01. Conversion Rate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T15:23:12.190742Z",
     "start_time": "2020-06-23T15:23:12.188584Z"
    }
   },
   "outputs": [],
   "source": [
    "## General Structure for all Notebooks \n",
    "## 1) Describe the problem being tackled, source of data, output \n",
    "## 2) Import libraries/set up wd; general working setup\n",
    "## 3) Examine the quality of the data \n",
    "## 4) Build the right dataset \n",
    "## 5) Apply the structure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem\n",
    "### what are we solving for?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T15:23:12.195238Z",
     "start_time": "2020-06-23T15:23:12.193044Z"
    }
   },
   "outputs": [],
   "source": [
    "#Build a model that predicts conversion rate and, based on the model, come up with ideas to improve it.\n",
    "\n",
    "#We have data about all users who hit our site: whether they converted or not, some characteristics,\n",
    "#whether they are repeat users and the number of pages visited during that session \n",
    "#(as a proxy for site activity/time spent on site)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T15:32:10.908829Z",
     "start_time": "2020-06-24T15:32:10.903437Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/annadudek/00_DataMasked\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import os \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from scipy.stats import zscore\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from sklearn.metrics import precision_recall_fscore_support as score\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "\n",
    "\n",
    "wd = os.getcwd()\n",
    "print(wd)\n",
    "\n",
    "## Github with answers: https://github.com/JifuZhao/DS-Take-Home/blob/master/01.%20Conversion%20Rate.ipynb "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Quality\n",
    "### Are there any outliers, weird data anamolies ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T11:09:54.057652Z",
     "start_time": "2020-06-24T11:09:52.405276Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(316200, 6)\n",
      "\n",
      "country                object\n",
      "age                     int64\n",
      "new_user                int64\n",
      "source                 object\n",
      "total_pages_visited     int64\n",
      "converted               int64\n",
      "dtype: object\n",
      "\n",
      "                 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\n"
     ]
    },
    {
     "data": {
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       "    .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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data_raw=pd.read_csv(\"https://drive.google.com/uc?export=download&id=1KvxyyF3QCtvIx0J7_8iWDEtFQpLgd0Yq\")\n",
    "\n",
    "print(data_raw.shape)\n",
    "print('')\n",
    "print(data_raw.dtypes)\n",
    "print('')\n",
    "print(data_raw.describe())\n",
    "data_raw.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T11:09:54.067425Z",
     "start_time": "2020-06-24T11:09:54.059818Z"
    }
   },
   "outputs": [],
   "source": [
    "### Drop outliers or truncate in depending on dataset size \n",
    "def outlier_management (data, columns): \n",
    "    \n",
    "    ## drop any columns which have fewer than 5 levels; assumption = they are categorical/boolean \n",
    "    for col in columns:\n",
    "        if len(data[col].unique())<=5:\n",
    "            columns.remove(col)\n",
    "        else: \n",
    "            print(f\"{[col]} is kept\")\n",
    "    print('')\n",
    "    print(columns)\n",
    "    print('')\n",
    "    col_zscore_list = []\n",
    "    \n",
    "    ### calculates z-scores for all numeric columns\n",
    "    for col in columns:\n",
    "        col_zscore = col + '_zscore'\n",
    "        col_zscore_list.append(col_zscore)\n",
    "        data[col_zscore] = abs((data[col] - data[col].mean())/data[col].std(ddof=0))\n",
    "    \n",
    "    ### drops or truncates depending on size \n",
    "    df_length = len(data)\n",
    "\n",
    "    if df_length>30000:\n",
    "        for col in col_zscore_list:\n",
    "#             df = data[(data[col] < 3).all(axis=1)]\n",
    "            data = data[data[col] < 3]\n",
    "    else:\n",
    "        print('test')\n",
    "\n",
    "    print(f\" Before outlier management :{df_length}; After outlier management {len(data)}\")  \n",
    "    print('')\n",
    "    print(f\" % of dataset retained :{len(data)/df_length}\")\n",
    "    return data\n",
    "\n",
    "\n",
    "def check_outlier_management(data, columns): \n",
    "    for col in columns: \n",
    "        print(f\" {col} : {data[col].max()}\")\n",
    "        print('')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T11:09:54.143137Z",
