Capstone-Escooter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee0c88a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tab\\AppData\\Local\\Temp\\ipykernel_27652\\2600087072.py:16: DeprecationWarning: `import kerastuner` is deprecated, please use `import keras_tuner`.\n",
      "  from kerastuner import HyperModel, RandomSearch\n"
     ]
    }
   ],
   "source": [
    "#!pip install keras-tuner\n",
    "#!pip install --upgrade tensorflow\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import TimeSeriesSplit, GridSearchCV\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense\n",
    "import numpy as np\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense\n",
    "from kerastuner import HyperModel, RandomSearch\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from tensorflow.keras.models import load_model\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "256ac5c6",
   "metadata": {},
   "source": [
    "## Read trips dataset and external data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "494efc44",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace this with user folder path to datasets\n",
    "folder_path = '/Users/tab/Documents/R/MScA/2024/Capstone Project'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d28c46c7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in Chicago_Weather.csv: ['name', 'datetime', 'tempmax', 'tempmin', 'temp', 'feelslikemax', 'feelslikemin', 'feelslike', 'dew', 'humidity', 'precip', 'precipprob', 'precipcover', 'preciptype', 'snow', 'snowdepth', 'windgust', 'windspeed', 'winddir', 'sealevelpressure', 'cloudcover', 'visibility', 'solarradiation', 'solarenergy', 'uvindex', 'severerisk', 'sunrise', 'sunset', 'moonphase', 'conditions', 'description', 'icon', 'stations']\n",
      "Columns in Weekly_Gas_Prices_Abridged.csv: ['Date', 'Weekly Chicago, IL Regular Reformulated Retail Gasoline Prices  (Dollars per Gallon)']\n",
      "Columns in Weekly_Gas_Prices.csv: ['Date', 'Weekly Chicago, IL Regular Reformulated Retail Gasoline Prices  (Dollars per Gallon)']\n",
      "Columns in E_Scooter_Trips.csv: ['Trip ID', 'Start Time', 'End Time', 'Trip Distance', 'Trip Duration', 'Vendor', 'Start Community Area Number', 'End Community Area Number', 'Start Community Area Name', 'End Community Area Name', 'Start Centroid Latitude', 'Start Centroid Longitude', 'Start Centroid Location', 'End Centroid Latitude', 'End Centroid Longitude', 'End Centroid Location']\n",
      "Columns in Chicago_Crimes.csv: ['ID', 'Case Number', 'Date', 'Block', 'IUCR', 'Primary Type', 'Description', 'Location Description', 'Arrest', 'Domestic', 'Beat', 'District', 'Ward', 'Community Area', 'FBI Code', 'X Coordinate', 'Y Coordinate', 'Year', 'Updated On', 'Latitude', 'Longitude', 'Location']\n",
      "File format not supported for /Users/tab/Documents/R/MScA/2024/Capstone Project\\Race_and_Ethnicity\n",
      "Columns in Citizenship.csv: ['community_area_number', 'community_name', 'total_population', 'us_citizen', 'foreign_born', 'naturalized_citizen', 'no_citizen', 'us_citizen_percent', 'foreign_born_percent', 'naturalized_citizen_percent', 'no_citizen_percent']\n",
      "Columns in Education.csv: ['community_area_number', 'community_name', 'total_population', 'not_hs_grad', 'hs_grad', 'some_college', 'has_bachelors', 'not_hs_grad_percent', 'hs_grad_percent', 'some_college_percent', 'has_bachelors_percent', 'hs_grad_or_higher_percent']\n",
      "Columns in Housing.csv: ['community_area_number', 'community_name', 'total_population', 'owner_occupied', 'renter_occupied', 'owner_percent', 'renter_percent']\n",
      "Columns in Poverty.csv: ['community_area_number', 'community_name', 'total_population', 'below_poverty', 'below_poverty_percent']\n",
      "Columns in Health_Insurance_Coverage.csv: ['community_area_number', 'community_name', 'total_population', 'insured', 'insured_percent']\n",
      "Columns in Income.csv: ['community_area_number', 'community_name', 'total_population', 'median_household_income', 'low_income', 'middle_income', 'high_income', 'below_10_income', '10_to_15_income', '15_to_20_income', '20_to_25_income', '25_to_30_income', '30_to_35_income', '35_to_40_income', '40_to_45_income', '45_to_50_income', '50_to_60_income', '60_to_75_income', '75_to_100_income', '100_to_125_income', '125_to_150_income', '150_to_200_income', 'above_200_income', 'low_income_percent', 'middle_income_percent', 'high_income_percent', 'below_10_income_percent', '10_to_15_income_percent', '15_to_20_income_percent', '20_to_25_income_percent', '25_to_30_income_percent', '30_to_35_income_percent', '35_to_40_income_percent', '40_to_45_income_percent', '45_to_50_income_percent', '50_to_60_income_percent', '60_to_75_income_percent', '75_to_100_income_percent', '100_to_125_income_percent', '125_to_150_income_percent', '150_to_200_income_percent', 'above_200_income_percent']\n"
     ]
    }
   ],
   "source": [
    "# List of files\n",
    "files = [\n",
    "    'Chicago_Weather.csv',\n",
    "    'Weekly_Gas_Prices_Abridged.csv',\n",
    "    'Weekly_Gas_Prices.csv',\n",
    "    'E_Scooter_Trips.csv',\n",
    "    'Chicago_Crimes.csv',\n",
    "    'Race_and_Ethnicity',\n",
    "    'Citizenship.csv',\n",
    "    'Education.csv',\n",
    "    'Housing.csv',\n",
    "    'Poverty.csv',\n",
    "    'Health_Insurance_Coverage.csv',\n",
    "    'Income.csv'\n",
    "]\n",
    "\n",
    "# Function to read a file and print its columns\n",
    "def print_column_names(file_path):\n",
    "    if file_path.endswith('.csv'):\n",
    "        df = pd.read_csv(file_path)\n",
    "    elif file_path.endswith('.xlsx'):\n",
    "        df = pd.read_excel(file_path)\n",
    "    else:\n",
    "        print(f\"File format not supported for {file_path}\")\n",
    "        return\n",
    "    print(f\"Columns in {os.path.basename(file_path)}: {df.columns.tolist()}\")\n",
    "\n",
    "# Iterate over the files, read each one, and print column names\n",
    "for file in files:\n",
    "    file_path = os.path.join(folder_path, file)\n",
    "    print_column_names(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "897275db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trips Dataset: (3797237, 16)\n",
      "Weather Data: (514, 33)\n",
      "Crime Data: {df_crimes.shape}\n",
      "Gas Data: {df_gas.shape}\n",
      "Race Data: {df_race.shape}\n",
      "Income Data: {df_income.shape}\n"
     ]
    }
   ],
   "source": [
    "# Trips data\n",
    "df_e_scooter_trips = pd.read_csv('E_Scooter_Trips.csv')\n",
    "\n",
    "# Weather data\n",
    "df_weather = pd.read_csv('Chicago_Weather.csv')\n",
    "\n",
    "# Crime data\n",
    "df_crimes = pd.read_csv('Chicago_Crimes.csv')\n",
    "\n",
    "# Gas data\n",
    "df_gas = pd.read_csv('Weekly_Gas_Prices_Abridged.csv')\n",
    "\n",
    "# Race and Ethnicity\n",
    "df_race = pd.read_csv('Race_and_Ethnicity.csv')\n",
    "\n",
