taxi_driver_app_eda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cbad3590",
   "metadata": {},
   "source": [
    "<div style=\"border-bottom: 15px linear-gradient(to right, red, blue); margin-top: 10px; width: 100%;\"></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "613ea95a",
   "metadata": {},
   "source": [
    "<div style=\"border-bottom: 15px solid lightblue; border-radius: 10px; width: 100%;\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4ab025c",
   "metadata": {},
   "source": [
    "# TAXI DRIVER APPLICATION STATISTICS AND ANALYTICS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50319a06",
   "metadata": {},
   "source": [
    "<div style=\"border-bottom: 15px solid lightblue; border-radius: 10px; width: 100%;\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2b03207",
   "metadata": {},
   "source": [
    "## About\n",
    "Conduct data analysis to examine driver departures from the cab company to identify differences between drivers who have left and those who remain. Hypotheses should be formulated and tested, and key segments of drivers most at risk of leaving should be identified. Based on the findings, conclusions should be drawn as to which aspects of the service can be improved to make appropriate changes in the future."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b40034b",
   "metadata": {},
   "source": [
    "<div style=\"border-bottom: 15px solid lightblue; border-radius: 10px; width: 100%;\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a23fdf2",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fd1c82d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as ss\n",
    "import scikit_posthocs as sp\n",
    "\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import matplotlib as mplt\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ca98556",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a496af86",
   "metadata": {},
   "source": [
    "<div style=\"border-bottom: 15px solid lightblue; border-radius: 10px; width: 100%;\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e309cf",
   "metadata": {},
   "source": [
    "## Defining Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "00802dec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Info about DataFrame\n",
    "def overview(df):\n",
    "    print(\" DATA INFO \".center(50,'-'))\n",
    "    print(df.info())\n",
    "    \n",
    "    print(\" SHAPE OF DATASET \".center(50,'-'))\n",
    "    print('Rows:{}'.format(df.shape[0]))\n",
    "    print('Columns:{}'.format(df.shape[1]))\n",
    "    \n",
    "    print(\" DATA TYPES \".center(50,'-'))\n",
    "    print(df.dtypes)\n",
    "    \n",
    "    print(\" STATISTICS OF DATA \".center(50,'-'))\n",
    "    print(df.describe(include=\"all\", datetime_is_numeric=True))\n",
    "    \n",
    "    print(\" MISSING VALUES \".center(50,'-'))\n",
    "    print(df.isnull().sum()[df.isnull().sum()>0].sort_values(ascending = False))\n",
    "    \n",
    "    print(\" DUPLICATED VALUES \".center(50,'-'))\n",
    "    print(df.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bea6482",
   "metadata": {},
   "source": [
    "<div style=\"border-bottom: 15px solid lightblue; border-radius: 10px; width: 100%;\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "198565ef",
   "metadata": {},
   "source": [
    "## Uploading and Obtaining Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d149e441",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>avg_dist</th>\n",
       "      <th>avg_rating_by_driver</th>\n",
       "      <th>avg_rating_of_driver</th>\n",
       "      <th>avg_surge</th>\n",
       "      <th>city</th>\n",
       "      <th>last_trip_date</th>\n",
       "      <th>phone</th>\n",
       "      <th>signup_date</th>\n",
       "      <th>surge_pct</th>\n",
       "      <th>trips_in_first_30_days</th>\n",
       "      <th>luxury_car_user</th>\n",
       "      <th>weekday_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.10</td>\n",
       "      <td>King's Landing</td>\n",
       "      <td>2014-06-17</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-25</td>\n",
       "      <td>15.4</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>46.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-05-05</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.77</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.36</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.14</td>\n",
       "      <td>King's Landing</td>\n",
       "      <td>2014-06-29</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-10</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.13</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.19</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-03-15</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-27</td>\n",
       "      <td>11.8</td>\n",
       "      <td>14</td>\n",
       "      <td>False</td>\n",
       "      <td>82.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_dist  avg_rating_by_driver  avg_rating_of_driver  avg_surge  \\\n",
       "0      3.67                   5.0                   4.7       1.10   \n",
       "1      8.26                   5.0                   5.0       1.00   \n",
       "2      0.77                   5.0                   4.3       1.00   \n",
       "3      2.36                   4.9                   4.6       1.14   \n",
       "4      3.13                   4.9                   4.4       1.19   \n",
       "\n",
       "             city last_trip_date    phone signup_date  surge_pct  \\\n",
       "0  King's Landing     2014-06-17   iPhone  2014-01-25       15.4   \n",
       "1         Astapor     2014-05-05  Android  2014-01-29        0.0   \n",
       "2         Astapor     2014-01-07   iPhone  2014-01-06        0.0   \n",
       "3  King's Landing     2014-06-29   iPhone  2014-01-10       20.0   \n",
       "4      Winterfell     2014-03-15  Android  2014-01-27       11.8   \n",
       "\n",
       "   trips_in_first_30_days  luxury_car_user  weekday_pct  \n",
       "0                       4             True         46.2  \n",
       "1                       0            False         50.0  \n",
       "2                       3            False        100.0  \n",
       "3                       9             True         80.0  \n",
       "4                      14            False         82.4  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_taxi = pd.read_csv('data\\churn.csv')\n",
    "df_taxi.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4338a73f",
   "metadata": {},
   "source": [
    "### Data description\n",
    "\n",
    "- `city` - city\n",