     "start_time": "2020-06-24T11:09:54.070842Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age'] is kept\n",
      "\n",
      "['age', 'total_pages_visited']\n",
      "\n",
      " Before outlier management :316200; After outlier management 309384\n",
      "\n",
      " % of dataset retained :0.9784440227703984\n",
      " age_zscore : 2.9534291961136763\n",
      "\n",
      " total_pages_visited_zscore : 2.731746337954114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## deal with the outliers \n",
    "data = outlier_management(data=data_raw, columns=data_raw.select_dtypes('number').columns.tolist())\n",
    "\n",
    "## check it was dealt with properly\n",
    "check_outlier_management(data=data, columns=data.filter(like='zscore').columns.to_list())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration \n",
    "### How do the different characteristics impact conversion? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T11:09:59.222080Z",
     "start_time": "2020-06-24T11:09:54.145552Z"
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Conversion by Country\n",
    "grouped = data[['country', 'converted']].groupby('country').mean().reset_index()\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18, 6))\n",
    "\n",
    "## plot 1\n",
    "sns.countplot(x='country', hue='converted', data=data, ax=ax[0])\n",
    "ax[0].set_title('Count Plot of Country', fontsize=16)\n",
    "# ax[0].set_yscale('log')\n",
    "\n",
    "## plot 2\n",
    "sns.barplot(x='country', y='converted', data=data, 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": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T11:10:03.326174Z",
     "start_time": "2020-06-24T11:09:59.224530Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Conversion by user type\n",
    "grouped = data[['new_user', 'converted']].groupby('new_user').mean().reset_index()\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18, 6))\n",
    "\n",
    "## plot 1\n",
    "sns.countplot(x='new_user', hue='converted', data=data, ax=ax[0])\n",
    "ax[0].set_title('Count Plot of User Types', fontsize=16)\n",
    "# ax[0].set_yscale('log')\n",
    "\n",
    "## plot 2\n",
    "sns.barplot(x='new_user', y='converted', data=data, 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": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T11:10:09.141632Z",
     "start_time": "2020-06-24T11:10:03.328733Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Conversion by acquisition source\n",
    "grouped = data[['source', 'converted']].groupby('source').mean().reset_index()\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18, 6))\n",
    "\n",
    "## plot 1 \n",
    "sns.countplot(x='source', hue='converted', data=data, ax=ax[0])\n",
    "ax[0].set_title('Count Plot of Different Sources', fontsize=16)\n",
    "# ax[0].set_yscale('log')\n",
    "\n",
    "## plot 2 \n",
    "sns.barplot(x='source', y='converted', data=data, 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": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T11:10:09.863858Z",
     "start_time": "2020-06-24T11:10:09.143588Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Conversion by Age \n",
    "grouped = data[['age', 'converted']].groupby('age').mean().reset_index()\n",
    "hist_kws={'histtype': 'bar', 'edgecolor':'black', 'alpha': 0.2}\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18, 6))\n",
    "\n",
    "## plot 1\n",
    "sns.distplot(data[data['converted'] == 0]['age'], label='Converted 0', \n",
    "             ax=ax[0], hist_kws=hist_kws)\n",
    "sns.distplot(data[data['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()\n",
    "\n",
    "\n",
    "## plot 2 \n",
    "ax[1].plot(grouped['age'], grouped['converted'], '.-')\n",
    "ax[1].set_title('Mean Conversion Rate vs. Age', fontsize=16)\n",
    "ax[1].set_xlabel('age')\n",
    "ax[1].set_ylabel('Mean conversion rate')\n",
    "ax[1].grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T11:10:10.512945Z",
     "start_time": "2020-06-24T11:10:09.865884Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Conversion by total pages View \n",
    "grouped = data[['total_pages_visited', 'converted']].groupby('total_pages_visited').mean().reset_index()\n",
    "hist_kws={'histtype': 'bar', 'edgecolor':'black', 'alpha': 0.2}\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18, 6))\n",
    "\n",
    "## plot 1\n",
    "sns.distplot(data[data['converted'] == 0]['total_pages_visited'], label='Converted 0', \n",
    "             ax=ax[0], hist_kws=hist_kws)\n",
    "sns.distplot(data[data['converted'] == 1]['total_pages_visited'], label='Converted 1', \n",
    "             ax=ax[0], hist_kws=hist_kws)\n",
    "ax[0].set_title('Count Plot of Pages Visited', fontsize=16)\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "## plot 2 \n",