    "# Citizenship\n",
    "df_citizenship = pd.read_csv('Citizenship.csv')\n",
    "\n",
    "# Education\n",
    "df_education = pd.read_csv('Education.csv')\n",
    "\n",
    "# Housing\n",
    "df_housing = pd.read_csv('Housing.csv')\n",
    "\n",
    "# Poverty\n",
    "df_poverty = pd.read_csv('Poverty.csv')\n",
    "\n",
    "# Health Insurance Coverage\n",
    "df_insurance = pd.read_csv('Health_Insurance_Coverage.csv')\n",
    "\n",
    "# Income\n",
    "df_income = pd.read_csv('Income.csv')\n",
    "\n",
    "print(f'Trips Dataset: {df_e_scooter_trips.shape}\\nWeather Data: {df_weather.shape}' +\n",
    "'\\nCrime Data: {df_crimes.shape}\\nGas Data: {df_gas.shape}' +\n",
    "'\\nRace Data: {df_race.shape}\\nIncome Data: {df_income.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e1ed40a5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Function to quickly inspect the datasets\n",
    "def inspect_data(df, name):\n",
    "    print(f\"--- Inspecting {name} ---\")\n",
    "    print(\"First few rows:\")\n",
    "    print(df.head())\n",
    "    print(\"\\nSummary Statistics:\")\n",
    "#     print(df.describe())\n",
    "    print(\"\\nMissing Values:\")\n",
    "    print(df.isnull().sum())\n",
    "    print(df.info())\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d19a1832",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Inspecting Weather Data ---\n",
      "First few rows:\n",
      "                    name    datetime  tempmax  tempmin  temp  feelslikemax  \\\n",
      "0  Chicago,United States  2022-05-05     52.9     44.3  49.7          52.9   \n",
      "1  Chicago,United States  2022-05-06     52.7     49.7  51.1          52.7   \n",
      "2  Chicago,United States  2022-05-07     64.5     47.6  55.9          64.5   \n",
      "3  Chicago,United States  2022-05-08     66.9     45.7  58.3          66.9   \n",
      "4  Chicago,United States  2022-05-09     83.5     57.1  70.8          83.3   \n",
      "\n",
      "   feelslikemin  feelslike   dew  humidity  ...  solarenergy  uvindex  \\\n",
      "0          40.5       48.8  42.7      76.8  ...          3.5        1   \n",
      "1          45.1       50.9  45.2      81.4  ...          2.1        1   \n",
      "2          43.9       55.5  31.4      40.6  ...         19.0        9   \n",
      "3          43.8       57.8  38.8      48.8  ...          8.5        4   \n",
      "4          57.1       70.8  50.8      49.6  ...         13.9        7   \n",
      "\n",
      "   severerisk              sunrise               sunset  moonphase  \\\n",
      "0          10  2022-05-05T05:41:32  2022-05-05T19:53:38       0.16   \n",
      "1          10  2022-05-06T05:40:18  2022-05-06T19:54:43       0.19   \n",
      "2          10  2022-05-07T05:39:06  2022-05-07T19:55:47       0.22   \n",
      "3          10  2022-05-08T05:37:55  2022-05-08T19:56:51       0.25   \n",
      "4          10  2022-05-09T05:36:45  2022-05-09T19:57:54       0.29   \n",
      "\n",
      "         conditions                                        description  \\\n",
      "0    Rain, Overcast         Cloudy skies throughout the day with rain.   \n",
      "1    Rain, Overcast  Cloudy skies throughout the day with a chance ...   \n",
      "2             Clear               Clear conditions throughout the day.   \n",
      "3  Partially cloudy                  Partly cloudy throughout the day.   \n",
      "4  Partially cloudy                  Partly cloudy throughout the day.   \n",
      "\n",
      "                icon                                           stations  \n",
      "0               rain  72534014819,AP878,KORD,KMDW,72530094846,F1983,...  \n",
      "1               rain  72534014819,AP878,KORD,KMDW,72530094846,F1983,...  \n",
      "2          clear-day  72534014819,KORD,KMDW,72530094846,F1983,744665...  \n",
      "3  partly-cloudy-day  72534014819,C8740,KORD,KMDW,72530094846,F1983,...  \n",
      "4  partly-cloudy-day  72534014819,KORD,KMDW,72530094846,F1983,744665...  \n",
      "\n",
      "[5 rows x 33 columns]\n",
      "\n",
      "Summary Statistics:\n",
      "\n",
      "Missing Values:\n",
      "name                  0\n",
      "datetime              0\n",
      "tempmax               0\n",
      "tempmin               0\n",
      "temp                  0\n",
      "feelslikemax          0\n",
      "feelslikemin          0\n",
      "feelslike             0\n",
      "dew                   0\n",
      "humidity              0\n",
      "precip                0\n",
      "precipprob            0\n",
      "precipcover           0\n",
      "preciptype          253\n",
      "snow                  0\n",
      "snowdepth             0\n",
      "windgust              0\n",
      "windspeed             0\n",
      "winddir               0\n",
      "sealevelpressure      0\n",
      "cloudcover            0\n",
      "visibility            0\n",
      "solarradiation        0\n",
      "solarenergy           0\n",
      "uvindex               0\n",
      "severerisk            0\n",
      "sunrise               0\n",
      "sunset                0\n",
      "moonphase             0\n",
      "conditions            0\n",
      "description           0\n",
      "icon                  0\n",
      "stations              0\n",
      "dtype: int64\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 514 entries, 0 to 513\n",
      "Data columns (total 33 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   name              514 non-null    object \n",
      " 1   datetime          514 non-null    object \n",
      " 2   tempmax           514 non-null    float64\n",
      " 3   tempmin           514 non-null    float64\n",
      " 4   temp              514 non-null    float64\n",
      " 5   feelslikemax      514 non-null    float64\n",
      " 6   feelslikemin      514 non-null    float64\n",
      " 7   feelslike         514 non-null    float64\n",
      " 8   dew               514 non-null    float64\n",
      " 9   humidity          514 non-null    float64\n",
      " 10  precip            514 non-null    float64\n",
      " 11  precipprob        514 non-null    int64  \n",
      " 12  precipcover       514 non-null    float64\n",
      " 13  preciptype        261 non-null    object \n",
      " 14  snow              514 non-null    float64\n",
      " 15  snowdepth         514 non-null    float64\n",
      " 16  windgust          514 non-null    float64\n",
      " 17  windspeed         514 non-null    float64\n",
      " 18  winddir           514 non-null    float64\n",
      " 19  sealevelpressure  514 non-null    float64\n",
      " 20  cloudcover        514 non-null    float64\n",
      " 21  visibility        514 non-null    float64\n",
      " 22  solarradiation    514 non-null    float64\n",
      " 23  solarenergy       514 non-null    float64\n",
      " 24  uvindex           514 non-null    int64  \n",