    "- `phone` - the main device used by the driver \n",
    "- `signup_date` - date of account registration (`YYYYYYYMMDD`)\n",
    "- `last_trip_date` - date of the last trip (`YYYYYYMMDD`)\n",
    "- `avg_dist` - average distance (in miles) per trip in the first 30 days after registration\n",
    "- `avg_rating_by_driver` - average rating of trips by driver \n",
    "- `avg_rating_of_driver` - average rating of driver trips\n",
    "- `surge_pct` - percentage of trips made with a multiplier > 1 (seems to be when there is heavy traffic, etc.)\n",
    "- `avg_surge` - average surge multiplier for all trips for this driver\n",
    "- `trips_in_first_30_days` - number of trips taken by the driver in the first 30 days after registration\n",
    "- `luxury_car_user` - TRUE if the user used a premium car in the first 30 days\n",
    "- `weekday_pct` - percentage of user's trips made on weekdays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a859143b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "avg_dist                  float64\n",
       "avg_rating_by_driver      float64\n",
       "avg_rating_of_driver      float64\n",
       "avg_surge                 float64\n",
       "city                       object\n",
       "last_trip_date             object\n",
       "phone                      object\n",
       "signup_date                object\n",
       "surge_pct                 float64\n",
       "trips_in_first_30_days      int64\n",
       "luxury_car_user              bool\n",
       "weekday_pct               float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_taxi.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "80957a0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change to date type after checking\n",
    "df_taxi.last_trip_date = pd.to_datetime(df_taxi.last_trip_date)\n",
    "df_taxi.signup_date = pd.to_datetime(df_taxi.signup_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b2dd7927",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------- DATA INFO --------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 12 columns):\n",
      " #   Column                  Non-Null Count  Dtype         \n",
      "---  ------                  --------------  -----         \n",
      " 0   avg_dist                50000 non-null  float64       \n",
      " 1   avg_rating_by_driver    49799 non-null  float64       \n",
      " 2   avg_rating_of_driver    41878 non-null  float64       \n",
      " 3   avg_surge               50000 non-null  float64       \n",
      " 4   city                    50000 non-null  object        \n",
      " 5   last_trip_date          50000 non-null  datetime64[ns]\n",
      " 6   phone                   49604 non-null  object        \n",
      " 7   signup_date             50000 non-null  datetime64[ns]\n",
      " 8   surge_pct               50000 non-null  float64       \n",
      " 9   trips_in_first_30_days  50000 non-null  int64         \n",
      " 10  luxury_car_user         50000 non-null  bool          \n",
      " 11  weekday_pct             50000 non-null  float64       \n",
      "dtypes: bool(1), datetime64[ns](2), float64(6), int64(1), object(2)\n",
      "memory usage: 4.2+ MB\n",
      "None\n",
      "---------------- SHAPE OF DATASET ----------------\n",
      "Rows:50000\n",
      "Columns:12\n",
      "------------------- DATA TYPES -------------------\n",
      "avg_dist                         float64\n",
      "avg_rating_by_driver             float64\n",
      "avg_rating_of_driver             float64\n",
      "avg_surge                        float64\n",
      "city                              object\n",
      "last_trip_date            datetime64[ns]\n",
      "phone                             object\n",
      "signup_date               datetime64[ns]\n",
      "surge_pct                        float64\n",
      "trips_in_first_30_days             int64\n",
      "luxury_car_user                     bool\n",
      "weekday_pct                      float64\n",
      "dtype: object\n",
      "--------------- STATISTICS OF DATA ---------------\n",
      "            avg_dist  avg_rating_by_driver  avg_rating_of_driver  \\\n",
      "count   50000.000000          49799.000000          41878.000000   \n",
      "unique           NaN                   NaN                   NaN   \n",
      "top              NaN                   NaN                   NaN   \n",
      "freq             NaN                   NaN                   NaN   \n",
      "mean        5.796827              4.778158              4.601559   \n",
      "min         0.000000              1.000000              1.000000   \n",
      "25%         2.420000              4.700000              4.300000   \n",
      "50%         3.880000              5.000000              4.900000   \n",
      "75%         6.940000              5.000000              5.000000   \n",
      "max       160.960000              5.000000              5.000000   \n",
      "std         5.707357              0.446652              0.617338   \n",
      "\n",
      "           avg_surge        city                 last_trip_date   phone  \\\n",
      "count   50000.000000       50000                          50000   49604   \n",
      "unique           NaN           3                            NaN       2   \n",
      "top              NaN  Winterfell                            NaN  iPhone   \n",
      "freq             NaN       23336                            NaN   34582   \n",
      "mean        1.074764         NaN  2014-04-19 08:02:04.992000256     NaN   \n",
      "min         1.000000         NaN            2014-01-01 00:00:00     NaN   \n",
      "25%         1.000000         NaN            2014-02-14 00:00:00     NaN   \n",
      "50%         1.000000         NaN            2014-05-08 00:00:00     NaN   \n",
      "75%         1.050000         NaN            2014-06-18 00:00:00     NaN   \n",
      "max         8.000000         NaN            2014-07-01 00:00:00     NaN   \n",
      "std         0.222336         NaN                            NaN     NaN   \n",
      "\n",
      "                       signup_date     surge_pct  trips_in_first_30_days  \\\n",
      "count                        50000  50000.000000            50000.000000   \n",
      "unique                         NaN           NaN                     NaN   \n",
      "top                            NaN           NaN                     NaN   \n",
      "freq                           NaN           NaN                     NaN   \n",
      "mean    2014-01-16 13:04:20.352000      8.849536                2.278200   \n",
      "min            2014-01-01 00:00:00      0.000000                0.000000   \n",
