",
       "    <tbody><tr><td>total_pages_visited</td>\n",
       "<td>6009332.0</td>\n",
       "<td>1.0</td>\n",
       "<td>0.7693529</td></tr>\n",
       "<tr><td>new_user</td>\n",
       "<td>837951.3125000</td>\n",
       "<td>0.1394417</td>\n",
       "<td>0.1072799</td></tr>\n",
       "<tr><td>country</td>\n",
       "<td>524928.3125000</td>\n",
       "<td>0.0873522</td>\n",
       "<td>0.0672047</td></tr>\n",
       "<tr><td>age</td>\n",
       "<td>366418.9375000</td>\n",
       "<td>0.0609750</td>\n",
       "<td>0.0469113</td></tr>\n",
       "<tr><td>source</td>\n",
       "<td>72260.7109375</td>\n",
       "<td>0.0120247</td>\n",
       "<td>0.0092513</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n",
       "</div><pre style=\"font-size: smaller; margin: 1em 0 0 0;\">\n",
       "\n",
       "[tips]\n",
       "Use `model.explain()` to inspect the model.\n",
       "--\n",
       "Use `h2o.display.toggle_user_tips()` to switch on/off this section.</pre>"
      ],
      "text/plain": [
       "Model Details\n",
       "=============\n",
       "H2ORandomForestEstimator : Distributed Random Forest\n",
       "Model Key: DRF_model_python_1670902059772_204\n",
       "\n",
       "\n",
       "Model Summary: \n",
       "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
       "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
       "    100                100                         3.20408e+06            19           20           19.99         1677          2989          2542.26\n",
       "\n",
       "ModelMetricsBinomial: drf\n",
       "** Reported on train data. **\n",
       "\n",
       "MSE: 0.11846613631537281\n",
       "RMSE: 0.3441890996463613\n",
       "LogLoss: 0.3764399306371099\n",
       "Mean Per-Class Error: 0.04077296202014859\n",
       "AUC: 0.9928548908904044\n",
       "AUCPR: 0.9916492588930318\n",
       "Gini: 0.9857097817808087\n",
       "\n",
       "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.02780413173063362\n",
       "       0       1       Error    Rate\n",
       "-----  ------  ------  -------  ------------------\n",
       "0      216822  12678   0.0552   (12678.0/229500.0)\n",
       "1      6037    223471  0.0263   (6037.0/229508.0)\n",
       "Total  222859  236149  0.0408   (18715.0/459008.0)\n",
       "\n",
       "Maximum Metrics: Maximum metrics at their respective thresholds\n",
       "metric                       threshold    value     idx\n",
       "---------------------------  -----------  --------  -----\n",
       "max f1                       0.0278041    0.959809  378\n",
       "max f2                       0.00969061   0.977572  389\n",
       "max f0point5                 0.0865393    0.96198   352\n",
       "max accuracy                 0.0292331    0.959253  377\n",
       "max precision                0.990718     0.999493  6\n",
       "max recall                   0.00273107   1         394\n",
       "max specificity              0.999482     0.999996  0\n",
       "max absolute_mcc             0.0278041    0.918839  378\n",
       "max min_per_class_accuracy   0.0452861    0.957705  370\n",
       "max mean_per_class_accuracy  0.0292331    0.959253  377\n",
       "max tns                      0.999482     229499    0\n",
       "max fns                      0.999482     228280    0\n",
       "max fps                      6.84159e-05  229500    399\n",
       "max tps                      0.00273107   229508    394\n",
       "max tnr                      0.999482     0.999996  0\n",
       "max fnr                      0.999482     0.994649  0\n",
       "max fpr                      6.84159e-05  1         399\n",
       "max tpr                      0.00273107   1         394\n",
       "\n",
       "Gains/Lift Table: Avg response rate: 50.00 %, avg score: 31.94 %\n",
       "group    cumulative_data_fraction    lower_threshold    lift      cumulative_lift    response_rate    score        cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain      cumulative_gain    kolmogorov_smirnov\n",
       "-------  --------------------------  -----------------  --------  -----------------  ---------------  -----------  --------------------------  ------------------  --------------  -------------------------  --------  -----------------  --------------------\n",
       "1        0.0100085                   0.994843           1.99822   1.99822            0.999129         0.997562     0.999129                    0.997562            0.0199993       0.0199993                  99.8224   99.8224            0.0199819\n",