      " 25  severerisk        514 non-null    int64  \n",
      " 26  sunrise           514 non-null    object \n",
      " 27  sunset            514 non-null    object \n",
      " 28  moonphase         514 non-null    float64\n",
      " 29  conditions        514 non-null    object \n",
      " 30  description       514 non-null    object \n",
      " 31  icon              514 non-null    object \n",
      " 32  stations          514 non-null    object \n",
      "dtypes: float64(21), int64(3), object(9)\n",
      "memory usage: 132.6+ KB\n",
      "None\n",
      "\n",
      "\n",
      "--- Inspecting Gas Prices Data ---\n",
      "First few rows:\n",
      "        Date  \\\n",
      "0   5/2/2022   \n",
      "1   5/9/2022   \n",
      "2  5/16/2022   \n",
      "3  5/23/2022   \n",
      "4  5/30/2022   \n",
      "\n",
      "   Weekly Chicago, IL Regular Reformulated Retail Gasoline Prices  (Dollars per Gallon)  \n",
      "0                                              4.467                                     \n",
      "1                                              4.672                                     \n",
      "2                                              4.929                                     \n",
      "3                                              5.133                                     \n",
      "4                                              5.264                                     \n",
      "\n",
      "Summary Statistics:\n",
      "\n",
      "Missing Values:\n",
      "Date                                                                                    0\n",
      "Weekly Chicago, IL Regular Reformulated Retail Gasoline Prices  (Dollars per Gallon)    0\n",
      "dtype: int64\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 75 entries, 0 to 74\n",
      "Data columns (total 2 columns):\n",
      " #   Column                                                                                Non-Null Count  Dtype  \n",
      "---  ------                                                                                --------------  -----  \n",
      " 0   Date                                                                                  75 non-null     object \n",
      " 1   Weekly Chicago, IL Regular Reformulated Retail Gasoline Prices  (Dollars per Gallon)  75 non-null     float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 1.3+ KB\n",
      "None\n",
      "\n",
      "\n",
      "--- Inspecting E-Scooter Trips Data ---\n",
      "First few rows:\n",
      "                                Trip ID              Start Time  \\\n",
      "0  17553846-c8d6-b9de-1755-3846c8d6b9de  05/28/2022 02:00:00 PM   \n",
      "1  1756d4dc-ccf1-af42-1756-d4dcccf1af42  05/29/2022 07:00:00 PM   \n",
      "2  1756d578-f376-9950-1756-d578f3769950  05/29/2022 08:00:00 PM   \n",
      "3  1756fc61-f058-c200-1756-fc61f058c200  05/29/2022 10:00:00 PM   \n",
      "4  17659074-0cfd-92ca-1765-90740cfd92ca  06/10/2022 12:00:00 AM   \n",
      "\n",
      "                 End Time  Trip Distance  Trip Duration Vendor  \\\n",
      "0  05/28/2022 03:00:00 PM           2484           1544   Lyft   \n",
      "1  05/29/2022 08:00:00 PM             36            135   Lyft   \n",
      "2  05/29/2022 08:00:00 PM           2653           1972   Lyft   \n",
      "3  05/29/2022 10:00:00 PM            105            137   Lyft   \n",
      "4  06/10/2022 12:00:00 AM           2299            540   Lyft   \n",
      "\n",
      "   Start Community Area Number  End Community Area Number  \\\n",
      "0                         32.0                       32.0   \n",
      "1                         32.0                       32.0   \n",
      "2                         32.0                        NaN   \n",
      "3                         32.0                        NaN   \n",
      "4                          8.0                       24.0   \n",
      "\n",
      "  Start Community Area Name End Community Area Name  Start Centroid Latitude  \\\n",
      "0                      Loop                    Loop                41.878866   \n",
      "1                      Loop                    Loop                41.878866   \n",
      "2                      Loop                     NaN                41.878866   \n",
      "3                      Loop                     NaN                41.878866   \n",
      "4           Near North Side               West Town                41.899602   \n",
      "\n",
      "   Start Centroid Longitude                        Start Centroid Location  \\\n",
      "0                -87.625192   POINT (-87.62519186960115 41.87886594636445)   \n",
      "1                -87.625192   POINT (-87.62519186960115 41.87886594636445)   \n",
      "2                -87.625192   POINT (-87.62519186960115 41.87886594636445)   \n",
      "3                -87.625192   POINT (-87.62519186960115 41.87886594636445)   \n",
      "4                -87.633309  POINT (-87.63330943703569 41.899602106426094)   \n",
      "\n",
      "   End Centroid Latitude  End Centroid Longitude  \\\n",
      "0              41.878866              -87.625192   \n",
      "1              41.878866              -87.625192   \n",
      "2                    NaN                     NaN   \n",
      "3                    NaN                     NaN   \n",
      "4              41.901207              -87.676357   \n",
      "\n",
      "                          End Centroid Location  \n",
      "0  POINT (-87.62519186960115 41.87886594636445)  \n",
      "1  POINT (-87.62519186960115 41.87886594636445)  \n",
      "2                                           NaN  \n",
      "3                                           NaN  \n",
      "4  POINT (-87.67635712603905 41.90120673431551)  \n",
      "\n",
      "Summary Statistics:\n",
      "\n",
      "Missing Values:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trip ID                           0\n",
      "Start Time                        0\n",
      "End Time                          0\n",
      "Trip Distance                     0\n",
      "Trip Duration                     0\n",
      "Vendor                            0\n",
      "Start Community Area Number    2827\n",
      "End Community Area Number      3941\n",
      "Start Community Area Name      2827\n",
      "End Community Area Name        3941\n",
      "Start Centroid Latitude        2827\n",
      "Start Centroid Longitude       2827\n",
      "Start Centroid Location        2827\n",
      "End Centroid Latitude          3941\n",
      "End Centroid Longitude         3941\n",
      "End Centroid Location          3941\n",
      "dtype: int64\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3797237 entries, 0 to 3797236\n",
      "Data columns (total 16 columns):\n",
      " #   Column                       Dtype  \n",
      "---  ------                       -----  \n",