    "ax[1].plot(grouped['total_pages_visited'], grouped['converted'], '.-')\n",
    "ax[1].set_title('Mean Conversion Rate vs. Pages Visited', fontsize=16)\n",
    "ax[1].set_xlabel('total_pages_visited')\n",
    "ax[1].set_ylabel('Mean conversion rate')\n",
    "ax[1].grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Features & Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T11:10:10.707164Z",
     "start_time": "2020-06-24T11:10:10.514970Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['country', 'age', 'new_user', 'source', 'total_pages_visited',\n",
      "       'converted', 'age_zscore', 'total_pages_visited_zscore',\n",
      "       'Acq_Country_China', 'Acq_Country_Germany', 'Acq_Country_UK',\n",
      "       'Acq_Country_US', 'Acq_Source_Ads', 'Acq_Source_Direct',\n",
      "       'Acq_Source_Seo', 'age_bin', 'agegroup0-20', 'agegroup20-30',\n",
      "       'agegroup30-40', 'agegroup40-50', 'agegroup50-60', 'agegroup60-70',\n",
      "       'agegroup70-80'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "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",
       "      <th>converted</th>\n",
       "      <th>Acq_Country_China</th>\n",
       "      <th>Acq_Country_Germany</th>\n",
       "      <th>Acq_Country_UK</th>\n",
       "      <th>Acq_Country_US</th>\n",
       "      <th>...</th>\n",
       "      <th>Acq_Source_Direct</th>\n",
       "      <th>Acq_Source_Seo</th>\n",
       "      <th>age_bin</th>\n",
       "      <th>agegroup0-20</th>\n",
       "      <th>agegroup20-30</th>\n",
       "      <th>agegroup30-40</th>\n",
       "      <th>agegroup40-50</th>\n",
       "      <th>agegroup50-60</th>\n",
       "      <th>agegroup60-70</th>\n",
       "      <th>agegroup70-80</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0-20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0-20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0-20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20-30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0-20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 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",
       "   Acq_Country_China  Acq_Country_Germany  Acq_Country_UK  Acq_Country_US  \\\n",
       "0                  0                    0               1               0   \n",
       "1                  0                    0               0               1   \n",
       "2                  0                    0               0               1   \n",
       "3                  1                    0               0               0   \n",
       "4                  0                    0               0               1   \n",
       "\n",
       "   ...  Acq_Source_Direct  Acq_Source_Seo  age_bin agegroup0-20  \\\n",
       "0  ...                  0               0     0-20            1   \n",
       "1  ...                  0               1     0-20            1   \n",
       "2  ...                  0               1     0-20            1   \n",
       "3  ...                  0               1    20-30            0   \n",
       "4  ...                  0               1     0-20            1   \n",
       "\n",
       "   agegroup20-30  agegroup30-40  agegroup40-50  agegroup50-60  agegroup60-70  \\\n",
       "0              0              0              0              0              0   \n",
       "1              0              0              0              0              0   \n",
       "2              0              0              0              0              0   \n",
       "3              1              0              0              0              0   \n",
       "4              0              0              0              0              0   \n",
       "\n",
       "   agegroup70-80  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Create Dummy Variables for source & country\n",
    "\n",
    "dummies_country = pd.get_dummies(data['country']).rename(columns=lambda x: 'Acq_Country_' + str(x))\n",
    "data = data.join(dummies_country)\n",
    "\n",
    "dummies_source = pd.get_dummies(data['source']).rename(columns=lambda x: 'Acq_Source_' + str(x))\n",
    "data = data.join(dummies_source)\n",
    "\n",
    "\n",
    "\n",
    "## Bin Age \n",
    "bins = [20, 30, 40, 50, 60, 70, 80, 1000]\n",
    "labels = ['0-20','20-30','30-40','40-50', '50-60', '60-70', '70-80']\n",
    "data['age_bin']=pd.cut(data['age'], bins=bins, labels=labels)\n",
    "\n",
    "dummies_age = pd.get_dummies(data['age_bin']).rename(columns=lambda x:'agegroup' + str(x))  \n",
    "data = data.join(dummies_age)\n",
    "\n",
    "\n",
    "## Reorganize dataframe to only what's needed\n",
    "print(data.columns)\n",
    "data=data[['country', 'age', 'new_user', 'source', 'total_pages_visited',\n",
    "       'converted', 'Acq_Country_China', 'Acq_Country_Germany',\n",
