Final-challenge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STAT 4710J Final Data Challenge\n",
    "\n",
    "A taxi company is interested in predicting rider retention. To help explore this question, we have\n",
    "provided a sample dataset of a cohort of users who signed up for an rider share app account. We consider a user retained if they\n",
    "were “active” (i.e. took a trip) in the preceding 30 days. **The data was collected at the end of June 30, 2014.**\n",
    "We would like you to use this dataset to help understand what factors are the best predictors\n",
    "for retention, and offer suggestions to operationalize those insights to help the company.\n",
    "The data is in the attached file data_challenge.json. See below for a detailed\n",
    "description of the dataset. \n",
    "\n",
    "**Data description**\n",
    "\n",
    "city: city this user signed up in\n",
    "\n",
    "phone: primary device for this user\n",
    "\n",
    "signup_date: date of account registration; in the form ‘YYYYMMDD’\n",
    "\n",
    "last_trip_date: the last time this user completed a trip; in the form ‘YYYYMMDD’\n",
    "\n",
    "avg_dist: the average distance in miles per trip taken in the first 30 days after signup\n",
    "\n",
    "avg_rating_by_driver: the rider’s average rating over all of their trips\n",
    "\n",
    "avg_rating_of_driver: the rider’s average rating of their drivers over all of their trips\n",
    "\n",
    "surge_pct: the percent of trips taken with surge multiplier > 1\n",
    "\n",
    "avg_surge: The average surge multiplier over all of this user’s trips\n",
    "\n",
    "trips_in_first_30_days: the number of trips this user took in the first 30 days after signing up\n",
    "\n",
    "ultimate_black_user: TRUE if the user took an Ultimate Black in their first 30 days;\n",
    "\n",
    "FALSE otherwise\n",
    "\n",
    "weekday_pct: the percent of the user’s trips occurring during a weekday\n",
    "\n",
    "\n",
    "\n",
    "Please include any code you wrote for the analysis.\n",
    "\n",
    "1. Perform any cleaning, exploratory analysis, and/or visualizations to use the provided\n",
    "data for this analysis.\n",
    "2. Build a predictive model to help the company determine whether or not a user will be active\n",
    "on the system. Discuss why you chose your approach, what\n",
    "alternatives you considered, and any concerns you have. How valid is your model?\n",
    "Include any key indicators of model performance.\n",
    "\n",
    "\n",
    "**If you use Chatgpt in any of the sections, please state clearly how you reference Chatgpt, with a proper declaration, we might take you points off if we find duplicate codes produced from Chatgpt**\n",
    "\n",
    "Grade break down: 60% on data analysis in the notebook (completeness, accuracy and interpretation) + 40% on test accuracy\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>trips_in_first_30_days</th>\n",
       "      <th>signup_date</th>\n",
       "      <th>avg_rating_of_driver</th>\n",
       "      <th>avg_surge</th>\n",
       "      <th>last_trip_date</th>\n",
       "      <th>phone</th>\n",
       "      <th>surge_pct</th>\n",
       "      <th>ultimate_black_user</th>\n",
       "      <th>weekday_pct</th>\n",
       "      <th>avg_dist</th>\n",
       "      <th>avg_rating_by_driver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Winterfell</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-27</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2014-06-22</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>33.3</td>\n",
       "      <td>True</td>\n",
       "      <td>16.7</td>\n",
       "      <td>2.10</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>King's Landing</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2014-06-08</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.30</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>King's Landing</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-01-23</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2014-06-27</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>100.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>King's Landing</td>\n",
       "      <td>0</td>\n",
       "      <td>2014-01-17</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2014-05-31</td>\n",
       "      <td>Android</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>33.3</td>\n",
       "      <td>6.73</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>King's Landing</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-28</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.16</td>\n",