      " 0   Trip ID                      object \n",
      " 1   Start Time                   object \n",
      " 2   End Time                     object \n",
      " 3   Trip Distance                int64  \n",
      " 4   Trip Duration                int64  \n",
      " 5   Vendor                       object \n",
      " 6   Start Community Area Number  float64\n",
      " 7   End Community Area Number    float64\n",
      " 8   Start Community Area Name    object \n",
      " 9   End Community Area Name      object \n",
      " 10  Start Centroid Latitude      float64\n",
      " 11  Start Centroid Longitude     float64\n",
      " 12  Start Centroid Location      object \n",
      " 13  End Centroid Latitude        float64\n",
      " 14  End Centroid Longitude       float64\n",
      " 15  End Centroid Location        object \n",
      "dtypes: float64(6), int64(2), object(8)\n",
      "memory usage: 463.5+ MB\n",
      "None\n",
      "\n",
      "\n",
      "--- Inspecting Crimes Data ---\n",
      "First few rows:\n",
      "         ID Case Number                    Date                      Block  \\\n",
      "0   5741943    HN549294  08/25/2007 09:22:18 AM         074XX N ROGERS AVE   \n",
      "1     25953    JE240540  05/24/2021 03:06:00 PM        020XX N LARAMIE AVE   \n",
      "2     26038    JE279849  06/26/2021 09:24:00 AM      062XX N MC CORMICK RD   \n",
      "3  13279676    JG507211  11/09/2023 07:30:00 AM           019XX W BYRON ST   \n",
      "4  13274752    JG501049  11/12/2023 07:59:00 AM  086XX S COTTAGE GROVE AVE   \n",
      "\n",
      "   IUCR Primary Type                                        Description  \\\n",
      "0  0560      ASSAULT                                             SIMPLE   \n",
      "1  0110     HOMICIDE                                FIRST DEGREE MURDER   \n",
      "2  0110     HOMICIDE                                FIRST DEGREE MURDER   \n",
      "3  0620     BURGLARY                                     UNLAWFUL ENTRY   \n",
      "4  0454      BATTERY  AGGRAVATED P.O. - HANDS, FISTS, FEET, NO / MIN...   \n",
      "\n",
      "  Location Description  Arrest  Domestic  ...  Ward  Community Area  FBI Code  \\\n",
      "0                OTHER   False     False  ...  49.0             1.0       08A   \n",
      "1               STREET    True     False  ...  36.0            19.0       01A   \n",
      "2          PARKING LOT    True     False  ...  50.0            13.0       01A   \n",
      "3            APARTMENT   False     False  ...  47.0             5.0        05   \n",
      "4   SMALL RETAIL STORE    True     False  ...   6.0            44.0       08B   \n",
      "\n",
      "   X Coordinate Y Coordinate  Year              Updated On   Latitude  \\\n",
      "0           NaN          NaN  2007  08/17/2015 03:03:40 PM        NaN   \n",
      "1     1141387.0    1913179.0  2021  11/18/2023 03:39:49 PM  41.917838   \n",
      "2     1152781.0    1941458.0  2021  11/18/2023 03:39:49 PM  41.995219   \n",
      "3     1162518.0    1925906.0  2023  11/18/2023 03:39:49 PM  41.952345   \n",
      "4     1183071.0    1847869.0  2023  12/09/2023 03:41:24 PM  41.737751   \n",
      "\n",
      "   Longitude                       Location  \n",
      "0        NaN                            NaN  \n",
      "1 -87.755969  (41.917838056, -87.755968972)  \n",
      "2 -87.713355  (41.995219444, -87.713354912)  \n",
      "3 -87.677975  (41.952345086, -87.677975059)  \n",
      "4 -87.604856  (41.737750767, -87.604855911)  \n",
      "\n",
      "[5 rows x 22 columns]\n",
      "\n",
      "Summary Statistics:\n",
      "\n",
      "Missing Values:\n",
      "ID                           0\n",
      "Case Number                  0\n",
      "Date                         0\n",
      "Block                        0\n",
      "IUCR                         0\n",
      "Primary Type                 0\n",
      "Description                  0\n",
      "Location Description     12891\n",
      "Arrest                       0\n",
      "Domestic                     0\n",
      "Beat                         0\n",
      "District                    47\n",
      "Ward                    614848\n",
      "Community Area          613472\n",
      "FBI Code                     0\n",
      "X Coordinate             88612\n",
      "Y Coordinate             88612\n",
      "Year                         0\n",
      "Updated On                   0\n",
      "Latitude                 88612\n",
      "Longitude                88612\n",
      "Location                 88612\n",
      "dtype: int64\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8041530 entries, 0 to 8041529\n",
      "Data columns (total 22 columns):\n",
      " #   Column                Dtype  \n",
      "---  ------                -----  \n",
      " 0   ID                    int64  \n",
      " 1   Case Number           object \n",
      " 2   Date                  object \n",
      " 3   Block                 object \n",
      " 4   IUCR                  object \n",
      " 5   Primary Type          object \n",
      " 6   Description           object \n",
      " 7   Location Description  object \n",
      " 8   Arrest                bool   \n",
      " 9   Domestic              bool   \n",
      " 10  Beat                  int64  \n",
      " 11  District              float64\n",
      " 12  Ward                  float64\n",
      " 13  Community Area        float64\n",
      " 14  FBI Code              object \n",
      " 15  X Coordinate          float64\n",
      " 16  Y Coordinate          float64\n",
      " 17  Year                  int64  \n",
      " 18  Updated On            object \n",
      " 19  Latitude              float64\n",
      " 20  Longitude             float64\n",
      " 21  Location              object \n",
      "dtypes: bool(2), float64(7), int64(3), object(10)\n",
      "memory usage: 1.2+ GB\n",
      "None\n",
      "\n",
      "\n",
      "--- Inspecting Community Areas Race Data ---\n",
      "First few rows:\n",
      "   community_area_number  community_name total_population   asian   black  \\\n",
      "0                      1     Rogers Park           55,643   3,018  15,059   \n",
      "1                      2      West Ridge           78,763  16,556   9,166   \n",
      "2                      3          Uptown           59,453   6,173  10,752   \n",
      "3                      4  Lincoln Square           41,350   3,771   1,234   \n",
      "4                      5    North Center           36,142   2,156     923   \n",
      "\n",
      "  hispanic   white  other  asian_percent  black_percent  hispanic_percent  \\\n",
      "0   10,537  24,644  2,385       0.054239       0.270636          0.189368   \n",
      "1   15,514  33,123  4,404       0.210200       0.116374          0.196971   \n",
      "2    8,911  31,611  2,006       0.103830       0.180849          0.149883   \n",