    "       'Acq_Country_UK', 'Acq_Country_US', 'Acq_Source_Ads', 'Acq_Source_Direct',\n",
    "       'Acq_Source_Seo', 'age_bin','agegroup0-20', 'agegroup20-30', \n",
    "       'agegroup30-40', 'agegroup40-50',\n",
    "       'agegroup50-60', 'agegroup60-70', 'agegroup70-80']]\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T11:30:45.522176Z",
     "start_time": "2020-06-24T11:30:35.448878Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(216568, 21)\n",
      "(92816, 21)\n"
     ]
    }
   ],
   "source": [
    "## split into train and test sets \n",
    "train, test = train_test_split(data, test_size=0.3, random_state=42)\n",
    "print(train.shape)\n",
    "print(test.shape)\n",
    "\n",
    "\n",
    "variables_to_use = ['age', 'new_user', 'total_pages_visited',\n",
    "       'Acq_Country_China', 'Acq_Country_Germany',\n",
    "       'Acq_Country_UK', 'Acq_Country_US', 'Acq_Source_Ads', 'Acq_Source_Direct',\n",
    "       'Acq_Source_Seo', 'agegroup0-20', 'agegroup20-30', 'agegroup30-40',\n",
    "       'agegroup40-50','agegroup50-60', 'agegroup60-70', 'agegroup70-80']\n",
    "\n",
    "X_train = train[variables_to_use]\n",
    "y_train = train['converted']\n",
    "\n",
    "X_test = test[variables_to_use]\n",
    "y_test = test['converted']\n",
    "\n",
    "\n",
    "clf=RandomForestClassifier(n_estimators=100, max_features = 5, oob_score=True)\n",
    "\n",
    "## Train the model \n",
    "clf.fit(X_train,y_train)\n",
    "\n",
    "\n",
    "## Predictions \n",
    "y_pred = clf.predict(X_test)\n",
    "test['y_hat']= clf.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reviewing Accuracy, F-Score (Precision v Recall), Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T11:30:45.676319Z",
     "start_time": "2020-06-24T11:30:45.524173Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9860476641958283\n",
      "Out of Bag Score: 0.9856765542462413\n",
      "\n",
      "Feature Importance sorted: \n",
      "total_pages_visited    0.678840\n",
      "age                    0.166925\n",
      "new_user               0.082335\n",
      "Acq_Country_China      0.022906\n",
      "Acq_Country_UK         0.008929\n",
      "Acq_Country_US         0.006691\n",
      "agegroup20-30          0.006222\n",
      "Acq_Country_Germany    0.004941\n",
      "agegroup30-40          0.004863\n",
      "agegroup0-20           0.004483\n",
      "Acq_Source_Seo         0.004000\n",
      "Acq_Source_Direct      0.003858\n",
      "Acq_Source_Ads         0.003806\n",
      "agegroup40-50          0.001201\n",
      "agegroup60-70          0.000000\n",
      "agegroup50-60          0.000000\n",
      "agegroup70-80          0.000000\n",
      "dtype: float64\n",
      "\n",
      "confusion matrix - absolute values\n",
      "Predicted      0    1    All\n",
      "Actual                      \n",
      "0          90962  336  91298\n",
      "1            959  559   1518\n",
      "All        91921  895  92816\n",
      "\n",
      "confusion matrix - % values\n",
      "Predicted         0         1       All\n",
      "Actual                                 \n",
      "0          0.498160  0.110672  0.491823\n",
      "1          0.005252  0.184124  0.008177\n",
      "All        0.503412  0.294796  0.500000\n"
     ]
    },
    {
     "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>metric</th>\n",
       "      <th>not_converted</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>precision</td>\n",
       "      <td>0.989567</td>\n",
       "      <td>0.624581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>recall</td>\n",
       "      <td>0.996320</td>\n",
       "      <td>0.368248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fscore</td>\n",
       "      <td>0.992932</td>\n",
       "      <td>0.463324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>support</td>\n",
       "      <td>91298.000000</td>\n",
       "      <td>1518.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      metric  not_converted    converted\n",
       "0  precision       0.989567     0.624581\n",
       "1     recall       0.996320     0.368248\n",
       "2     fscore       0.992932     0.463324\n",
       "3    support   91298.000000  1518.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
    "print (\"Out of Bag Score:\", clf.oob_score_)\n",
    "## which features are important \n",
    "feature_imp = pd.Series(clf.feature_importances_,\n",
    "                        index=variables_to_use).sort_values(ascending=False)\n",
    "print('')\n",
    "print('Feature Importance sorted: ')\n",
    "print(feature_imp)\n",
    "\n",
    "\n",
    "## confusion matrix \n",
    "confusion_matrix(test['converted'], test['y_hat']) \n",
    "\n",
    "df_confusion = pd.crosstab(test['converted'], test['y_hat'],\n",
    "                           rownames=['Actual'], colnames=['Predicted'], margins=True)\n",