       "      <td>2014-06-20</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>26.9</td>\n",
       "      <td>True</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2.48</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             city  trips_in_first_30_days signup_date  avg_rating_of_driver  \\\n",
       "0      Winterfell                       1  2014-01-27                   3.5   \n",
       "1  King's Landing                       1  2014-01-11                   5.0   \n",
       "2  King's Landing                       0  2014-01-23                   3.0   \n",
       "3  King's Landing                       0  2014-01-17                   4.0   \n",
       "4  King's Landing                       1  2014-01-28                   5.0   \n",
       "\n",
       "   avg_surge last_trip_date    phone  surge_pct  ultimate_black_user  \\\n",
       "0       1.67     2014-06-22   iPhone       33.3                 True   \n",
       "1       1.00     2014-06-08   iPhone        0.0                False   \n",
       "2       1.00     2014-06-27   iPhone        0.0                 True   \n",
       "3       1.00     2014-05-31  Android        0.0                False   \n",
       "4       1.16     2014-06-20   iPhone       26.9                 True   \n",
       "\n",
       "   weekday_pct  avg_dist  avg_rating_by_driver  \n",
       "0         16.7      2.10                   4.8  \n",
       "1          0.0      7.30                   5.0  \n",
       "2        100.0      4.00                   5.0  \n",
       "3         33.3      6.73                   5.0  \n",
       "4         50.0      2.48                   4.8  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#set up environment\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import datetime\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#load data and visualize\n",
    "with open('data_challenge.json') as f:\n",
    "    data = json.load(f)\n",
    "    \n",
    "df = pd.DataFrame(data)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 47500 entries, 0 to 47499\n",
      "Data columns (total 12 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   city                    47500 non-null  object \n",
      " 1   trips_in_first_30_days  47500 non-null  int64  \n",
      " 2   signup_date             47500 non-null  object \n",
      " 3   avg_rating_of_driver    39746 non-null  float64\n",
      " 4   avg_surge               47500 non-null  float64\n",
      " 5   last_trip_date          47500 non-null  object \n",
      " 6   phone                   47128 non-null  object \n",
      " 7   surge_pct               47500 non-null  float64\n",
      " 8   ultimate_black_user     47500 non-null  bool   \n",
      " 9   weekday_pct             47500 non-null  float64\n",
      " 10  avg_dist                47500 non-null  float64\n",
      " 11  avg_rating_by_driver    47311 non-null  float64\n",
      "dtypes: bool(1), float64(6), int64(1), object(4)\n",
      "memory usage: 4.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\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>trips_in_first_30_days</th>\n",
       "      <th>avg_rating_of_driver</th>\n",
       "      <th>avg_surge</th>\n",
       "      <th>surge_pct</th>\n",
       "      <th>weekday_pct</th>\n",
       "      <th>avg_dist</th>\n",
       "      <th>avg_rating_by_driver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>47500.000000</td>\n",
       "      <td>39746.000000</td>\n",
       "      <td>47500.000000</td>\n",
       "      <td>47500.000000</td>\n",
       "      <td>47500.000000</td>\n",
       "      <td>47500.000000</td>\n",
       "      <td>47311.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.275158</td>\n",
       "      <td>4.602186</td>\n",
       "      <td>1.074716</td>\n",
       "      <td>8.852829</td>\n",
       "      <td>60.966623</td>\n",
       "      <td>5.799024</td>\n",
       "      <td>4.778400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.789220</td>\n",
       "      <td>0.617603</td>\n",
       "      <td>0.222291</td>\n",
       "      <td>19.993570</td>\n",
       "      <td>37.064133</td>\n",
       "      <td>5.677958</td>\n",
       "      <td>0.447863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.300000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>4.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>66.700000</td>\n",
       "      <td>3.890000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>125.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>129.890000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       trips_in_first_30_days  avg_rating_of_driver     avg_surge  \\\n",