      "3    7,373  26,590  2,382       0.091197       0.029843          0.178307   \n",
      "4    4,323  27,005  1,735       0.059654       0.025538          0.119612   \n",
      "\n",
      "   white_percent  other_percent  \n",
      "0       0.442895       0.042863  \n",
      "1       0.420540       0.055915  \n",
      "2       0.531697       0.033741  \n",
      "3       0.643047       0.057606  \n",
      "4       0.747192       0.048005  \n",
      "\n",
      "Summary Statistics:\n",
      "\n",
      "Missing Values:\n",
      "community_area_number    0\n",
      "community_name           0\n",
      "total_population         0\n",
      "asian                    0\n",
      "black                    0\n",
      "hispanic                 0\n",
      "white                    0\n",
      "other                    0\n",
      "asian_percent            0\n",
      "black_percent            0\n",
      "hispanic_percent         0\n",
      "white_percent            0\n",
      "other_percent            0\n",
      "dtype: int64\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 77 entries, 0 to 76\n",
      "Data columns (total 13 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   community_area_number  77 non-null     int64  \n",
      " 1   community_name         77 non-null     object \n",
      " 2   total_population       77 non-null     object \n",
      " 3   asian                  77 non-null     object \n",
      " 4   black                  77 non-null     object \n",
      " 5   hispanic               77 non-null     object \n",
      " 6   white                  77 non-null     object \n",
      " 7   other                  77 non-null     object \n",
      " 8   asian_percent          77 non-null     float64\n",
      " 9   black_percent          77 non-null     float64\n",
      " 10  hispanic_percent       77 non-null     float64\n",
      " 11  white_percent          77 non-null     float64\n",
      " 12  other_percent          77 non-null     float64\n",
      "dtypes: float64(5), int64(1), object(7)\n",
      "memory usage: 7.9+ KB\n",
      "None\n",
      "\n",
      "\n",
      "--- Inspecting Citizenship Status Data ---\n",
      "First few rows:\n",
      "   community_area_number  community_name  total_population  us_citizen  \\\n",
      "0                      1     Rogers Park             55643       41124   \n",
      "1                      2      West Ridge             78763       46591   \n",
      "2                      3          Uptown             59453       45193   \n",
      "3                      4  Lincoln Square             41350       32642   \n",
      "4                      5    North Center             36142       32941   \n",
      "\n",
      "   foreign_born  naturalized_citizen  no_citizen  us_citizen_percent  \\\n",
      "0         14519                 5574        8945            0.739069   \n",
      "1         32172                17406       14766            0.591534   \n",
      "2         14260                 7862        6398            0.760147   \n",
      "3          8708                 4496        4212            0.789407   \n",
      "4          3201                 2176        1025            0.911433   \n",
      "\n",
      "   foreign_born_percent  naturalized_citizen_percent  no_citizen_percent  \n",
      "0              0.260931                     0.100174            0.160757  \n",
      "1              0.408466                     0.220992            0.187474  \n",
      "2              0.239853                     0.132239            0.107614  \n",
      "3              0.210593                     0.108730            0.101862  \n",
      "4              0.088567                     0.060207            0.028360  \n",
      "\n",
      "Summary Statistics:\n",
      "\n",
      "Missing Values:\n",
      "community_area_number          0\n",
      "community_name                 0\n",
      "total_population               0\n",
      "us_citizen                     0\n",
      "foreign_born                   0\n",
      "naturalized_citizen            0\n",
      "no_citizen                     0\n",
      "us_citizen_percent             0\n",
      "foreign_born_percent           0\n",
      "naturalized_citizen_percent    0\n",
      "no_citizen_percent             0\n",
      "dtype: int64\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 77 entries, 0 to 76\n",
      "Data columns (total 11 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   community_area_number        77 non-null     int64  \n",
      " 1   community_name               77 non-null     object \n",
      " 2   total_population             77 non-null     int64  \n",
      " 3   us_citizen                   77 non-null     int64  \n",
      " 4   foreign_born                 77 non-null     int64  \n",
      " 5   naturalized_citizen          77 non-null     int64  \n",
      " 6   no_citizen                   77 non-null     int64  \n",
      " 7   us_citizen_percent           77 non-null     float64\n",
      " 8   foreign_born_percent         77 non-null     float64\n",
      " 9   naturalized_citizen_percent  77 non-null     float64\n",
      " 10  no_citizen_percent           77 non-null     float64\n",
      "dtypes: float64(4), int64(6), object(1)\n",
      "memory usage: 6.7+ KB\n",
      "None\n",
      "\n",
      "\n",
      "--- Inspecting Education Level Data ---\n",
      "First few rows:\n",
      "   community_area_number  community_name  total_population  not_hs_grad  \\\n",
      "0                      1     Rogers Park             38323         4480   \n",
      "1                      2      West Ridge             51975         9041   \n",
      "2                      3          Uptown             46702         4579   \n",
      "3                      4  Lincoln Square             31582         2026   \n",
      "4                      5    North Center             24984          854   \n",
      "\n",
      "   hs_grad  some_college  has_bachelors  not_hs_grad_percent  hs_grad_percent  \\\n",
      "0     6901          8859          18083             0.116901         0.180075   \n",
      "1    10150         11124          21660             0.173949         0.195286   \n",
      "2     5830          9359          26934             0.098047         0.124834   \n",
      "3     3600          5119          20837             0.064150         0.113989   \n",
      "4     1398          3330          19402             0.034182         0.055956   \n",
      "\n",
      "   some_college_percent  has_bachelors_percent  hs_grad_or_higher_percent  \n",
      "0              0.231167               0.471858                      0.883  \n",
      "1              0.214026               0.416739                      0.826  \n",
      "2              0.200398               0.576720                      0.902  \n",