       "2        0.0200018                   0.990664           1.99953   1.99888            0.999782         0.992752     0.999455                    0.995159            0.0199819       0.0399812                  99.9529   99.8876            0.0399594\n",
       "3        0.0300038                   0.985929           1.99779   1.99851            0.998911         0.988318     0.999274                    0.992878            0.0199819       0.0599631                  99.7787   99.8513            0.0599195\n",
       "4        0.0400015                   0.98142            1.99866   1.99855            0.999346         0.983642     0.999292                    0.99057             0.0199819       0.0799449                  99.8658   99.8549            0.0798883\n",
       "5        0.0500013                   0.976922           1.99822   1.99848            0.999129         0.979207     0.999259                    0.988297            0.0199819       0.0999268                  99.8222   99.8484            0.0998527\n",
       "6        0.1                         0.935962           1.99543   1.99696            0.997734         0.960204     0.998497                    0.974251            0.0997699       0.199697                   99.5434   99.6959            0.199396\n",
       "7        0.150002                    0.8637             1.9923    1.9954             0.996166         0.900773     0.99772                     0.949758            0.0996174       0.299314                   99.2297   99.5405            0.29863\n",
       "8        0.200001                    0.801698           1.99317   1.99485            0.996601         0.831617     0.99744                     0.920223            0.0996567       0.398971                   99.3168   99.4846            0.397947\n",
       "9        0.300001                    0.616559           1.98724   1.99231            0.993638         0.713458     0.996173                    0.851302            0.198725        0.597696                   98.7242   99.2311            0.5954\n",
       "10       0.4                         0.303393           1.9482    1.98128            0.974118         0.475744     0.990659                    0.757414            0.194817        0.792513                   94.8201   98.1284            0.78504\n",
       "11       0.5                         0.0434454          1.65802   1.91663            0.829023         0.151695     0.958332                    0.636269            0.165802        0.958315                   65.8018   91.663             0.916646\n",
       "12       0.6                         0.00119328         0.416846  1.66667            0.208427         0.0119604    0.833347                    0.532217            0.0416848       1                          -58.3154  66.6665            0.800013\n",
       "13       0.699999                    0.000411679        0         1.42857            0                0.000687816  0.714299                    0.456286            0               1                          -100      42.8574            0.600013\n",
       "14       0.799999                    0.000146202        0         1.25               0                0.000274577  0.625012                    0.399284            0               1                          -100      25.0001            0.400009\n",
       "15       0.9                         5.33874e-05        0         1.11111            0                8.8196e-05   0.555566                    0.354929            0               1                          -100      11.1112            0.200004\n",
       "16       1                           0                  0         1                  0                2.65338e-05  0.500009                    0.319438            0               1                          -100      0                  0\n",
       "\n",
       "Scoring History: \n",
       "     timestamp            duration    number_of_trees    training_rmse        training_logloss     training_auc        training_pr_auc     training_lift       training_classification_error\n",
       "---  -------------------  ----------  -----------------  -------------------  -------------------  ------------------  ------------------  ------------------  -------------------------------\n",
       "     2022-12-13 18:12:13  0.030 sec   0.0                nan                  nan                  nan                 nan                 nan                 nan\n",
       "     2022-12-13 18:12:13  0.344 sec   1.0                0.31621097558467665  0.3588505305446817   0.9911985727184072  0.9883076403321843  1.9887309229538692  0.04387375140374727\n",