       "count            47500.000000          39746.000000  47500.000000   \n",
       "mean                 2.275158              4.602186      1.074716   \n",
       "std                  3.789220              0.617603      0.222291   \n",
       "min                  0.000000              1.000000      1.000000   \n",
       "25%                  0.000000              4.300000      1.000000   \n",
       "50%                  1.000000              4.900000      1.000000   \n",
       "75%                  3.000000              5.000000      1.050000   \n",
       "max                125.000000              5.000000      8.000000   \n",
       "\n",
       "          surge_pct   weekday_pct      avg_dist  avg_rating_by_driver  \n",
       "count  47500.000000  47500.000000  47500.000000          47311.000000  \n",
       "mean       8.852829     60.966623      5.799024              4.778400  \n",
       "std       19.993570     37.064133      5.677958              0.447863  \n",
       "min        0.000000      0.000000      0.000000              1.000000  \n",
       "25%        0.000000     33.300000      2.420000              4.700000  \n",
       "50%        0.000000     66.700000      3.890000              5.000000  \n",
       "75%        8.500000    100.000000      6.950000              5.000000  \n",
       "max      100.000000    100.000000    129.890000              5.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city                         0\n",
       "trips_in_first_30_days       0\n",
       "signup_date                  0\n",
       "avg_rating_of_driver      7754\n",
       "avg_surge                    0\n",
       "last_trip_date               0\n",
       "phone                      372\n",
       "surge_pct                    0\n",
       "ultimate_black_user          0\n",
       "weekday_pct                  0\n",
       "avg_dist                     0\n",
       "avg_rating_by_driver       189\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  View null values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. EDA\n",
    "a. First of all, all the users will need to be labled by active or inactive, and categorical variables must be encoded. The null values must be accounted for. While the reason for the missing ratings cannot be inferred, it seems reasonable that the user/driver simply declined to input a rating. In this case, we will fill with the **mean value** so as to not affect the current distribution, which is primarily high ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values in avg_rating_by_driver and avg_rating_of_driver\n",
    "\n",
    "avg_rating_by_driver_mean = df['avg_rating_by_driver'].mean()\n",
    "avg_rating_of_driver_mean = df['avg_rating_of_driver'].mean()\n",
    "df['avg_rating_by_driver'].fillna(avg_rating_by_driver_mean, inplace=True)\n",
    "df['avg_rating_of_driver'].fillna(avg_rating_of_driver_mean, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city                        0\n",
       "trips_in_first_30_days      0\n",
       "signup_date                 0\n",
       "avg_rating_of_driver        0\n",
       "avg_surge                   0\n",
       "last_trip_date              0\n",
       "phone                     372\n",
       "surge_pct                   0\n",
       "ultimate_black_user         0\n",
       "weekday_pct                 0\n",
       "avg_dist                    0\n",
       "avg_rating_by_driver        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  View null values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# There aren't many missing values for \"phone,\" so just drop them directly.\n",
    "df.dropna(axis=0, how='any', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city                      0\n",
       "trips_in_first_30_days    0\n",
       "signup_date               0\n",
       "avg_rating_of_driver      0\n",
       "avg_surge                 0\n",
       "last_trip_date            0\n",
       "phone                     0\n",
       "surge_pct                 0\n",
       "ultimate_black_user       0\n",
       "weekday_pct               0\n",
       "avg_dist                  0\n",
       "avg_rating_by_driver      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  View null values again\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b. The target variable will be whether the user was active. Convert the signup date and last trip date to datetime objects. If the last trip is in June, then encode each user active as True, else False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set as datatime objects\n",
    "# Convert the date format to datetime\n",