      "3              0.162086               0.659775                      0.936  \n",
      "4              0.133285               0.776577                      0.966  \n",
      "\n",
      "Summary Statistics:\n",
      "\n",
      "Missing Values:\n",
      "community_area_number        0\n",
      "community_name               0\n",
      "total_population             0\n",
      "not_hs_grad                  0\n",
      "hs_grad                      0\n",
      "some_college                 0\n",
      "has_bachelors                0\n",
      "not_hs_grad_percent          0\n",
      "hs_grad_percent              0\n",
      "some_college_percent         0\n",
      "has_bachelors_percent        0\n",
      "hs_grad_or_higher_percent    0\n",
      "dtype: int64\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 77 entries, 0 to 76\n",
      "Data columns (total 12 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   community_area_number      77 non-null     int64  \n",
      " 1   community_name             77 non-null     object \n",
      " 2   total_population           77 non-null     int64  \n",
      " 3   not_hs_grad                77 non-null     int64  \n",
      " 4   hs_grad                    77 non-null     int64  \n",
      " 5   some_college               77 non-null     int64  \n",
      " 6   has_bachelors              77 non-null     int64  \n",
      " 7   not_hs_grad_percent        77 non-null     float64\n",
      " 8   hs_grad_percent            77 non-null     float64\n",
      " 9   some_college_percent       77 non-null     float64\n",
      " 10  has_bachelors_percent      77 non-null     float64\n",
      " 11  hs_grad_or_higher_percent  77 non-null     float64\n",
      "dtypes: float64(5), int64(6), object(1)\n",
      "memory usage: 7.3+ KB\n",
      "None\n",
      "\n",
      "\n",
      "--- Inspecting Housing Occupancy Data ---\n",
      "First few rows:\n",
      "   community_area_number  community_name  total_population  owner_occupied  \\\n",
      "0                      1     Rogers Park             24916            6518   \n",
      "1                      2      West Ridge             25869           12557   \n",
      "2                      3          Uptown             30970            9782   \n",
      "3                      4  Lincoln Square             18481            7106   \n",
      "4                      5    North Center             14237            8216   \n",
      "\n",
      "   renter_occupied  owner_percent  renter_percent  \n",
      "0            18398       0.261599        0.738401  \n",
      "1            13312       0.485407        0.514593  \n",
      "2            21188       0.315854        0.684146  \n",
      "3            11375       0.384503        0.615497  \n",
      "4             6021       0.577088        0.422912  \n",
      "\n",
      "Summary Statistics:\n",
      "\n",
      "Missing Values:\n",
      "community_area_number    0\n",
      "community_name           0\n",
      "total_population         0\n",
      "owner_occupied           0\n",
      "renter_occupied          0\n",
      "owner_percent            0\n",
      "renter_percent           0\n",
      "dtype: int64\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 77 entries, 0 to 76\n",
      "Data columns (total 7 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   community_area_number  77 non-null     int64  \n",
      " 1   community_name         77 non-null     object \n",
      " 2   total_population       77 non-null     int64  \n",
      " 3   owner_occupied         77 non-null     int64  \n",
      " 4   renter_occupied        77 non-null     int64  \n",
      " 5   owner_percent          77 non-null     float64\n",
      " 6   renter_percent         77 non-null     float64\n",
      "dtypes: float64(2), int64(4), object(1)\n",
      "memory usage: 4.3+ KB\n",
      "None\n",
      "\n",
      "\n",
      "--- Inspecting Poverty Data ---\n",
      "First few rows:\n",
      "   community_area_number  community_name  total_population  below_poverty  \\\n",
      "0                      1     Rogers Park             52439          11310   \n",
      "1                      2      West Ridge             77573          13204   \n",
      "2                      3          Uptown             58193          12142   \n",
      "3                      4  Lincoln Square             40788           3080   \n",
      "4                      5    North Center             36041           1560   \n",
      "\n",
      "   below_poverty_percent  \n",
      "0               0.215679  \n",
      "1               0.170214  \n",
      "2               0.208651  \n",
      "3               0.075512  \n",
      "4               0.043284  \n",
      "\n",
      "Summary Statistics:\n",
      "\n",
      "Missing Values:\n",
      "community_area_number    0\n",
      "community_name           0\n",
      "total_population         0\n",
      "below_poverty            0\n",
      "below_poverty_percent    0\n",
      "dtype: int64\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 77 entries, 0 to 76\n",
      "Data columns (total 5 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   community_area_number  77 non-null     int64  \n",
      " 1   community_name         77 non-null     object \n",
      " 2   total_population       77 non-null     int64  \n",
      " 3   below_poverty          77 non-null     int64  \n",
      " 4   below_poverty_percent  77 non-null     float64\n",
      "dtypes: float64(1), int64(3), object(1)\n",
      "memory usage: 3.1+ KB\n",
      "None\n",
      "\n",
      "\n",
      "--- Inspecting Insurance Data ---\n",
      "First few rows:\n",
      "   community_area_number  community_name  total_population  insured  \\\n",
      "0                      1     Rogers Park             54097    47143   \n",
      "1                      2      West Ridge             77680    66818   \n",
      "2                      3          Uptown             58188    52667   \n",
      "3                      4  Lincoln Square             40840    37544   \n",
      "4                      5    North Center             36080    35292   \n",
      "\n",
      "   insured_percent  \n",
      "0         0.871453  \n",
      "1         0.860170  \n",
      "2         0.905118  \n",
      "3         0.919295  \n",
      "4         0.978160  \n",
      "\n",
      "Summary Statistics:\n",
      "\n",
      "Missing Values:\n",
      "community_area_number    0\n",
      "community_name           0\n",
      "total_population         0\n",
      "insured                  0\n",
      "insured_percent          0\n",
      "dtype: int64\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 77 entries, 0 to 76\n",
      "Data columns (total 5 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   community_area_number  77 non-null     int64  \n",