      "25%            2014-01-09 00:00:00      0.000000                0.000000   \n",
      "50%            2014-01-17 00:00:00      0.000000                1.000000   \n",
      "75%            2014-01-24 00:00:00      8.600000                3.000000   \n",
      "max            2014-01-31 00:00:00    100.000000              125.000000   \n",
      "std                            NaN     19.958811                3.792684   \n",
      "\n",
      "       luxury_car_user   weekday_pct  \n",
      "count            50000  50000.000000  \n",
      "unique               2           NaN  \n",
      "top              False           NaN  \n",
      "freq             31146           NaN  \n",
      "mean               NaN     60.926084  \n",
      "min                NaN      0.000000  \n",
      "25%                NaN     33.300000  \n",
      "50%                NaN     66.700000  \n",
      "75%                NaN    100.000000  \n",
      "max                NaN    100.000000  \n",
      "std                NaN     37.081503  \n",
      "----------------- MISSING VALUES -----------------\n",
      "avg_rating_of_driver    8122\n",
      "phone                    396\n",
      "avg_rating_by_driver     201\n",
      "dtype: int64\n",
      "--------------- DUPLICATED VALUES ----------------\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "overview(df=df_taxi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af39c3b2",
   "metadata": {},
   "source": [
    "### Note \n",
    "Many rating values are empty since it appears after a critical mass of ratings has been accumulated"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7ffe0dc",
   "metadata": {},
   "source": [
    "<div style=\"border-bottom: 15px solid lightblue; border-radius: 10px; width: 100%;\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daca5606",
   "metadata": {},
   "source": [
    "## Hypothesis Testing and Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d809351",
   "metadata": {},
   "source": [
    "User has left if inactive for 30 days\n",
    "\n",
    "-  `days_since_last_trip` - Number of days elapsed since the last trip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6f449891",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_taxi['days_since_last_trip'] = (df_taxi.last_trip_date.max() - df_taxi.last_trip_date).dt.days\n",
    "# df_taxi.days_since_last_trip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61dcd036",
   "metadata": {},
   "source": [
    "-  `churn` - drivers' churn rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "15359eb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_taxi['churn'] = (df_taxi.last_trip_date.max() - df_taxi.last_trip_date) \\\n",
    "                            .dt.days \\\n",
    "                            .apply(lambda x: 'churn' if x > 30 else 'not_churn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "df06ad30",
   "metadata": {},
   "outputs": [
    {
     "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>avg_dist</th>\n",
       "      <th>avg_rating_by_driver</th>\n",
       "      <th>avg_rating_of_driver</th>\n",
       "      <th>avg_surge</th>\n",
       "      <th>city</th>\n",
       "      <th>last_trip_date</th>\n",
       "      <th>phone</th>\n",
       "      <th>signup_date</th>\n",
       "      <th>surge_pct</th>\n",
       "      <th>trips_in_first_30_days</th>\n",
       "      <th>luxury_car_user</th>\n",
       "      <th>weekday_pct</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.10</td>\n",
       "      <td>King's Landing</td>\n",
       "      <td>2014-06-17</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-25</td>\n",
       "      <td>15.4</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>46.2</td>\n",
       "      <td>not_churn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-05-05</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>50.0</td>\n",
       "      <td>churn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.77</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Astapor</td>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>churn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.36</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.14</td>\n",
       "      <td>King's Landing</td>\n",
       "      <td>2014-06-29</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>2014-01-10</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "      <td>80.0</td>\n",
       "      <td>not_churn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.13</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.19</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>2014-03-15</td>\n",
       "      <td>Android</td>\n",
       "      <td>2014-01-27</td>\n",
       "      <td>11.8</td>\n",
       "      <td>14</td>\n",
       "      <td>False</td>\n",
       "      <td>82.4</td>\n",
       "      <td>churn</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_dist  avg_rating_by_driver  avg_rating_of_driver  avg_surge  \\\n",
       "0      3.67                   5.0                   4.7       1.10   \n",
       "1      8.26                   5.0                   5.0       1.00   \n",
       "2      0.77                   5.0                   4.3       1.00   \n",
       "3      2.36                   4.9                   4.6       1.14   \n",
       "4      3.13                   4.9                   4.4       1.19   \n",
       "\n",
       "             city last_trip_date    phone signup_date  surge_pct  \\\n",
       "0  King's Landing     2014-06-17   iPhone  2014-01-25       15.4   \n",
       "1         Astapor     2014-05-05  Android  2014-01-29        0.0   \n",
       "2         Astapor     2014-01-07   iPhone  2014-01-06        0.0   \n",
       "3  King's Landing     2014-06-29   iPhone  2014-01-10       20.0   \n",
       "4      Winterfell     2014-03-15  Android  2014-01-27       11.8   \n",
       "\n",
       "   trips_in_first_30_days  luxury_car_user  weekday_pct      churn  \n",
       "0                       4             True         46.2  not_churn  \n",
       "1                       0            False         50.0      churn  \n",
       "2                       3            False        100.0      churn  \n",
       "3                       9             True         80.0  not_churn  \n",
       "4                      14            False         82.4      churn  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_taxi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1a96cb77",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of people who left: 31196\n",