    "df['signup_date'] = pd.to_datetime(df['signup_date'])\n",
    "df['last_trip_date'] = pd.to_datetime(df['last_trip_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  last_trip_date  active\n",
      "0     2014-06-22    True\n",
      "1     2014-06-08    True\n",
      "2     2014-06-27    True\n",
      "3     2014-05-31   False\n",
      "4     2014-06-20    True\n",
      "5     2014-01-20   False\n",
      "6     2014-05-17   False\n",
      "7     2014-02-01   False\n",
      "8     2014-06-01    True\n",
      "9     2014-05-11   False\n"
     ]
    }
   ],
   "source": [
    "# Encode target variable 'active' as true or false for last trip in june\n",
    "# you can add more lines before assigning the 'active' column\n",
    "# Encode the target variable 'active' as a boolean type, representing whether the user took a ride in the last June.\n",
    "june_start = pd.to_datetime('2014-06-01')\n",
    "june_end = pd.to_datetime('2014-07-01')\n",
    "# If 'last_trip_date' greater than or equal to June 1st, 2014, and less than or equal to June 30th, 2014, then label as True; otherwise, label as False.\n",
    "df['active'] = (df['last_trip_date'] >= june_start) & (df['last_trip_date'] <= june_end)\n",
    "print(df[['last_trip_date', 'active']].head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the ratio of active users to inactive users?\n",
    "Calculate the percentage of active users among all users and visualize the results with a chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate ratio of active to all users\n",
    "# Visualization\n",
    "# Calculate the number of active users and non-active users.\n",
    "active_users = df[df['active'] == True]['active'].count()\n",
    "inactive_users = df[df['active'] == False]['active'].count()\n",
    "\n",
    "# Calculate the ratio of active users to inactive users.\n",
    "total_users = df.shape[0]\n",
    "active_ratio = active_users / total_users\n",
    "inactive_ratio = inactive_users / total_users\n",
    "\n",
    "# Visualize the proportion of active users and inactive users.\n",
    "labels = ['Active Users', 'Inactive Users']\n",
    "sizes = [active_ratio, inactive_ratio]\n",
    "colors = ['#66b3ff', '#ff9999']\n",
    "explode = (0.1, 0)  \n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "plt.axis('equal')  \n",
    "plt.title('Ratio of Active to All Users')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unsurprisingly the classes are imbalanced, but not tremendously. This should not present too much of a problem while modeling.\n",
    "\n",
    "c. In the cell below, show a visualization that you used to select features for your model.\n",
    "\n",
    "Include:\n",
    "\n",
    "1. At least one plot showing something meaningful about the data that helped you during feature selection, model selection, or both.\n",
    "\n",
    "2. Two or three sentences describing what you plotted and its implications with respect to your features.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, delete features that are not present in the test set\n",
    "df.drop(['signup_date','last_trip_date'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate discrete and continuous features\n",
    "discrete_features=['city', 'trips_in_first_30_days','phone','ultimate_black_user']\n",
    "continuous_features=['avg_rating_of_driver', 'avg_surge','surge_pct','weekday_pct','avg_dist','avg_rating_by_driver']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot statistics for each value of discrete features\n",
    "plt.figure(figsize=(16, 8))\n",
    "i = 1\n",
    "for feature in discrete_features:\n",
    "    if df[feature].nunique()<100:\n",
    "        plt.subplot(2, 4, i)\n",
    "        i += 1\n",
    "        v = df[feature].value_counts()\n",
    "        fig = sns.barplot(x=v.index, y=v.values)\n",
    "        for item in fig.get_xticklabels():\n",
    "            item.set_rotation(90)\n",
    "        plt.title(feature)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x3000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot density estimation graph for continuous variables\n",
    "dist_cols=3\n",
    "dist_rows=len(continuous_features)\n",
    "plt.figure(figsize=(5*dist_cols,5*dist_rows))\n",
    "i=1\n",
    "for feature in continuous_features:\n",
    "    ax=plt.subplot(dist_rows,dist_cols,i)\n",
    "    ax=sns.kdeplot(df[feature],color='Red',fill=True)\n",
    "    ax.set_xlabel(feature)\n",
    "    ax.set_ylabel('Frequency')\n",
    "    i+=1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the covariance and correlation of each variable\n",