       "     2022-12-13 18:12:14  0.551 sec   2.0                0.31592316207890925  0.35801664172852365  0.991358218304642   0.9887040398653105  1.989657885999479   0.04374691948624279\n",
       "     2022-12-13 18:12:14  0.880 sec   3.0                0.33138594265495797  0.37297109681158547  0.9911432430280703  0.9886010688446388  1.9905181713683044  0.043622478722163925\n",
       "     2022-12-13 18:12:14  1.258 sec   4.0                0.3288489572750535   0.36851135260439805  0.9913373650515205  0.9886992148468785  1.9899656930322942  0.04330024524720245\n",
       "     2022-12-13 18:12:15  1.788 sec   5.0                0.32802588237708824  0.3635734061373862   0.9916427973581109  0.9892951302793811  1.9911389607799599  0.042841333484423635\n",
       "     2022-12-13 18:12:15  2.164 sec   6.0                0.32714929480227584  0.3585915389880392   0.9918819208715653  0.9896843221049973  1.9918525949920785  0.04237412059076803\n",
       "     2022-12-13 18:12:16  2.573 sec   7.0                0.3262284896481252   0.3552593045696047   0.9920295533941772  0.989917100107791   1.9922183754028653  0.04220718158808854\n",
       "     2022-12-13 18:12:16  3.001 sec   8.0                0.3252959790174767   0.35168875136822947  0.9921698729239463  0.9901295669527164  1.9925430696903244  0.04199119312599368\n",
       "     2022-12-13 18:12:17  3.385 sec   9.0                0.3262820525145416   0.3537216794837634   0.9921888427103476  0.9901380761635334  1.9924148332792662  0.041755163808209\n",
       "---  ---                  ---         ---                ---                  ---                  ---                 ---                 ---                 ---\n",
       "     2022-12-13 18:12:44  30.705 sec  91.0               0.3460256447244143   0.3794721577012952   0.9928385091488497  0.9916029431415919  1.9990938881364237  0.0408141034578918\n",
       "     2022-12-13 18:12:44  31.038 sec  92.0               0.3458156901464726   0.37912154660459896  0.9928385394969927  0.9916043666375431  1.999094077869588   0.04082281790295594\n",
       "     2022-12-13 18:12:45  31.366 sec  93.0               0.3454588845611718   0.37845728221212066  0.9928452984091042  0.9916141197261218  1.9990938881364237  0.04082499651422197\n",
       "     2022-12-13 18:12:45  31.702 sec  94.0               0.34538320216766966  0.3783276556992653   0.9928416228189633  0.9916134788386661  1.9990938881364237  0.040844604015616286\n",
       "     2022-12-13 18:12:45  32.111 sec  95.0               0.3452747093173052   0.3781735789061273   0.9928446728595484  0.9916242364789527  1.9990938881364237  0.040816282069157835\n",
       "     2022-12-13 18:12:46  32.449 sec  96.0               0.3449549539221984   0.37755646171222457  0.9928463084222637  0.991632095097003   1.999094077869588   0.040866390128276635\n",
       "     2022-12-13 18:12:46  32.775 sec  97.0               0.34472684627222644  0.37719755454854825  0.9928598310308044  0.9916428681183163  1.9990938881364237  0.04074438789737869\n",
       "     2022-12-13 18:12:46  33.067 sec  98.0               0.3444350196760413   0.37673153642359547  0.9928567655361134  0.9916389188337766  1.9990938881364237  0.040763995398773005\n",
       "     2022-12-13 18:12:47  33.374 sec  99.0               0.34418491112561533  0.3763017794876812   0.9928566621227038  0.9916553345462904  1.9986582607910026  0.04092956985499163\n",
       "     2022-12-13 18:12:47  33.664 sec  100.0              0.3441890996463613   0.3764399306371099   0.9928548908904044  0.9916492588930318  1.9982237713489657  0.04077270984383714\n",
       "[101 rows x 10 columns]\n",
       "\n",
       "\n",
       "Variable Importances: \n",
       "variable             relative_importance    scaled_importance    percentage\n",
       "-------------------  ---------------------  -------------------  ------------\n",
       "total_pages_visited  6.00933e+06            1                    0.769353\n",
       "new_user             837951                 0.139442             0.10728\n",
       "country              524928                 0.0873522            0.0672047\n",
       "age                  366419                 0.060975             0.0469113\n",
       "source               72260.7                0.0120247            0.00925128\n",
       "\n",
       "[tips]\n",
       "Use `model.explain()` to inspect the model.\n",
       "--\n",
       "Use `h2o.display.toggle_user_tips()` to switch on/off this section."