      "Number of all people:      50000\n"
     ]
    }
   ],
   "source": [
    "print('Number of people who left: {}'.format(df_taxi\n",
    "                                                 .query('churn == \"churn\"')\n",
    "                                                 .shape[0]))\n",
    "print('Number of all people:      {}'.format(df_taxi\n",
    "                                                 .shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "426069f8",
   "metadata": {},
   "source": [
    "### Note\n",
    "Notice that a significant number of users have had no activity on the platform over the last month. Consequently, an analysis is required to identify the factors influencing driver disengagement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ed584ed5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "churn        62.392\n",
       "not_churn    37.608\n",
       "Name: churn, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count parts precents of churn rate\n",
    "df_taxi.churn.value_counts(normalize=True).mul(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4540b979",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of churn rate - Histogram\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
    "\n",
    "sns.histplot(data=df_taxi, \n",
    "             x='churn', \n",
    "             stat='count', \n",
    "             color='lightblue', \n",
    "             edgecolor='white', \n",
    "             shrink=0.8, \n",
    "             binwidth=1,\n",
    "             ax=ax1)\n",
    "\n",
    "ax1.set_title(\"Histogram Churn\", fontsize=16)\n",
    "ax1.set_xlabel(\"Churn Status\", fontsize=14)\n",
    "ax1.set_ylabel(\"Drivers\", fontsize=14)\n",
    "ax1.set_xticks([0, 1])\n",
    "ax1.set_xticklabels(['Churn', 'Not Churn'], fontsize=12)\n",
    "ax1.tick_params(axis='y', labelsize=12)\n",
    "\n",
    "for p in ax1.patches:\n",
    "    height = p.get_height()\n",
    "    ax1.text(p.get_x() + p.get_width() / 2., height + 5, f\"{int(height)}\", ha=\"center\", fontsize=12)\n",
    "\n",
    "# Visualization of churn rate - Pie Chart\n",
    "ax2.pie(df_taxi['churn'].value_counts(), labels=['Churn', 'Not Churn'], colors=['#3498db', '#85c1e9'], autopct='%1.1f%%', startangle=140, textprops={'fontsize': 12})\n",
    "ax2.set_title(\"Churn Rate Distribution\", fontsize=16)\n",
    "\n",
    "plt.tight_layout()\n",
    "sns.despine(ax=ax1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2df82cf0",
   "metadata": {},
   "source": [
    "<div style=\"border-bottom: 15px solid lightblue; border-radius: 10px; width: 100%;\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b8b3371",
   "metadata": {},
   "source": [
    " ## Churn on Mobile Platforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b55f67ac",
   "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>phone</th>\n",
       "      <th>Android</th>\n",
       "      <th>iPhone</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>churn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>churn</th>\n",
       "      <td>11876</td>\n",
       "      <td>19057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>not_churn</th>\n",
       "      <td>3146</td>\n",
       "      <td>15525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "phone      Android  iPhone\n",
       "churn                     \n",
       "churn        11876   19057\n",
       "not_churn     3146   15525"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_taxi.churn, df_taxi.phone)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "297d6bd2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vizualization of churn rate\n",
    "plt.figure(figsize=(8, 5))\n",
    "fig = sns.histplot(data=df_taxi[['churn', 'phone']], \n",
    "                   x='churn', \n",
    "                   stat='count', \n",
    "                   hue=df_taxi.phone,\n",
    "                   edgecolor='white', \n",
    "                   palette={'iPhone': '#3498db', 'Android': '#85c1e9'},\n",
    "                   multiple='stack',\n",
    "                   shrink=0.8, \n",
    "                   binwidth=1,\n",
    "                   fill=True\n",
    "                  )\n",
    "\n",
    "plt.title(\"Histogram Churn on Mobile Platforms\", fontsize=16)\n",
    "plt.xlabel(\"Churn Status\", fontsize=14)\n",
    "plt.ylabel(\"Drivers\", fontsize=14)\n",
    "\n",
    "plt.xticks([0, 1], ['Churn', 'Not Churn'], fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "for p in fig.patches:\n",
    "    height = p.get_height()\n",
    "    plt.text(p.get_x() + p.get_width() / 2., p.get_y()+height+100, f\"{int(height)}\", ha=\"center\", fontsize=12)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b23ec5c",
   "metadata": {},
   "source": [
    "<div style=\"border-bottom: 15px solid lightblue; border-radius: 10px; width: 100%;\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2660ea0",
   "metadata": {},
   "source": [
    "## Churn by City"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "994d4a07",
   "metadata": {},
   "source": [
    "`churn` and `city` are categorical variables, therefore, a chi-square test (*χ2*-test) is used to determine the relationship between two categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4c6069d5",
   "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>city</th>\n",
       "      <th>Astapor</th>\n",
       "      <th>King's Landing</th>\n",
       "      <th>Winterfell</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>churn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>churn</th>\n",
       "      <td>12306</td>\n",
       "      <td>3767</td>\n",
       "      <td>15123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>not_churn</th>\n",
       "      <td>4228</td>\n",
       "      <td>6363</td>\n",
       "      <td>8213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "city       Astapor  King's Landing  Winterfell\n",
       "churn                                         \n",
       "churn        12306            3767       15123\n",
       "not_churn     4228            6363        8213"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab = pd.crosstab(df_taxi.churn, df_taxi.city)\n",
    "tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e0f45373",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3821.5510225559633\n",
      "0.0\n",
      "2\n",
      "[[10315.89328  6320.3096  14559.79712]\n",
      " [ 6218.10672  3809.6904   8776.20288]]\n"
     ]
    }
   ],
   "source": [
    "stat, p, dof, expected =ss.chi2_contingency(tab)\n",
    "print(stat, p, dof, expected, sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b763bfbe",
