1. conversion rate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e5390564",
   "metadata": {},
   "source": [
    "## our goal is to predict conversion rate and make recommendations to improve conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "7170046f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "c25a22b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('conversion_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "b83c2a56",
   "metadata": {},
   "outputs": [
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       "      <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",
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       "  </thead>\n",
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       "      <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",
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       "    <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",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>China</td>\n",
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       "      <td>1</td>\n",
       "      <td>Seo</td>\n",
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       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>316196</th>\n",
       "      <td>US</td>\n",
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       "      <td>1</td>\n",
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       "      <th>316197</th>\n",
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       "      <th>316198</th>\n",
       "      <td>US</td>\n",
       "      <td>22</td>\n",
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       "      <td>0</td>\n",
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       "    <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": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "8e1a6f07",
   "metadata": {},
   "outputs": [
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       "      <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",
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       "    <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": 186,
     "metadata": {},
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   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2997ec3f",
   "metadata": {},
   "source": [
    "#### the max of age is 123, which seems to be abnormal "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "bafd7e5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90928     123\n",
       "295581    111\n",
       "265167     79\n",
       "192644     77\n",
       "154217     73\n",
       "         ... \n",
       "267312     17\n",
       "116907     17\n",
       "19652      17\n",
       "116914     17\n",
       "223133     17\n",
       "Name: age, Length: 316200, dtype: int64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['age'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18b54e25",
   "metadata": {},
   "source": [
    "#### there are two people age 123 and 111, we should delete these two "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "0df7787e",
   "metadata": {},
   "outputs": [
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       "      <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",
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       "<p>316198 rows × 6 columns</p>\n",
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      ],
      "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": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[data['age']<111]\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f04acdf",
   "metadata": {},
   "source": [
    "#### to look at each country's conversion rate specifically "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "9046e2de",
   "metadata": {},
   "outputs": [
    {
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       "      <th rowspan=\"2\" valign=\"top\">China</th>\n",
       "      <th>0</th>\n",
       "      <td>76500</td>\n",
       "      <td>76500</td>\n",
       "      <td>76500</td>\n",
       "      <td>76500</td>\n",
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       "      <th>1</th>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Germany</th>\n",
       "      <th>0</th>\n",
       "      <td>12240</td>\n",
       "      <td>12240</td>\n",
       "      <td>12240</td>\n",
       "      <td>12240</td>\n",
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       "      <th>1</th>\n",
       "      <td>815</td>\n",
       "      <td>815</td>\n",
       "      <td>815</td>\n",
       "      <td>815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">UK</th>\n",
       "      <th>0</th>\n",
       "      <td>45900</td>\n",
       "      <td>45900</td>\n",
       "      <td>45900</td>\n",
       "      <td>45900</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2549</td>\n",
       "      <td>2549</td>\n",
       "      <td>2549</td>\n",
       "      <td>2549</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">US</th>\n",
       "      <th>0</th>\n",
       "      <td>171360</td>\n",
       "      <td>171360</td>\n",
       "      <td>171360</td>\n",
       "      <td>171360</td>\n",
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       "      <th>1</th>\n",
       "      <td>6732</td>\n",
       "      <td>6732</td>\n",
       "      <td>6732</td>\n",
       "      <td>6732</td>\n",
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      ],
      "text/plain": [
       "                      age  new_user  source  total_pages_visited\n",
       "country converted                                               \n",
       "China   0           76500     76500   76500                76500\n",
       "        1             102       102     102                  102\n",
       "Germany 0           12240     12240   12240                12240\n",
       "        1             815       815     815                  815\n",
       "UK      0           45900     45900   45900                45900\n",
       "        1            2549      2549    2549                 2549\n",
       "US      0          171360    171360  171360               171360\n",
       "        1            6732      6732    6732                 6732"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['country','converted']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "41e8676b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "China : 0.0013315579227696406\n",
      "Germany : 0.062428188433550365\n",
      "UK : 0.052612025015996204\n",
      "US : 0.037800687285223365\n"
     ]
    }
   ],
   "source": [
    "for country in ['China', 'Germany', 'UK', 'US']:\n",
    "    conversion = len(data[(data['country']==country)&(data['converted']==1)]) / len(data[data['country']==country])\n",
    "    print(country, ':', conversion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "933f4fc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='country'>"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby('country')['converted'].mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43fb4b01",
   "metadata": {},
   "source": [
    "#### apparently, China has the lowest conversion rate and Germany has the highest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "cbcf1f45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe34dab7350>"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.age, data.converted)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0304cf8",
   "metadata": {},
   "source": [
    "#### apparently there is no conversion for people age over  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7299f153",
   "metadata": {},
   "source": [
    "#### replace all texts into values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "f2c74d98",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['country'] = data['country'].replace('China', 0)\n",
    "data['country'] = data['country'].replace('Germany', 1)\n",
    "data['country'] = data['country'].replace('UK', 2)\n",
    "data['country'] = data['country'].replace('US', 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "56fbb67d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['source'] = data['source'].replace('Seo', 0)\n",
    "data['source'] = data['source'].replace('Ads', 1)\n",
    "data['source'] = data['source'].replace('Direct', 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "2bbd6924",
   "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",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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        2   25         1       1                    1          0\n",
       "1        3   23         1       0                    5          0\n",