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build random forest model\n",
    "model = H2ORandomForestEstimator(balance_classes=True, ntrees=100, max_depth=20, \n",
    "                                 mtries=-1, seed=42, score_each_iteration=True)\n",
    "#The H2ORandomForestEstimator class is used to create a new Random Forest model\n",
    "#These include balance_classes which indicates whether to balance the class distribution in the training data\n",
    "#In a binary classification problem, where the classes are \"positive\" and \"negative\", balancing the class distribution can ensure that there are roughly equal numbers of positive and negative examples in the training data. This can be useful when the classes are highly imbalanced (e.g. there are many more negative examples than positive examples), as it can help the model to learn from both classes more effectively. \n",
    "# mtries which specifies the number of variables to randomly select at each split in the trees\n",
    "# a value of -1 indicates that all variables should be considered at each split. This means that the model will use all available features to make predictions, which can potentially improve the model's performance. However, it can also make the model more computationally expensive and may lead to overfitting if the number of features is very large.\n",
    "# score_each_iteration which indicates whether to score the model after each iteration of training.\n",
    "model.train(x=feature, y=target, training_frame=train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<h2o.plot._plot_result._MObject at 0x7f86f023aaf0>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.varimp_plot()\n",
    "#This code uses the varimp_plot() method to create a plot of the variable importance for a trained H2O model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feature importance\n",
    "importance = model.varimp(use_pandas=True)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.barplot(x='scaled_importance', y='variable', data=importance)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import auc, roc_curve, classification_report\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drf prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
      "drf prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n"
     ]
    }
   ],
   "source": [
    "# Make predictions\n",
    "\n",
    "train_true = train.as_data_frame()['converted'].values\n",
    "test_true = test.as_data_frame()['converted'].values\n",
    "train_pred = model.predict(train).as_data_frame()['p1'].values\n",
    "test_pred = model.predict(test).as_data_frame()['p1'].values\n",
    "\n",
    "train_fpr, train_tpr, _ = roc_curve(train_true, train_pred)\n",
    "test_fpr, test_tpr, _ = roc_curve(test_true, test_pred)\n",
    "\n",
    "train_auc = np.round(auc(train_fpr, train_tpr), 3)\n",
    "test_auc = np.round(auc(test_fpr, test_tpr), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99     76500\n",
      "           1       0.84      0.63      0.72      2550\n",
      "\n",
      "    accuracy                           0.98     79050\n",
      "   macro avg       0.91      0.81      0.86     79050\n",
      "weighted avg       0.98      0.98      0.98     79050\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report\n",
    "print(classification_report(y_true=test_true, y_pred=(test_pred > 0.5).astype(int)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.plot(train_fpr, train_tpr, label='Train AUC: ' + str(train_auc))\n",
    "ax.plot(test_fpr, test_tpr, label='Test AUC: ' + str(test_auc))\n",
    "ax.set_xlabel('False Positive Rate', fontsize=12)\n",
    "ax.set_ylabel('True Positive Rate', fontsize=12)\n",
    "ax.legend(fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PartialDependencePlot progress: |████████████████████████████████████████████████| (done) 100%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x1080 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = model.partial_plot(train, cols=feature, figsize=(8, 15))\n",
    "#which shows the marginal effect one or more features have on the predicted outcome of a machine learning model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H2O session _sid_8421 closed.\n"
     ]
    }
   ],
   "source": [
    "# Shutdown h2o instance\n",
    "h2o.cluster().shutdown()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "untitled",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6 (default, Aug 18 2021, 12:38:10) \n[Clang 10.0.0 ]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "a476a4d55e9d2eba0bf3220d935cd405961ec1ca78e4143d34c6c517149b6ade"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}