   "metadata": {},
   "source": [
    "### Note \n",
    "The results suggest that there is a statistically significant relationship between the variables in the contingent tab table because the p-value is very close to zero. This means that the null hypothesis of independence of the variables is rejected in favor of the alternative hypothesis that there is a relationship between the variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b70e0e0",
   "metadata": {},
   "source": [
    "<div style=\"border-bottom: 15px solid lightblue; border-radius: 10px; width: 100%;\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "815e72fd",
   "metadata": {},
   "source": [
    "## Difference in Activity in the First 30 Days of Drivers by City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "09def230",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Iz89n3LhxAERHR7N//34aGxvtc3JzcxkyZAjBwcFO1RAREelspj5CXr58OYcPH2bDhg307duXEydO2Mf69u2Lr68vSUlJrFy5kr59+3LRRRfx+9//npCQEKZMmQJAYmIiGzZsYOHChcycOZNPPvmEzZs321/L7EwNERGRzmbaQG5qauLPf/4z9fX13H333a3Gd+/ezcUXX8y8efNoaGhg0aJF1NbWEh0dTUZGhv0mreDgYDZs2MCyZctISEigX79+LFiwgISEBHuttmqIiIh0NlMF8ooVK+yPu3XrxieffNLmPl5eXqSkpJCSknLGOWPGjCE7O7tdNURERDqTqa8hi4iInC8UyCIiIiagQBYRETEBBbKIiIgJKJBFRERMQIEsIiJiAgpkERERE1Agi4iImIACWURExAQUyCIiIiagQBYRETEBBbKIiIgJKJBFRERMQIEsIiJiAgpkERERE1Agi4iImIACWURExAQUyCIiIiagQBYRETEBBbKIiIgJKJBFRERMQIEsIiJiAgpkERERE1Agi4iImIACWURExAQUyCIiIiagQBYRETEBBbKIiIgJKJBFRERMQIEsIiJiAgpkERERE1Agi4iImIACWURExAQUyCIiIiagQBYRETEBBbKIiIgJKJBFRERMQIEsIiJiAgpkEREREzBVIKenpzNt2jSHbQUFBSQlJREZGUlcXBwZGRkO401NTaxZs4bY2FgiIiJITk6muLi4w2uIiIh0JtME8qZNm1izZo3DtlOnTjF9+nRCQ0PJyclh7ty5pKWlkZOTY5+Tnp5OVlYWS5cuJTs7G4vFwqxZs7DZbB1WQ0REpLN1eSAfP36cmTNnkpaWxpAhQxzGtm7diq+vL0uWLGHo0KEkJiZyzz33sH79egBsNhuZmZnMnTuXyZMnExYWxurVqzl+/Dg7d+7ssBoiIiKdrcsD+bPPPqNXr168/vrrREREOIzt27eP6OhovL297dtiYmIoKiqirKyMwsJCqqqqiImJsY8HBQUxatQo9u7d22E1REREOpt321M6V3x8PPHx8acdKykpYfjw4Q7b+vfvD8DRo0cpKSkBYODAga3mHDt2rMNquMIwDKqrq13eH6Cmpsbhb0/j6f2B5/eo/tyfp/dohv4CAgKcmtflgXw2tbW1+Pr6Omzr3r07AHV1dfYf8OnmlJeXd1gNV9TX11NQUODy/gAWiwUAq9Vqf2wYRrtqmpHVau3qJXQ6T+9R/bk/T++xK/sbO3asU/NMHch+fn6tbqyqq6sDmp9x+Pn5Ac3XgVset8zx9/fvsBqu8PHxYdiwYS7vD83P6IqLiwkNDW3XWsyqpqYGq9Xqsf2B5/eo/tyfp/foTv2ZOpBDQkIoLS112Nby9YABA2hoaLBvGzRokMOcsLCwDqvhCovF4vRpirb4+/t3WC0z8vT+wPN7VH/uz9N7dIf+uvymrrOJjo5m//79NDY22rfl5uYyZMgQgoODCQsLIzAwkLy8PPt4RUUF+fn5jBs3rsNqiIiIdDZTB3JiYiKVlZUsXLiQgwcPsm3bNjZv3szs2bOB5uu+SUlJrFy5kt27d1NYWMj8+fMJCQlhypQpHVZDRESks5n6lHVwcDAbNmxg2bJlJCQk0K9fPxYsWEBCQoJ9zrx582hoaGDRokXU1tYSHR1NRkaG/SatjqghIiLS2SyGJ96228U+/fRTAMLDw9tVp7q6msLCQsLCwkx/7cMV1dXVFBQUMHLkSI/sDzy/R/Xn/jy9R3fqz9SnrEVERM4XCmQRERETUCCLiIiYgAJZRETEBBTIIiIiJqBAFhERMQEFsoiIiAkokEVERExAgSwiImICCmQRERETUCCLiIiYgAJZRETEBBTIIiIiJqBAFhERMQEFsoiIiAkokEVERExAgSwiImICCmQRERETUCCLiIiYgAJZRETEBBTIIiIiJqBAFhERMQGXAnnv3r1UVVWddqyiooL//d//bdeiREREzjcuBfJdd93FoUOHTjuWn5/Po48+2q5FiYiInG+8nZ2YmprKsWPHADAMgyVLlhAYGNhqntVq5YILLui4FYqIiJwHnD5Cvv766zEMA8Mw7Ntavm75061bNyIjI1m+fHmnLFZERMRTOX2EHB8fT3x8PADTpk1jyZIlDB06tNMWJiIicj5xOpC/6+WXX+7odYiIiJzXXArkmpoaXnjhBd555x1qampoampyGLdYLOzatatDFigiInI+cCmQly1bRk5ODldccQUjR46kWze9nFlERKQ9XArkt956i/nz53Pvvfd29HpERETOSy4d2jY0NDBmzJiOXouIiMh5y6VAnjhxIu+9915Hr0VEROS85dIp6xtvvJHFixdz8uRJIiIi8Pf3bzXnZz/7WXvXJiIict5wKZAffPBBALZv38727dtbjVssFgWyiIjIOXApkHfv3t3R6xARETmvuRTIF110UUevQ0RE5LzmUiCvXbu2zTkPPPCAK6VFRETOSx0eyIGBgfTv31+BLCIicg5cetlTYWFhqz8ffvgh69evp1evXjz++OMdusj6+npWr15NXFwcUVFR3HHHHXz44Yf28YKCApKSkoiMjCQuLo6MjAyH/ZuamlizZg2xsbFERESQnJxMcXGxw5y2aoiIiHSmDnvPy4CAAGJjY7n//vv53e9+11FlAXj++efJyclh6dKlbN++nUsvvZRZs2Zx/PhxTp06xfTp0wkNDSUnJ4e5c+eSlpZGTk6Off/09HSysrJYunQp2dnZWCwWZs2ahc1mA3CqhoiISGdy6ZT12QwcOJBDhw51aM3du3fz4x//mIkTJwLwyCOP8Nprr/HPf/4Tq9WKr68vS5Yswdvbm6FDh1JcXMz69etJTEzEZrORmZlJSkoKkydPBmD16tXExsayc+dOpk6dytatW89aQ0REpLN12BGyYRgcPXqU9evXd/hd2L179+add97hyJEjNDY2kp2dja+vLyNHjmTfvn1ER0fj7f3f5xYxMTEUFRVRVlZGYWEhVVVVxMTE2MeDgoIYNWoUe/fuBWizhoiISGdz6Qg5LCwMi8Vy2jHDMDr8lPXChQuZP38+11xzDV5eXnTr1o20tDQGDRpESUkJw4cPd5jfv39/AI4ePUpJSQnQfOT+/TnHjh0DaLNGcHBwh/YjIiLyfS4F8v3333/aQA4MDCQuLo7Q0ND2rsvBoUOHCAoKYt26dQwYMIDXXnuN1NRUXnnlFWpra/H19XWY3717dwDq6uqoqakBOO2c8vJygDZruMIwDKqrq13at0XL2lv+9jSe3h94fo/qz/15eo9m6C8gIMCpeS4F8ty5c13ZzSVfffUVKSkpbNq0iXHjxgEQHh7OwYMHee655/Dz87PfnNWiJUQDAgLw8/MDwGaz2R+3zGl5D+62ariivr6egoICl/Zt0fKkx2q12h8bhtGummZktVq7egmdztN7VH/uz9N77Mr+xo4d69Q8l2/qstlsbNu2jby8PCoqKujTpw/jxo0jISHBfnTZET755BPq6+sJDw932B4REcF7773HhRdeSGlpqcNYy9cDBgygoaHBvm3QoEEOc8LCwgAICQk5aw1X+Pj4MGzYMJf2bVFTU0NxcTGhoaGn/QAPd1dTU4PVavXY/sDze1R/7s/Te3Sn/lwK5IqKCu666y4KCwu58MIL6devH0VFRbz55pu8+uqrbNmyhZ49e3bIAluu/X7++ecOn8F84MABBg8eTGRkJFlZWTQ2NuLl5QVAbm4uQ4YMITg4mJ49exIYGEheXp49kCsqKsjPzycpKQmA6Ojos9ZwhcVicfno+vv8/f07rJYZeXp/4Pk9qj/35+k9ukN/Lt1lvWrVKkpKSnjllVd4++23yc7O5u233+aVV16hrKyMtLS0DlvgmDFjGDduHKmpqXzwwQdYrVaeffZZcnNzuffee0lMTKSyspKFCxdy8OBBtm3bxubNm5k9ezbQfO04KSmJlStXsnv3bgoLC5k/fz4hISFMmTIFoM0aIiIinc2lQN69ezcPPvig/Zpui3HjxjFv3jzeeuutDlkcQLdu3UhPTycmJoZHH32Um2++mQ8++IBNmzYRGRlJcHAwGzZsoKioiISEBNauXcuCBQtISEiw15g3bx633HILixYt4he/+AVeXl5kZGTYb+RypoaIiEhncumUdVVVFZdccslpxy655BK++eab9qyplV69erF48WIWL1582vExY8aQnZ19xv29vLxISUkhJSXljHPaqiEiItKZXDpCvvTSS3nnnXdOO7Z7924GDx7crkWJiIicb1w6Qp4xYwYPPfQQNpuNm266iQsuuICvv/6aN954g9dee40lS5Z08DJFREQ8m0uBfOONN2K1WnnhhRd47bXX7Nt9fHy4//77ue222zpsgSIiIucDlwK5urqaOXPmkJSUxD//+U/Ky8s5duwYt912G7169eroNYqIiHi8c7qGXFBQwM9+9jM2bdoENH9Iw6RJk5g0aRLPPvssd9xxR4d/0pOIiMj5wOlAPnz4MPfccw/l5eWt3oHK19eXxx57jKqqKu644w77BzqIiIiIc5wO5Jdeeok+ffrwxz/+keuuu85hzN/fn6SkJHJycggICOCFF17o8IWKiIh4MqcDOTc3l5kzZ9K7d+8zzgkODmb69Onk5uZ2xNpERETOG04H8okTJ5x6ffHw4cN1ylpEROQcOR3Iffv2bfWJSKdz8uTJsx5FS/v94e0DXb0EERHpYE4HcnR0NNu2bWtz3vbt2xk5cmS7FiVn19TkeZ+JLCJyvnM6kKdNm0ZeXh4rVqygrq6u1bjNZuPpp5/m/fff58477+zQRYqIiHg6p98YJDw8nEcffZTf/va3/OlPf2LChAlcfPHFNDY2cvToUfLy8jh16hS/+tWviI2N7cw1i4iIeJxzeqeuO++8k7CwMDIyMti9e7f9SLlHjx5MnDiR5ORkIiIiOmWhIiIinuyc3zpz7NixjB07FoBTp07RrVs3vV2miIhIO7n0XtYt+vTp01HrEBEROa+59HnIIiIi0rEUyCIiIiagQBYRETEBBbKIiIgJKJBFRERMQIEsIiJiAgpkERERE1Agi4iImIACWURExAQUyCIiIiagQBYRETEBBbKIiIgJKJBFRERMQIEsIiJiAgpkERERE1Agi4iImIACWURExAQUyCIiIiagQBYRETEBBbKIiIgJKJBFRERMQIEsIiJiAgpkERERE1Agi4iImIDbBPL27du58cYbCQ8PZ+rUqezYscM+VlBQQFJSEpGRkcTFxZGRkeGwb1NTE2vWrCE2NpaIiAiSk5MpLi52mNNWDRERkc7kFoH8pz/9iccee4zbbruNN998kxtvvJGHHnqIjz76iFOnTjF9+nRCQ0PJyclh7ty5pKWlkZOTY98/PT2drKwsli5dSnZ2NhaLhVmzZmGz2QCcqiEiItKZvLt6AW0xDIO0tDTuvvtu7r77bgDuv/9+PvzwQ/bs2cOePXvw9fVlyZIleHt7M3ToUIqLi1m/fj2JiYnYbDYyMzNJSUlh8uTJAKxevZrY2Fh27tzJ1KlT2bp161lriIiIdDbTHyF/8cUXfPXVV9x0000O2zMyMpg9ezb79u0jOjoab+//PreIiYmhqKiIsrIyCgsLqaqqIiYmxj4eFBTEqFGj2Lt3L0CbNURERDqb6Y+QrVYrANXV1cyYMYP8/Hwuvvhi7rvvPuLj4ykpKWH48OEO+/Tv3x+Ao0ePUlJSAsDAgQNbzTl27BhAmzWCg4PPed2GYVBdXX3O+31XTU2Nw98t6usb2l3bDM7Unyfx9B7Vn/vz9B7N0F9AQIBT80wfyJWVlQCkpqbywAMP8PDDD/PXv/6VOXPmsHHjRmpra/H19XXYp3v37gDU1dXZ/xFON6e8vBygzRquqK+vp6CgwKV9W1gsFqD5SUnLY4Cvv/6WwsI6DMNoV32zaHnS5ck8vUf15/48vceu7G/s2LFOzTN9IPv4+AAwY8YMEhISABg5ciT5+fls3LgRPz8/+81ZLVpCNCAgAD8/PwBsNpv9ccscf39/gDZruLruYcOGubRvi5qaGoqLiwkNDbWvFaDgeBFhYUPaVdsMampqsFqtrfrzJJ7eo/pzf57eozv1Z/pADgkJAWh1SnnYsGG8++67XHTRRZSWljqMtXw9YMAAGhoa7NsGDRrkMCcsLMz+Pc5WwxUWi8XlMP8+f39/h1o+Pt4dVtsMvt+fJ/L0HtWf+/P0Ht2hP9Pf1DVq1Ch69OjBxx9/7LD9wIEDDBo0iOjoaPbv309jY6N9LDc3lyFDhhAcHExYWBiBgYHk5eXZxysqKsjPz2fcuHEAbdYQERHpbKYPZD8/P2bOnMm6det48803+fLLL3n++ef5xz/+wfTp00lMTKSyspKFCxdy8OBBtm3bxubNm5k9ezbQfO04KSmJlStXsnv3bgoLC5k/fz4hISFMmTIFoM0aIiIinc30p6wB5syZg7+/P6tXr+b48eMMHTqU5557jvHjxwOwYcMGli1bRkJCAv369WPBggX2680A8+bNo6GhgUWLFlFbW0t0dDQZGRn2G7mCg4PbrCEiItKZ3CKQAaZPn8706dNPOzZmzBiys7PPuK+XlxcpKSmkpKSccU5bNURERDqT6U9Zi4iInA8UyCIiIiagQBYRETEBBbKIiIgJKJBFRERMQIEsIiJiAgpkERERE1Agi4iImIACWURExAQUyCIiIiagQBYRETEBBbKIiIgJKJBFRERMQIEsIiJiAgpkERERE1Agi4iImIACWURExAQUyCIiIiagQBYRETEBBbKIiIgJKJBFRERMQIEsIiJiAgpkERERE1Agi4iImIACWURExAQUyCIiIiagQBYRETEBBbKIiIgJKJBFRERMQIEsIiJiAgpkERERE1Agi4iImIACWURExAQUyCIiIiagQBYRETEBBbKIiIgJKJBFRERMQIEsIiJiAgpkERERE3CrQC4qKiIqKopt27bZtxUUFJCUlERkZCRxcXFkZGQ47NPU1MSaNWuIjY0lIiKC5ORkiouLHea0VUNERKSzuU0g19fX8/DDD1NdXW3fdurUKaZPn05oaCg5OTnMnTuXtLQ0cnJy7HPS09PJyspi6dKlZGdnY7FYmDVrFjabzekaIiIinc27qxfgrOeee44ePXo4bNu6dSu+vr4sWbIEb29vhg4dSnFxMevXrycxMRGbzUZmZiYpKSlMnjwZgNWrVxMbG8vOnTuZOnVqmzVERER+CG5xhLx3716ys7N5+umnHbbv27eP6OhovL3/+7wiJiaGoqIiysrKKCwspKqqipiYGPt4UFAQo0aNYu/evU7VEBER+SGY/gi5oqKCBQsWsGjRIgYOHOgwVlJSwvDhwx229e/fH4CjR49SUlIC0Gq//v37c+zYMadqBAcHu7RuwzAcTq+7oqamxuHvFvX1De2ubQZn6s+TeHqP6s/9eXqPZugvICDAqXmmD+QlS5YQGRnJTTfd1GqstrYWX19fh23du3cHoK6uzv4PcLo55eXlTtVwVX19PQUFBS7vD2CxWACwWq32xwBff/0thYV1GIbRrvpmYbVau3oJnc7Te1R/7s/Te+zK/saOHevUPFMH8vbt29m3bx9vvPHGacf9/PzsN2e1aAnRgIAA/Pz8ALDZbPbHLXP8/f2dquEqHx8fhg0b5vL+0PyMrri4mNDQUPt6AQqOFxEWNqRdtc2gpqYGq9Xaqj9P4uk9qj/35+k9ulN/pg7knJwcysrKiIuLc9i+ePFiMjIyuPDCCyktLXUYa/l6wIABNDQ02LcNGjTIYU5YWBgAISEhZ63hKovF0q5A/y5/f3+HWj4+3h1W2wy+358n8vQe1Z/78/Qe3aE/UwfyypUrqa2tddh23XXXMW/ePG688Ub+93//l6ysLBobG/Hy8gIgNzeXIUOGEBwcTM+ePQkMDCQvL88eyBUVFeTn55OUlARAdHT0WWuIiIj8EEx9l/WAAQMYPHiwwx+A4OBgLrroIhITE6msrGThwoUcPHiQbdu2sXnzZmbPng00XztOSkpi5cqV7N69m8LCQubPn09ISAhTpkwBaLOGiIjID8HUR8htCQ4OZsOGDSxbtoyEhAT69evHggULSEhIsM+ZN28eDQ0NLFq0iNraWqKjo8nIyLDfyOVMDRERkc7mdoH8+eefO3w9ZswYsrOzzzjfy8uLlJQUUlJSzjinrRpm9oe3D3BL/PC2J4qIiKmZ+pS1tK2pyTNe+iQicr5TIIuIiJiAAllERMQEFMgiIiImoEAWERExAQWyiIiICSiQRURETECBLCIiYgIKZBERERNQIIuIiJiAAllERMQEFMgiIiImoEAWERExAQWyiIiICSiQRURETECBLCIiYgIKZBERERNQIIuIiJiAAllERMQEFMgiIiImoEAWERExAQWyiIiICSiQRURETECBLCIiYgIKZBERERNQIIuIiJiAAllERMQEFMgiIiImoEAWERExAQWyiIiICSiQRURETECBLCIiYgLeXb0AOb2GxibSt/0LH6OasLCuXo2IiHQ2HSGbVG1dA3/76Bi7/1lO6amarl6OiIh0MgWySQUG+DJmaF8MYOeeI129HBER6WQKZBO7fvwlALzz4VfY6hu7eDUiItKZFMgmdvmIfvTu4cW31fW8/8+vuno5IiLSiRTIJtatm4VxPwoEYNfeL7t4NSIi0pkUyCZ32eAAAPK/KKO8sq6LVyMiIp3F9IH8zTff8MQTTzBp0iQuv/xyfvGLX7Bv3z77eEFBAUlJSURGRhIXF0dGRobD/k1NTaxZs4bY2FgiIiJITk6muLjYYU5bNbpSn0BvQgf2pMmAPZ+VdPVyRESkk5g+kB966CE+/vhjnnnmGf7whz8wevRoZsyYwaFDhzh16hTTp08nNDSUnJwc5s6dS1paGjk5Ofb909PTycrKYunSpWRnZ2OxWJg1axY2mw3AqRpd7YqR/QHI/dexLl6JiIh0FlO/MUhxcTH/+Mc/+J//+R8uv/xyABYuXMh7773Hm2++iZ+fH76+vixZsgRvb2+GDh1KcXEx69evJzExEZvNRmZmJikpKUyePBmA1atXExsby86dO5k6dSpbt249aw0ziB7Vn61vH+KfB05QXVvf1csREZFOYOoj5D59+vDSSy9x2WWX2bdZLBYMw6C8vJx9+/YRHR2Nt/d/n1fExMRQVFREWVkZhYWFVFVVERMTYx8PCgpi1KhR7N27F6DNGmZwSf8eDLygB/UNTXz4eWlXL0dERDqBqQM5KCiIyZMn4+vra9+2Y8cOvvzySyZOnEhJSQkhISEO+/Tv33x69+jRo5SUNF9zHThwYKs5x441n/5tq4YZWCwWxgy7AADrsYouXo2IiHQGU5+y/r79+/fz2GOPcc011xAfH8/y5csdwhqge/fuANTV1VFT0/yWk6ebU15eDkBtbe1Za7jKMAyqq6td3h+wr7+mpoagAC8ATpyson8ff3vt+vqGdn+frvLd/jyVp/eo/tyfp/dohv4CAgKcmuc2gbxr1y4efvhhIiIieOaZZwDw8/Oz35zVoiVEAwIC8PPzA8Bms9kft8zx9/d3qoar6uvrKSgocHl/aD4yBrBardRWVQFw5FgZ3Rr9KCxsXuPXX39rfwzNTwTcjdVq7eoldDpP71H9uT9P77Er+xs7dqxT89wikF955RWWLVvGlClTWLlypf2INiQkhNJSx2uqLV8PGDCAhoYG+7ZBgwY5zAn7z0cotVXDVT4+PgwbNszl/aH5GV1xcTGhoaFUd6vkjbxT1OPDBRdcQFjYEAAKjhfZH7ubmpoarFYroaGh9idInsbTe1R/7s/Te3Sn/kwfyFu2bOGpp55i2rRpPPbYY3Tr9t/L3tHR0WRlZdHY2IiXV/Mp3dzcXIYMGUJwcDA9e/YkMDCQvLw8eyBXVFSQn59PUlKSUzVcZbFY2nWE/V3+/v4MvKB5beWVNnx8vO21v/vYXfn7+7t9D23x9B7Vn/vz9B7doT9T39RVVFTEb3/7W6ZMmcLs2bMpKyvjxIkTnDhxgm+//ZbExEQqKytZuHAhBw8eZNu2bWzevJnZs2cDzdeOk5KSWLlyJbt376awsJD58+cTEhLClClTANqsYRZ9gpqva3/zbR1NbnhaWkREzs7UR8h//etfqa+vZ+fOnezcudNhLCEhgRUrVrBhwwaWLVtGQkIC/fr1Y8GCBSQkJNjnzZs3j4aGBhYtWkRtbS3R0dFkZGTYT3sHBwe3WcMMegd2x2KBJgPqbPrkJxERT2PqQP7lL3/JL3/5y7POGTNmDNnZ2Wcc9/LyIiUlhZSUFJdrmIGXVzd6BXbnm2/rqKlr6OrliIhIBzP1KWtx1Ldn853iCmQREc+jQHYjLdeRa2oVyCIinkaB7Eb6BukIWUTEUymQ3UgfBbKIiMdSILuRvj3/c8pagSwi4nEUyG7kv0fIetmTiIinUSC7kZZryNU6QhYR8TgKZDfy3WvI7vghEiIicmYKZDfS5z/XkJuaDKpq6rt4NSIi0pEUyG7E18eLQH8fAE5W1HbxakREpCMpkN3MBb2bPz7sxDee+WHiIiLnKwWymwkJbv74sJKvq7p4JSIi0pEUyG5m4AWBABwtUyCLiHgSBbKbGXhBDwCO6QhZRMSjKJDdzMCWU9Y6QhYR8SgKZDfTcsr62NfVNDbptcgiIp5CgexmLujtTzcLNDQ2UVbe9p3Wf3j7wA+wKhERaS8Fspvx6mYhMMAXcO46cpOOokVE3IIC2Q31DGh+cxDd2CUi4jkUyG6o53+OkHVjl4iI51Agu6GWI+SjOkIWEfEYCmQ31LOH89eQRUTEPSiQ3VDLKetjZVU06WMYRUQ8ggLZDQX6+9DD34c6WyNf60MmREQ8ggLZDXXrZmHsiP4AfHVCp61FRDyBAtlNjRs1AICvSiu7eCUiItIRFMhu6vIR/elmgVPf1nHilE5bi4i4OwWym+oV2J0Rg/sCsK/weBevRkRE2kuB7Mai/3PaOu9fx7p4JSIi0l4KZDd25ZgLAfjo81KdthYRcXMKZDd2Ub9ABvQNoMmAt/KKz2lffQqUiIi5KJDd3I8u6Q00B3JjY5PT++lToEREzEWB7OYGDQikV6AvJytq2ZNf0tXLERERFymQ3ZyXVzemXDEYgE1v5lNX33jONXT6WkSk6ymQPUBi/I/oG9Sdo19Xkb3z83PeX6evRUS6ngLZAwT6+/DLmyMAyHnnIJ8e+rqLVyQiIudKgewhJoQPZFLkRTQ1GTyVkceBL0919ZJEROQcKJA9yLzboxgz7AJq6hpY/FIu1mMVXb0kERFxkgLZg3T38WLh9CsYMbgPlTX1PP7i/1FeWdfVyxIREScokD1MgJ8PS2bGcOmFvfjm2zre/LuVNdkfcfj4ty7X1F3YIiKdT4H8H01NTaxZs4bY2FgiIiJITk6muPjc3v3KLAIDfHny3glcNjSYJsNg554vmfO7t1mamUd+Udk519Nd2CIinU+B/B/p6elkZWWxdOlSsrOzsVgszJo1C5vN1tVLc0nvnt1ZPmci18cMYvzoEADyPishde3fmbvyHf7xyVG2/+0QhdaT5/QOX989Wj7TYxEROXfeXb0AM7DZbGRmZpKSksLkyZMBWL16NbGxsezcuZOpU6d28Qpd179PAA/cGsXh49+y/W+HeHvfYfvNXl989S8Aulks7C0opX+fAPr28qNPz+4E9/KjT08/+vbyo66+EcMwsFgsDkfLZ3osIiLnToEMFBYWUlVVRUxMjH1bUFAQo0aNYu/evW4dyC0uGdCTuT+PJOn/hVFQdJI//18Rfr7eFBafpLzSxr8Pf8O/D39zxv3feL+I4F5+VNfWs6+gFB/vbpSV1/Dvw9/Q0Ghw9EQlh0sr6Rngi7dXN7y9LP/5u/nxv74oY2zYALy9uvHRgeNMuOxCGhvqOXashlqvr+kR4Ie3Vze6dbNgwXLGdVjOPPSfCa4PW9os3kbt0+xeW1vLV2U2un9VgZ/fWc62tGPdzd+7HT+zdtSura3l+Df19Dheib9fw2l2bqO2i9/XGWfbva3aLaM1tTWUVdRzrKwK/6qm1hPOuH/X/Hu0Wfp0/43W1PBNVQNff1ODf53r625PX87+e7gyobbGRmVtI+WVNmyNXqf53mfet7uvF36+P1xMWgzDOO8Pbd566y3mzp3Lxx9/jJ+fn337r371K2pra3nxxRfPqd6HH36IYRj4+Pi0a12GYdDQ0IC3t7fDf7BVtQ308PNu9fh0zjT+3e2VNfX4eHWj0TBoavrvn8YmgybDQP+FiMj5qk/P7vh4t+/qbvfu3RkxYkSb83SEDNTUNH+WsK+vr8P27t27U15efs71WsKz/c/wLa3WBM3vzHW6x6dzpvHvbu8Z0Pp7iIjID0uBDPajYpvN5nCEXFdXh7+//znXi4qK6rC1iYjI+UF3WQMDBw4EoLS01GF7aWkpISEhXbEkERE5zyiQgbCwMAIDA8nLy7Nvq6ioID8/n3HjxnXhykRE5HyhU9Y0XztOSkpi5cqV9O3bl4suuojf//73hISEMGXKlK5enoiInAcUyP8xb948GhoaWLRoEbW1tURHR5ORkXHam6pEREQ6ml72JCIiYgK6hiwiImICCmQRERETUCCLiIiYgAJZRETEBBTIIiIiJqBAFhERMQEFsgk1NTWxZs0aYmNjiYiIIDk5meLi4q5elsu++eYbnnjiCSZNmsTll1/OL37xC/bt22cfLygoICkpicjISOLi4sjIyOjC1bZPUVERUVFRbNu2zb7NU/rbvn07N954I+Hh4UydOpUdO3bYxzyhx/r6elavXk1cXBxRUVHccccdfPjhh/Zxd+4xPT2dadOmOWxrqx93+j10uv7efvttEhMTiYqKIj4+nqeffpra2lr7uCn7M8R0nnvuOWPChAnGu+++axQUFBjJycnGlClTjLq6uq5emkumT59u/OQnPzH27t1rHDp0yHjqqaeMMWPGGAcPHjROnjxpjB8/3li4cKFx8OBB4w9/+IMRHh5u/OEPf+jqZZ8zm81m3Hzzzcbw4cONnJwcwzAMj+lv+/btxsiRI41NmzYZVqvVWLt2rREWFmZ8+OGHHtNjWlqacdVVVxnvv/++YbVajYULFxqXX365UVJS4tY9bty40RgxYoSRlJRk3+ZMP+7ye+h0/e3du9cYOXKk8eKLLxpWq9X429/+ZkyePNl45JFH7HPM2J8C2WTq6uqMqKgoY8uWLfZt5eXlxpgxY4w333yzC1fmGqvVagwfPtzYv3+/fVtTU5MxZcoU49lnnzVeeOEFIzY21qivr7ePr1q1yrj++uu7YrntsmrVKmPatGkOgewJ/TU1NRlXX321sWLFCoftycnJxgsvvOARPRqGYfzkJz8xli9fbv/622+/NYYPH2785S9/ccseS0pKjBkzZhiRkZHGDTfc4BBYbfXjDr+Hztbfr3/9a2P69OkO87dv326MGjXKqKurM21/OmVtMoWFhVRVVRETE2PfFhQUxKhRo9i7d28Xrsw1ffr04aWXXuKyyy6zb7NYLBiGQXl5Ofv27SM6Ohpv7/++i2tMTAxFRUWUlZV1xZJdsnfvXrKzs3n66acdtntCf1988QVfffUVN910k8P2jIwMZs+e7RE9AvTu3Zt33nmHI0eO0NjYSHZ2Nr6+vowcOdIte/zss8/o1asXr7/+OhEREQ5jbfXjDr+HztZfcnIyCxYsaLVPQ0MDlZWVpu1PgWwyJSUlwH8/ErJF//79OXbsWFcsqV2CgoKYPHmyw3uC79ixgy+//JKJEydSUlLS6iMu+/fvD8DRo0d/0LW6qqKiggULFrBo0aJW/26e0J/VagWgurqaGTNmMGHCBG699VbefvttwDN6BFi4cCHe3t5cc801hIeHs3r1ap599lkGDRrklj3Gx8ezatUqLrnkklZjbfXjDr+HztbfqFGjCAsLs39ts9nYuHEjo0ePpm/fvqbtT4FsMjU1NQCtPtSie/fu1NXVdcWSOtT+/ft57LHHuOaaa4iPj6e2tva0vQJu0++SJUuIjIxsdQQJeER/lZWVAKSmpvLjH/+YzMxMrrrqKubMmUNubq5H9Ahw6NAhgoKCWLduHdnZ2dx8882kpqZSWFjoMT22aKsfT/o91NDQwIIFCzh48CCLFy8GzPt7Vp/2ZDJ+fn5A8zO6lsfQ/D+Jv79/Vy2rQ+zatYuHH36YiIgInnnmGaC5X5vN5jCv5X+IgICAH3yN52r79u3s27ePN95447Tj7t4fgI+PDwAzZswgISEBgJEjR5Kfn8/GjRs9osevvvqKlJQUNm3aZP8M9PDwcA4ePMhzzz3nET1+V1v9eMrvocrKSh588EHy8vJYs2aN/dS2WfvTEbLJtJxCKS0tddheWlra6hSTO3nllVeYO3cukyZNYv369fb/CUJCQk7bK8CAAQN+8HWeq5ycHMrKyuwvlYmKigJg8eLFTJ061e37A+z/3Q0fPtxh+7Bhwzhy5IhH9PjJJ59QX19PeHi4w/aIiAisVqtH9PhdbfXjCb+HSktLufPOO/noo49Yv3498fHx9jGz9qdANpmwsDACAwPJy8uzb6uoqCA/P9/+zN3dbNmyhaeeeoo777yTZ5991uE0UXR0NPv376exsdG+LTc3lyFDhhAcHNwVyz0nK1eu5M9//jPbt2+3/4Hmz9d+6aWX3L4/aL4e16NHDz7++GOH7QcOHGDQoEEe0WPLL+jPP//cYfuBAwcYPHiwR/T4XW314+6/h8rLy7n77rs5efIkW7Zscbh5C0z8e7bL7u+WM3rmmWeMK664wti1a5f99XHXXXed6V7/54wvvvjCGD16tHH//fcbpaWlDn8qKiqMr7/+2oiOjjZSU1ONf//730ZOTo4RHh5ubNu2rauX7rLvvuzJU/pbt26dERUVZbzxxhtGcXGxkZ6eboSFhRkffPCBR/TY2Nho3HHHHcYNN9xg5ObmGkVFRcbq1auNkSNHGh999JHb95iamurwsiBn+nGn30Pf7y81NdUYPXq0kZub2+r3TkNDg2EY5uxPgWxCDQ0Nxu9+9zsjJibGiIyMNGbNmmUcPny4q5flkueff94YPnz4af+kpqYahmEYH3/8sfHzn//cuOyyy4yrr77aePnll7t41e3z3UA2DM/pLzMz04iPjzdGjx5t/OQnPzF27txpH/OEHr/55htjyZIlRlxcnBEVFWXcdtttRl5enn3cnXv8fmAZRtv9uNPvoe/219jYaISHh5/x905LD2bsz2IYhtF1x+ciIiICuoYsIiJiCgpkERERE1Agi4iImIACWURExAQUyCIiIiagQBYRETEBBbKIiIgJKJBF2qCX6nc8/UxFWlMgi5zF7t27SU1NPeucRx55xOGN639II0aM4Lnnnuvwug0NDTz66KNcfvnlXH755XzwwQcd8r0qKipITU1l375957xvYWEhM2fOZNy4cYwfP57U1NRWHw4AsHHjRq699lrCw8P56U9/yq5du9q15u/qrJ+3CCiQRc5q06ZNbX5g+Zw5c1i7du0PtCJH2dnZ3HrrrR1e9/3332fbtm3cfffdvPjii4SHh3fI9yooKGD79u00NTWd034lJSXcfffdVFZWsnLlSpYsWcJHH33E9OnTaWhosM/bsGEDv//970lISGDt2rUMHjyYefPmsXfv3natW+SHoM9DFmmnQYMGddn3joyM7JS633zzDQA333wzl1xySad+L2dkZWVRU1PDCy+8QO/evQHo27cvd911F7m5ucTGxlJbW8uLL77IPffcw/333w/ApEmTuP3221m3bh2bNm3qsvWLOENHyCJnMG3aNPbs2cOePXsYMWIEeXl5jBgxgqysLK6++mquvPJK/v73v7c6ZR0fH8/q1atZvnw5V1xxBVdccQUpKSmcOnXKPufkyZM8/PDDXHXVVfZTqy0f3XguvnsKtWV9ubm5JCcnExERwZVXXsnTTz/tcBTZlkceeYRHHnkEgGuvvZZp06ad8Xt9/2dxtr7y8vK46667ALjrrrvsdZ1x11138eqrr9rDGMDHxwdo/pB5gI8//piKigquu+46+xyLxcKUKVPYs2cPtbW1Tn8/gD179nDbbbcRERHB9ddfz//93/+1mnPkyBEWLFjAxIkTGT16NBMmTGDBggX2f+unn36aMWPG8O233zrs99JLLxEVFUV1dTV1dXU8+eSTTJo0icsuu4wbbriBzMzMc1qreAYdIYucweLFi0lJSbE/LisrA2D16tU8+eST1NXVERkZyZtvvtlq3y1btjB48GB++9vfcvLkSVatWsUXX3zBa6+9Rrdu3UhJSaGsrIwnn3ySHj168Prrr5OamsrAgQMZP358u9b98MMPc8cddzBr1izeffddMjMzGTx4MLfffrtT+8+ZM4eQkBCef/551q5dy5AhQ8449/s/i1/96ldn7Gv06NE88cQT/OY3v+GJJ544pz779u1L3759AairqyM/P5/f/OY3hIaGMnHiRAAOHToEQGhoqMO+gwcPprGxkS+//JLhw4c79f0+++wzkpOTGT9+PGlpaRw9epSHHnrIYU5NTQ133XUXffr0YfHixfTs2ZP9+/ezbt06unfvzlNPPcUtt9xCZmYmf/nLXxxO92/fvp0bbriBgIAAnnjiCf7+97+TmprKBRdcwHvvvcfTTz9N7969ufnmm53+GYn7UyCLnMGwYcMIDAwEmk/XtnyY+e23384NN9xw1n0tFgsbN26kZ8+eQHOg3H///bz33nvExcWxZ88e5syZw7XXXgvA+PHj6d27N15eXu1e96233mo/ZTthwgR27drFu+++63QgDxo0yH4afuTIkVx88cVnnPv9n8XZ+goMDGTYsGFA88+25fG5uummmyguLqZ79+6sWbOG7t27A9iPQlv+zVr06NEDgMrKSqe/x4svvkjfvn15/vnn8fX1BaB3797Mnz/fPsdqtRISEsKKFSvsP6+YmBg+/fRT9uzZA8DQoUOJioriT3/6kz2QP/nkEw4dOsRvfvMboPlnduWVVzJ16lSg+WcWEBBAnz59zu0HI25PgSxyjkaMGNHmnKuvvtoextB8GtvHx4d9+/YRFxfH+PHjee655ygsLGTy5MlMmjSpzbu5nRUVFeXwdUhICNXV1R1S+/u+/7PozL5aLF68GIBt27Zx3333sWLFCn7605+e8UaxlpdYdevm/BW6/fv3ExcXZw9jgOuuu87hCdPIkSPZsmULTU1NHD58GKvVyr///W+++OILh0sEiYmJPP744xw5coSLL76Ybdu2MWjQIMaNGwc0/8yysrI4fvw4V199NZMnT7Y/oZLzi64hi5yj4ODgNuf079/f4etu3brRu3dvKioqgOZTvdOnT+fTTz/l0UcfJTY2lhkzZnD48OF2r8/Pz6/V9+6s1/1+/2fRmX21uOqqq7jqqqtYtWoVY8eOZd26dQAEBQUBUFVV5TC/5cnId58gtaW8vNx+iryFt7d3q6PWjRs3cuWVV3LttdfyyCOP8MEHH+Dv7+8w58Ybb8Tf35/XX38dm83Gjh07SEhIsI8vXLiQBx98kCNHjvDkk08SHx/P7bffTn5+vtPrFc+gQBbpBC13KbdobGzk1KlT9l/yPXv2JCUlhbfffpsdO3bw0EMP8eGHH/Lkk092wWo7Tmf1lZuby9/+9rdW2y+77DL7y9JarnUXFxc7zCkuLsbX19d+t7gzevfuzddff+2wzTAMysvL7V+/8cYbrFixguTkZHJzc/nHP/7BSy+91Ooado8ePbjhhhvYsWMH//jHP6ioqOBnP/uZfdzX15f77ruPHTt28M477/DEE09w+PBhfv3rXzu9XvEMCmSRsziX05zf9f7779vv/oXmNxhpaGhgwoQJfPXVV0yePJm//OUvAFx66aXMmjWLK6+8kpKSkg5Zd1dwpi9Xr5H/8Y9/ZMGCBQ7XgRsaGsjNzSUsLAxoPlUfEBDAX//6V/scwzDYuXMnV1xxhcPp57ZMmDCB9957j5qaGvu2999/n/r6evvX+/fvp2fPntx77732J1pVVVXs37+/1enzW265hQMHDpCZmUlMTAwXXnghALW1tVx//fX2u6ovvPBC7rzzTqZOnerW/y2Ia3QNWeQsgoKC+Oijj8jNzT2nm4JKSkq47777uOuuuzh27BjPPPMMEydOtN9ZHBISwtKlS6msrGTQoEH861//4m9/+xuzZ8/urFY63UUXXdRmXy2njd9991169eplD9O2zJw5k7feeot7772XmTNnYhgGL7/8MocOHbKHmb+/P8nJyaxbtw4fHx+ioqLIycnhs88+Y/PmzefUy/3338+uXbuYMWMGM2fO5NSpU6xevdr+UiuAMWPG8D//8z+sWLGCq6++mtLSUjIyMvj666/p1auXQ72xY8dy6aWXsmfPHlauXGnf7ufnx+jRo1m7di0+Pj6MGDGCoqIi/vjHP3L99def05rF/SmQRc7izjvv5F//+hezZs1i+fLlTu83depUgoKCePDBBwkICCAhIcHhDt21a9fyzDPPkJaWxqlTpxg4cCAPPPAA9957b2e08YNpq68f/ehH/PjHP+bVV1/l/fffP+1Lxk5n+PDhvPrqqzzzzDM8+uij2Gw2oqKieOWVVxzesOSBBx7Ay8uLrVu3kpmZybBhw0hPT2fs2LHn1EdoaCivvPIKK1asYP78+QQHB5OamsqKFSvscxISEjhy5Ag5OTls2bKFAQMGMHnyZO644w4ef/xxDh486HAneVxcHCdOnGDKlCkO3+s3v/kNzz77LJmZmZw4cYLg4GBuueUWfvWrX53TmsX9WQy9y7tIh4qPj+eKK65w+OUt5zfDMLjpppsYP348jz/+eFcvR0xKR8giJtPY2OjUXdHe3uf2v69hGDQ2NrY5z8vLC4vFck61XeXMO4h169bN5Wv53+Vs/x31/aD5tc+bNm3i008/xWq1kp6e3iF1xTMpkEVM5p577rG/scTZfP755+dU949//COPPvpom/OWL1/+g7xD1JEjR7jmmmvanJeQkNAhZxv27Nljf+vOs3nggQeYO3duu78fNF8jzsrKoqmpiWXLlnXp+56L+emUtYjJfPHFF61eS3s64eHh51T31KlTHDlypM15F1988Q/yLlE2m82pJxV9+vQ567uFOauyspKioqI25/Xv358BAwa0+/uJnCsFsoiIiAnodcgiIiImoEAWERExAQWyiIiICSiQRURETECBLCIiYgIKZBERERNQIIuIiJiAAllERMQE/j9jU2/pvZ2cGwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df_taxi.trips_in_first_30_days, \n",