       "2        3   28         1       0                    4          0\n",
       "3        0   39         1       0                    5          0\n",
       "4        3   30         1       0                    6          0"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4984656f",
   "metadata": {},
   "source": [
    "### conversion rate = number of converted / total number of sessions, next we calculate the current conversion rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "35ba8386",
   "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",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</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>316087</th>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316088</th>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316114</th>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316128</th>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316184</th>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10198 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  age  new_user  source  total_pages_visited  converted\n",
       "54            3   25         1       2                   18          1\n",
       "72            3   33         0       0                   15          1\n",
       "87            3   18         0       2                   19          1\n",
       "130           3   17         0       1                   19          1\n",
       "182           3   19         0       1                    9          1\n",
       "...         ...  ...       ...     ...                  ...        ...\n",
       "316087        3   19         1       1                   19          1\n",
       "316088        3   28         0       0                   15          1\n",
       "316114        3   26         0       1                   13          1\n",
       "316128        3   17         0       2                   13          1\n",
       "316184        2   26         1       0                   16          1\n",
       "\n",
       "[10198 rows x 6 columns]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['converted'] ==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "3474ff41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03225194340255157"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conversion_rate = len(data[data['converted'] ==1]) / len(data['converted'])\n",
    "conversion_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43f43a9a",
   "metadata": {},
   "source": [
    "#### to increase the conversion rate, we must increase the number of converted, so we need to first look at what factors will influence their converted decision "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4600f820",
   "metadata": {},
   "source": [
    "#### because the outcome is binary, I will first try to fit a logistic regression model to see what factors contribute the most for conversion "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "fc895794",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "82436859",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('converted', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "812cc624",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['converted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "90c939e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(solver='liblinear', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "1d89b235",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0, solver='liblinear')"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "c6ffb758",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]),\n",
       " array([-8.28460453]),\n",
       " array([[ 0.4771877 , -0.07560157, -1.73673935, -0.07277083,  0.7555342 ]]))"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.classes_, model.intercept_, model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "8b169f94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.99965491e-01, 3.45093957e-05],\n",
       "       [9.98573275e-01, 1.42672495e-03],\n",
       "       [9.99540305e-01, 4.59694776e-04],\n",
       "       ...,\n",
       "       [9.99272579e-01, 7.27420709e-04],\n",
       "       [9.95906036e-01, 4.09396392e-03],\n",
       "       [9.97965159e-01, 2.03484055e-03]])"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred1 = model.predict_proba(X)\n",
    "y_pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "9b78a4c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred2 = model.predict(X)\n",
    "y_pred2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cecd983d",
   "metadata": {},
   "source": [
    "### model evaluation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "b3501033",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.985569168685444"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d154d30",
   "metadata": {},
   "source": [
    "#### the accuracy is pretty high as 0.98 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "de260263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y, y_pred2)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.imshow(cm)\n",
    "ax.grid(False)\n",
    "ax.xaxis.set(ticks=(0, 1), ticklabels=('Predicted 0', 'Predicted 1'))\n",
    "ax.yaxis.set(ticks=(0, 1), ticklabels=('Actual 0', 'Actual 1'))\n",
    "ax.set_ylim(1.5, -0.5)\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        ax.text(j, i, cm[i, j], ha='center', va='center', color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "9524817f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99    306000\n",
      "           1       0.85      0.67      0.75     10198\n",
      "\n",
      "    accuracy                           0.99    316198\n",
      "   macro avg       0.92      0.83      0.87    316198\n",
      "weighted avg       0.98      0.99      0.98    316198\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y, y_pred2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a03bf55f",
   "metadata": {},
   "source": [
    "#### next, I will try to determine which factors are most important to our model and increase those factors will increase conversion "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "84508a5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['country', 'age', 'new_user', 'source', 'total_pages_visited'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_names_in_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "79d51e48",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = ['country', 'age', 'new_user', 'source', 'total_pages_visited']\n",
    "for feature_name in feature_names:\n",
    "    data[feature_name] = data[feature_name] / data[feature_name].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "834a433f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "feature_importance = pd.DataFrame(feature_names, columns = [\"feature\"])\n",
    "feature_importance[\"importance\"] = pow(math.e, model.coef_[0])\n",
    "feature_importance = feature_importance.sort_values(by = [\"importance\"], ascending=True)\n",
    "\n",
    "ax = feature_importance.plot.barh(x='feature', y='importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2c6887e",
   "metadata": {},
   "source": [
    "#### we can see that total_pages_visited is the most important factor to our model, followed by country, source, age, and new_user "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "026d375e",
   "metadata": {},
   "source": [
    "#### based on our model, we can have the following recommendations:\n",
    "\n",
    "#### 1. try to provide more promotion or reach out to customers who have a large number of total pages visited, they have higher desire and probability to make a purchase\n",
    "\n",
    "#### 2. China seems to have a lower conversion rate than other countries, try to figure out whether the Chinese website version has some problems or bad translation that could result those problem and fix it\n",
    "\n",
    "#### 3. German customers have the highest conversion rate, try to provide more promotion or coupons to encourage their purchase behavior"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2c43556",
   "metadata": {},
   "source": [
    "## Future improvement: "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c40057c6",
   "metadata": {},
   "source": [
    "#### if I had more time, I will also try other models such as random forest or decision trees to see if I can get better results "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a4bee0",
   "metadata": {},
   "source": [
    "#### I will also try to analyze the errors such as RMSE or other metrics to evaluate and pick a better model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d363ffbd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}