    "df_corr=df.corr()\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(abs(df_corr),vmax=0.8,linewidths=0.05,cmap=sns.cm.rocket_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the analysis, it can be concluded that 'trips_in_first_30_days' and 'ultimate_black_user' features have the strongest correlation with activation, while 'avg_surge' has the weakest correlation and is also densely distributed. Therefore, we will remove 'avg_dist' from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['avg_surge'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['trips_in_first_30_days', 'avg_rating_of_driver', 'surge_pct',\n",
       "       'ultimate_black_user', 'weekday_pct', 'avg_dist',\n",
       "       'avg_rating_by_driver', 'city_Astapor', 'city_King's Landing',\n",
       "       'city_Winterfell', 'phone_Android', 'phone_iPhone'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the input and output, and perform one-hot encoding on 'city' and 'phone'\n",
    "X = pd.get_dummies(df.drop(['active'], axis=1))\n",
    "y = df['active']\n",
    "X.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Modeling\n",
    "\n",
    "Now, please perform feature engineering to build a model that can predict whether the users are active. (To get full credit, you need to make some visualization and try out several different models using cross validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier Cross-Validation Accuracy: 0.7769\n",
      "LogisticRegression Cross-Validation Accuracy: 0.7170\n",
      "AdaBoostClassifier Cross-Validation Accuracy: 0.7813\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "# Normalize all features\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "seed = 0\n",
    "models = [RandomForestClassifier(n_estimators=50,\n",
    "                                 max_depth=10,\n",
    "                                 oob_score=True,\n",
    "                                 class_weight=\"balanced\", \n",
    "                                 random_state=seed),\n",
    "          LogisticRegression(penalty = \"l2\",\n",
    "                             C = 1,\n",
    "                             fit_intercept=True,\n",
    "                            #  class_weight=\"balanced\",\n",
    "                             solver = 'newton-cholesky',\n",
    "                             random_state=seed), \n",
    "          AdaBoostClassifier(n_estimators=100,\n",
    "                             learning_rate=0.85,\n",
    "                             algorithm=\"SAMME.R\",\n",
    "                             random_state=seed)]\n",
    "scores_list = []\n",
    "\n",
    "for model in models:\n",
    "    scores = cross_val_score(model, X_scaled, y, cv=5, scoring='accuracy')\n",
    "    scores_list.append(scores)\n",
    "    print(f\"{model.__class__.__name__} Cross-Validation Accuracy: {scores.mean():.4f}\")\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "models_names = ['RandomForest', 'LogisticRegression', 'AdaBoost']\n",
    "scores_means = [scores.mean() for scores in scores_list]\n",
    "sns.barplot(x=models_names, y=scores_means)\n",
    "plt.xlabel('Model')\n",
    "plt.ylabel('Cross-Validation Accuracy')\n",
    "plt.title('Cross-Validation Accuracy Comparison for Different Models')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylim(0.7, 0.9)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Model Evaluation\n",
    "\n",
    "The bottom line here is: Was the model useful? You need to check both **numerical metrics and visual metrics**. **Please explain different performance metrics and your results.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoost cross-validation accuracy： 0.7813\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "# Build an AdaBoostClassifier and perform cross-validation\n",
    "adaboost_model = AdaBoostClassifier(n_estimators=100,\n",
    "                             learning_rate=0.85,\n",
    "                             algorithm=\"SAMME.R\",\n",
    "                             random_state=seed)\n",
    "adaboost_scores = cross_val_score(adaboost_model, X_scaled, y, cv=5, scoring='accuracy')\n",
    "\n",
    "# Output cross-validation accuracy.\n",
    "print(f\"AdaBoost cross-validation accuracy： {adaboost_scores.mean():.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train the model using the entire dataset and make predictions.\n",
    "adaboost_model.fit(X_scaled, y)\n",
    "y_pred = adaboost_model.predict(X_scaled)\n",
    "# Calculate the confusion matrix.\n",
    "conf_matrix = confusion_matrix(y, y_pred)\n",
    "# Visualize the confusion matrix\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('True Labels')\n",