    "            kde=True, \n",
    "           )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "518caec6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ShapiroResult(statistic=0.5414707660675049, pvalue=4.203895392974451e-45)\n",
      "ShapiroResult(statistic=0.6132168769836426, pvalue=1.6829594556541053e-42)\n",
      "ShapiroResult(statistic=0.545579195022583, pvalue=7.006492321624085e-45)\n"
     ]
    }
   ],
   "source": [
    "Astapor = df_taxi[df_taxi.city == 'Astapor'].trips_in_first_30_days\n",
    "Kings = df_taxi[df_taxi.city == \"King's Landing\"].trips_in_first_30_days\n",
    "Winterfell = df_taxi[df_taxi.city == 'Winterfell'].trips_in_first_30_days\n",
    "\n",
    "print(ss.shapiro(Astapor.sample(1000, random_state=17)))\n",
    "print(ss.shapiro(Kings.sample(1000, random_state=17)))\n",
    "print(ss.shapiro(Winterfell.sample(1000, random_state=17)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02745975",
   "metadata": {},
   "source": [
    "### Note\n",
    "In this case, all three p-values are very close to zero, which means that the null hypothesis of normality of the data is rejected. Thus, we can conclude that the data on the number of trips in the first 30 days do not follow a normal distribution.\n",
    "\n",
    "Accordingly, the Kraskell-Wallis test may be a more appropriate choice than the Shapiro-Wilk test for each group separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "70ce8e73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=221.32105325317454, pvalue=8.724567791938856e-49)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kruskal_results = ss.kruskal(Astapor, Kings, Winterfell)\n",
    "kruskal_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecf5e1fc",
   "metadata": {},
   "source": [
    "### Note\n",
    "We can conclude that there are statistically significant differences in the number of trips in the first 30 days between the cities of Astapor, Kings, and Winterfell. The P-value is very small, indicating that the differences between the groups cannot be explained by chance and you can consider the results statistically significant.\n",
    "\n",
    "---\n",
    "A post-hoc Dunn's test adjusted for Hill's method to compare groups of data (Astapor, King's Landing, and Winterfell) on the number of trips in the first 30 days and determine between which pairs of groups there are statistically significant differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "00e028a3",
   "metadata": {
    "scrolled": true
   },
   "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>Astapor</th>\n",
       "      <th>King's Landing</th>\n",
       "      <th>Winterfell</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Astapor</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.313616e-09</td>\n",
       "      <td>2.448501e-49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>King's Landing</th>\n",
       "      <td>1.313616e-09</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>8.440212e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Winterfell</th>\n",
       "      <td>2.448501e-49</td>\n",
       "      <td>8.440212e-10</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Astapor  King's Landing    Winterfell\n",
       "Astapor         1.000000e+00    1.313616e-09  2.448501e-49\n",
       "King's Landing  1.313616e-09    1.000000e+00  8.440212e-10\n",
       "Winterfell      2.448501e-49    8.440212e-10  1.000000e+00"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.posthoc_dunn(df_taxi[['city', 'trips_in_first_30_days']],\n",
    "                val_col='trips_in_first_30_days', \n",
    "                group_col='city', \n",
    "                p_adjust='holm'\n",
    "               )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad9cff6d",
   "metadata": {},
   "source": [
    "### Note\n",
    "The p-values between groups range from very close to zero (2.448501e-49, 8.440212e-10, etc.), which means that the differences between groups are statistically significant."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b0e8c9",
   "metadata": {},
   "source": [
    "<div style=\"border-bottom: 15px solid lightblue; border-radius: 10px; width: 100%;\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96853c40",
   "metadata": {},
   "source": [
    "## Difference in Activity in the First 30 Days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4b48bac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "churn = df_taxi[df_taxi.churn == \"churn\"].trips_in_first_30_days\n",
    "not_churn = df_taxi[df_taxi.churn == \"not_churn\"].trips_in_first_30_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4615cb52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(churn, kde=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a8acd996",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(not_churn, kde=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84215e97",
   "metadata": {},
   "source": [
    "The distributions are not normal so the Mann-Whitney U-test is used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bc1fa670",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=234767452.0, pvalue=0.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss.mannwhitneyu(churn, not_churn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "621a54a6",
   "metadata": {},
   "source": [
    "### Note\n",
    "A P-value close to zero indicates that the null hypothesis of equality of distributions between your two groups is rejected. Thus, the results indicate that there are statistically significant differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a0015630",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='churn', ylabel='trips_in_first_30_days'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(data=df_taxi, x='churn', y='trips_in_first_30_days')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f6ee364",
   "metadata": {},
   "source": [
    "<div style=\"border-bottom: 15px solid lightblue; border-radius: 10px; width: 100%;\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec7af09b",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "This finding may make practical sense when analyzing user behavior and the reasons why they may have left. Perhaps activity in the first 30 days plays a role in user retention, and differences in activity between cities may have influenced users' decision to stay or leave."
   ]
  }
 ],
 "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}