    "print('')\n",
    "print('confusion matrix - absolute values')\n",
    "print(df_confusion)\n",
    "\n",
    "print('')\n",
    "print('confusion matrix - % values')\n",
    "print(df_confusion / df_confusion.sum(axis=1))       ### represented as a percentage \n",
    "\n",
    "\n",
    "## Prediction v Recall \n",
    "precision, recall, fscore, support = score(test['converted'], test['y_hat'],labels=[0,1])\n",
    "\n",
    "\n",
    "## Precision: % of results that are TP of all the records identified positively. TP/(TP + FP) \n",
    "## Recall: % of results that are TP from all records that are TP and which were missed as TP (FN). TP/(TP + FN)\n",
    "## Accuracy: % of all positives and negatives identified correctly (TP + TN)/total records\n",
    "## F1-Score: \n",
    "## Support: \n",
    "\n",
    "\n",
    "classification_report= pd.DataFrame(columns = ('metric', 'not_converted', 'converted'))\n",
    "\n",
    "classification_report.loc[0] = ['precision', precision[0], precision[1]]\n",
    "classification_report.loc[1] = ['recall', recall[0], recall[1]]\n",
    "classification_report.loc[2] = ['fscore', fscore[0], fscore[1]]\n",
    "classification_report.loc[3] = ['support', support[0], support[1]]\n",
    "\n",
    "classification_report\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression for Comparison "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T15:29:41.532674Z",
     "start_time": "2020-06-24T15:29:39.761978Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9871789346664368\n",
      "[[91070   228]\n",
      " [  962   556]]\n"
     ]
    }
   ],
   "source": [
    "logisticRegr = LogisticRegression()\n",
    "logisticRegr.fit(X_train,y_train)\n",
    "\n",
    "## Predict for test set \n",
    "predictions = logisticRegr.predict(X_test)\n",
    "test['y_hat_lr']= clf.predict(X_test)\n",
    "\n",
    "lr_score = logisticRegr.score(X_test, y_test)\n",
    "print(lr_score)\n",
    "\n",
    "confusion_matrix_lr = metrics.confusion_matrix(y_test, predictions)\n",
    "print(confusion_matrix_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T15:27:24.421454Z",
     "start_time": "2020-06-24T15:27:24.185188Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Confusion Matrix\n",
    "\n",
    "plt.figure(figsize=(9,9))\n",
    "sns.heatmap(confusion_matrix_lr, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Actual label');\n",
    "plt.xlabel('Predicted label');\n",
    "all_sample_title = 'Accuracy Score: {0}'.format(score)\n",
    "plt.title(all_sample_title, size = 15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T15:33:03.863771Z",
     "start_time": "2020-06-24T15:33:03.830342Z"
    }
   },
   "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>metric</th>\n",
       "      <th>not_converted</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>precision</td>\n",
       "      <td>0.989567</td>\n",
       "      <td>0.624581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>recall</td>\n",
       "      <td>0.996320</td>\n",
       "      <td>0.368248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fscore</td>\n",
       "      <td>0.992932</td>\n",
       "      <td>0.463324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>support</td>\n",
       "      <td>91298.000000</td>\n",
       "      <td>1518.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      metric  not_converted    converted\n",
       "0  precision       0.989567     0.624581\n",
       "1     recall       0.996320     0.368248\n",
       "2     fscore       0.992932     0.463324\n",
       "3    support   91298.000000  1518.000000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Prediction v Recall \n",
    "del precision, recall, fscore, support\n",
    "precision, recall, fscore, support = score(test['converted'], test['y_hat_lr'],labels=[0,1])\n",
    "\n",
    "\n",
    "## Precision: % of results that are TP of all the records identified positively. TP/(TP + FP) \n",
    "## Recall: % of results that are TP from all records that are TP and which were missed as TP (FN). TP/(TP + FN)\n",
    "## Accuracy: % of all positives and negatives identified correctly (TP + TN)/total records\n",
    "## F1-Score: \n",
    "## Support: \n",
    "\n",
    "\n",
    "classification_report_lr= pd.DataFrame(columns = ('metric', 'not_converted', 'converted'))\n",
    "\n",
    "classification_report_lr.loc[0] = ['precision', precision[0], precision[1]]\n",
    "classification_report_lr.loc[1] = ['recall', recall[0], recall[1]]\n",
    "classification_report_lr.loc[2] = ['fscore', fscore[0], fscore[1]]\n",
    "classification_report_lr.loc[3] = ['support', support[0], support[1]]\n",
    "\n",
    "classification_report_lr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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