    "plt.title('Confusion Matrix for AdaBoost Model')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy is an evaluation metric for classification models, reflecting the percentage of correct predictions out of all predictions made.\n",
    "\n",
    "Precision is another metric, spotlighting the proportion of accurately predicted positive samples among all samples predicted as positive. It underlines the veracity of positive predictions.\n",
    "\n",
    "Recall quantifies a classifier's competence in accurately identifying positive samples. It emphasizes the number of positive samples correctly predicted out of the total true positive samples.\n",
    "\n",
    "Given their respective areas of focus, precision and recall are frequently employed evaluation metrics in classification problems. As they concurrently examine the precision of predictions and the capacity to correctly identify positive samples, it's important to consider both metrics for a well-rounded evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       False       0.80      0.86      0.83     29404\n",
      "        True       0.74      0.65      0.69     17724\n",
      "\n",
      "    accuracy                           0.78     47128\n",
      "   macro avg       0.77      0.76      0.76     47128\n",
      "weighted avg       0.78      0.78      0.78     47128\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Output classification report.\n",
    "class_report = classification_report(y, y_pred)\n",
    "print(\"Classification Report:\\n\", class_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " # Get feature importance and sort it.\n",
    "feature_importance = adaboost_model.feature_importances_\n",
    "sorted_indices = np.argsort(feature_importance)[::-1]\n",
    "\n",
    "# Visualize based on the sorted feature importance.\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=feature_importance[sorted_indices], y=X.columns[sorted_indices])\n",
    "plt.xlabel('Feature Importance')\n",
    "plt.ylabel('Feature Name')\n",
    "plt.title('Feature Importance Sorted Chart')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Feature importance\" denotes a measurement utilized in machine learning models to gauge the influence of input features on the predictive results of the model. This metric assists in determining which features substantially impact the model's decision-making process. As indicated by the graph, weekday_pct emerges as the most significant feature, corroborating our intuitive supposition."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Final Submission\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The following code will write your predictions on the test dataset to a CSV file. **You will need to submit this file to get credit for this question.**\n",
    "\n",
    "Save your predictions in a 1-dimensional array called `test_predictions` (contains 0 and 1s). \n",
    "\n",
    "Remember that if you've performed transformations or featurization on the training data, you must also perform the same transformations on the test data in order to make predictions. \n",
    "\n",
    "\n",
    "\n",
    "### Submission on Gitea\n",
    "\n",
    "Please push all your work (ipynb, submission.csv) to the `-final` repo on Gitea. All your work should be on the master branch. \n",
    "\n",
    "### Submission on Canvas\n",
    "\n",
    "Just in case you have issues with Git, submit the same items on Canvas.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load and preprocess test data\n",
    "test = pd.read_csv('data/test.csv')\n",
    "# Fill missing values in avg_rating_by_driver and avg_rating_of_driver\n",
    "test['avg_rating_by_driver'].fillna(test['avg_rating_by_driver'].mean(), inplace=True)\n",
    "test['avg_rating_of_driver'].fillna(test['avg_rating_of_driver'].mean(), inplace=True)\n",
    "test_encoded = pd.get_dummies(test)\n",
    "test_encoded = test_encoded[X.columns]\n",
    "test_scaled = scaler.transform(test_encoded)\n",
    "# Make predictions on the test data\n",
    "test_predictions = adaboost_model.predict(test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created a CSV file: submission.csv\n",
      "You may now upload this CSV file to Gitea for grading.\n"
     ]
    }
   ],
   "source": [
    "submission_df = pd.DataFrame({ \n",
    "    \"active\": test_predictions\n",
    "}, columns=['active'])\n",
    "submission_df.to_csv(\"submission.csv\", index=False)\n",
    "\n",
    "print('Created a CSV file: submission.csv')\n",
    "print('You may now upload this CSV file to Gitea for grading.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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