      " 1   community_name         77 non-null     object \n",
      " 2   total_population       77 non-null     int64  \n",
      " 3   insured                77 non-null     int64  \n",
      " 4   insured_percent        77 non-null     float64\n",
      "dtypes: float64(1), int64(3), object(1)\n",
      "memory usage: 3.1+ KB\n",
      "None\n",
      "\n",
      "\n",
      "--- Inspecting Community Areas Income Data ---\n",
      "First few rows:\n",
      "   community_area_number  community_name  total_population  \\\n",
      "0                      1     Rogers Park             24916   \n",
      "1                      2      West Ridge             25869   \n",
      "2                      3          Uptown             30970   \n",
      "3                      4  Lincoln Square             18481   \n",
      "4                      5    North Center             14237   \n",
      "\n",
      "   median_household_income  low_income  middle_income  high_income  \\\n",
      "0                    46164       10925          11113         2878   \n",
      "1                    57621        8498          12503         4868   \n",
      "2                    57576       11102          13694         6174   \n",
      "3                    79696        4218           8901         5362   \n",
      "4                   132628        1926           4837         7474   \n",
      "\n",
      "   below_10_income  10_to_15_income  15_to_20_income  ...  \\\n",
      "0             2652             1836             1097  ...   \n",
      "1             1447             1039             1200  ...   \n",
      "2             3200             1838             1286  ...   \n",
      "3              768              329              417  ...   \n",
      "4              323              215              163  ...   \n",
      "\n",
      "   35_to_40_income_percent  40_to_45_income_percent  45_to_50_income_percent  \\\n",
      "0                 0.055025                 0.053058                 0.034034   \n",
      "1                 0.044880                 0.050833                 0.043836   \n",
      "2                 0.037359                 0.036067                 0.038683   \n",
      "3                 0.034847                 0.040799                 0.045560   \n",
      "4                 0.032521                 0.011730                 0.010114   \n",
      "\n",
      "   50_to_60_income_percent  60_to_75_income_percent  75_to_100_income_percent  \\\n",
      "0                 0.078584                 0.084524                  0.123936   \n",
      "1                 0.096795                 0.096641                  0.116781   \n",
      "2                 0.084759                 0.090701                  0.118728   \n",
      "3                 0.061090                 0.092636                  0.134246   \n",
      "4                 0.033153                 0.058510                  0.119969   \n",
      "\n",
      "   100_to_125_income_percent  125_to_150_income_percent  \\\n",
      "0                   0.071882                   0.041018   \n",
      "1                   0.078434                   0.056322   \n",
      "2                   0.073232                   0.060510   \n",
      "3                   0.107299                   0.092365   \n",
      "4                   0.106272                   0.073119   \n",
      "\n",
      "   150_to_200_income_percent  above_200_income_percent  \n",
      "0                   0.040616                  0.033874  \n",
      "1                   0.063358                  0.068499  \n",
      "2                   0.070520                  0.068324  \n",
      "3                   0.079866                  0.117905  \n",
      "4                   0.136124                  0.315727  \n",
      "\n",
      "[5 rows x 42 columns]\n",
      "\n",
      "Summary Statistics:\n",
      "\n",
      "Missing Values:\n",
      "community_area_number        0\n",
      "community_name               0\n",
      "total_population             0\n",
      "median_household_income      0\n",
      "low_income                   0\n",
      "middle_income                0\n",
      "high_income                  0\n",
      "below_10_income              0\n",
      "10_to_15_income              0\n",
      "15_to_20_income              0\n",
      "20_to_25_income              0\n",
      "25_to_30_income              0\n",
      "30_to_35_income              0\n",
      "35_to_40_income              0\n",
      "40_to_45_income              0\n",
      "45_to_50_income              0\n",
      "50_to_60_income              0\n",
      "60_to_75_income              0\n",
      "75_to_100_income             0\n",
      "100_to_125_income            0\n",
      "125_to_150_income            0\n",
      "150_to_200_income            0\n",
      "above_200_income             0\n",
      "low_income_percent           0\n",
      "middle_income_percent        0\n",
      "high_income_percent          0\n",
      "below_10_income_percent      0\n",
      "10_to_15_income_percent      0\n",
      "15_to_20_income_percent      0\n",
      "20_to_25_income_percent      0\n",
      "25_to_30_income_percent      0\n",
      "30_to_35_income_percent      0\n",
      "35_to_40_income_percent      0\n",
      "40_to_45_income_percent      0\n",
      "45_to_50_income_percent      0\n",
      "50_to_60_income_percent      0\n",
      "60_to_75_income_percent      0\n",
      "75_to_100_income_percent     0\n",
      "100_to_125_income_percent    0\n",
      "125_to_150_income_percent    0\n",
      "150_to_200_income_percent    0\n",
      "above_200_income_percent     0\n",
      "dtype: int64\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 77 entries, 0 to 76\n",
      "Data columns (total 42 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   community_area_number      77 non-null     int64  \n",
      " 1   community_name             77 non-null     object \n",
      " 2   total_population           77 non-null     int64  \n",
      " 3   median_household_income    77 non-null     int64  \n",
      " 4   low_income                 77 non-null     int64  \n",
      " 5   middle_income              77 non-null     int64  \n",
      " 6   high_income                77 non-null     int64  \n",
      " 7   below_10_income            77 non-null     int64  \n",
      " 8   10_to_15_income            77 non-null     int64  \n",
      " 9   15_to_20_income            77 non-null     int64  \n",
      " 10  20_to_25_income            77 non-null     int64  \n",
      " 11  25_to_30_income            77 non-null     int64  \n",
      " 12  30_to_35_income            77 non-null     int64  \n",
      " 13  35_to_40_income            77 non-null     int64  \n",
      " 14  40_to_45_income            77 non-null     int64  \n",
      " 15  45_to_50_income            77 non-null     int64  \n",
      " 16  50_to_60_income            77 non-null     int64  \n",
      " 17  60_to_75_income            77 non-null     int64  \n",
      " 18  75_to_100_income           77 non-null     int64  \n",
      " 19  100_to_125_income          77 non-null     int64  \n",
      " 20  125_to_150_income          77 non-null     int64  \n",
      " 21  150_to_200_income          77 non-null     int64  \n",
      " 22  above_200_income           77 non-null     int64  \n",
      " 23  low_income_percent         77 non-null     float64\n",
      " 24  middle_income_percent      77 non-null     float64\n",
      " 25  high_income_percent        77 non-null     float64\n",
      " 26  below_10_income_percent    77 non-null     float64\n",
      " 27  10_to_15_income_percent    77 non-null     float64\n",
      " 28  15_to_20_income_percent    77 non-null     float64\n",
      " 29  20_to_25_income_percent    77 non-null     float64\n",
      " 30  25_to_30_income_percent    77 non-null     float64\n",
      " 31  30_to_35_income_percent    77 non-null     float64\n",
      " 32  35_to_40_income_percent    77 non-null     float64\n",
      " 33  40_to_45_income_percent    77 non-null     float64\n",
      " 34  45_to_50_income_percent    77 non-null     float64\n",
      " 35  50_to_60_income_percent    77 non-null     float64\n",
      " 36  60_to_75_income_percent    77 non-null     float64\n",
      " 37  75_to_100_income_percent   77 non-null     float64\n",
      " 38  100_to_125_income_percent  77 non-null     float64\n",
      " 39  125_to_150_income_percent  77 non-null     float64\n",
      " 40  150_to_200_income_percent  77 non-null     float64\n",
      " 41  above_200_income_percent   77 non-null     float64\n",
      "dtypes: float64(19), int64(22), object(1)\n",
      "memory usage: 25.4+ KB\n",
      "None\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Inspect each dataset\n",
    "inspect_data(df_weather, \"Weather Data\")\n",
    "inspect_data(df_gas, \"Gas Prices Data\")\n",
    "inspect_data(df_e_scooter_trips, \"E-Scooter Trips Data\")\n",
    "inspect_data(df_crimes, \"Crimes Data\")\n",
    "inspect_data(df_race, \"Community Areas Race Data\")\n",
    "inspect_data(df_citizenship, \"Citizenship Status Data\")\n",
    "inspect_data(df_education, \"Education Level Data\")\n",
    "inspect_data(df_housing, \"Housing Occupancy Data\")\n",
    "inspect_data(df_poverty, \"Poverty Data\")\n",
    "inspect_data(df_insurance, \"Insurance Data\")\n",
    "inspect_data(df_income, \"Community Areas Income Data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3076a4c6",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "221e733e",
   "metadata": {},
   "source": [
    "### Crimes Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d7df2feb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling missing values in the Crimes dataset\n",
    "# Fill missing coordinates and location with NaN\n",
    "df_crimes[['X Coordinate', 'Y Coordinate', 'Latitude', 'Longitude', 'Location']] = df_crimes[['X Coordinate', 'Y Coordinate', 'Latitude', 'Longitude', 'Location']].replace(0, np.nan)\n",
    "\n",
    "# For 'Location Description', 'District', 'Ward', and 'Community Area', we will fill missing values with a placeholder\n",
    "df_crimes['Location Description'] = df_crimes['Location Description'].fillna('Unknown')\n",
    "df_crimes['District'] = df_crimes['District'].fillna('Unknown')\n",
    "df_crimes['Ward'] = df_crimes['Ward'].fillna('Unknown')\n",
    "df_crimes['Community Area'] = df_crimes['Community Area'].fillna('Unknown')\n",
    "\n",
    "# Dropping rows where essential coordinates are missing since they are crucial for spatial analysis\n",
    "df_crimes.dropna(subset=['X Coordinate', 'Y Coordinate', 'Latitude', 'Longitude'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f0892ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Case Number</th>\n",
       "      <th>Date</th>\n",
       "      <th>Block</th>\n",
       "      <th>IUCR</th>\n",
       "      <th>Primary Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Location Description</th>\n",
       "      <th>Arrest</th>\n",
       "      <th>Domestic</th>\n",
       "      <th>...</th>\n",
       "      <th>Ward</th>\n",
       "      <th>Community Area</th>\n",
       "      <th>FBI Code</th>\n",
       "      <th>X Coordinate</th>\n",
       "      <th>Y Coordinate</th>\n",
       "      <th>Year</th>\n",
       "      <th>Updated On</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25953</td>\n",
       "      <td>JE240540</td>\n",
       "      <td>05/24/2021 03:06:00 PM</td>\n",
       "      <td>020XX N LARAMIE AVE</td>\n",
       "      <td>0110</td>\n",
       "      <td>HOMICIDE</td>\n",
       "      <td>FIRST DEGREE MURDER</td>\n",
       "      <td>STREET</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>36.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>01A</td>\n",
       "      <td>1141387.0</td>\n",
       "      <td>1913179.0</td>\n",
       "      <td>2021</td>\n",
       "      <td>11/18/2023 03:39:49 PM</td>\n",
       "      <td>41.917838</td>\n",
       "      <td>-87.755969</td>\n",
       "      <td>(41.917838056, -87.755968972)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26038</td>\n",
       "      <td>JE279849</td>\n",
       "      <td>06/26/2021 09:24:00 AM</td>\n",
       "      <td>062XX N MC CORMICK RD</td>\n",
       "      <td>0110</td>\n",
       "      <td>HOMICIDE</td>\n",
       "      <td>FIRST DEGREE MURDER</td>\n",
       "      <td>PARKING LOT</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>01A</td>\n",
       "      <td>1152781.0</td>\n",
       "      <td>1941458.0</td>\n",
       "      <td>2021</td>\n",
       "      <td>11/18/2023 03:39:49 PM</td>\n",
       "      <td>41.995219</td>\n",
       "      <td>-87.713355</td>\n",
       "      <td>(41.995219444, -87.713354912)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13279676</td>\n",
       "      <td>JG507211</td>\n",
       "      <td>11/09/2023 07:30:00 AM</td>\n",
       "      <td>019XX W BYRON ST</td>\n",
       "      <td>0620</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>UNLAWFUL ENTRY</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>47.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>05</td>\n",
       "      <td>1162518.0</td>\n",
       "      <td>1925906.0</td>\n",
       "      <td>2023</td>\n",
       "      <td>11/18/2023 03:39:49 PM</td>\n",
       "      <td>41.952345</td>\n",
       "      <td>-87.677975</td>\n",
       "      <td>(41.952345086, -87.677975059)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13274752</td>\n",
       "      <td>JG501049</td>\n",
       "      <td>11/12/2023 07:59:00 AM</td>\n",
       "      <td>086XX S COTTAGE GROVE AVE</td>\n",
       "      <td>0454</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>AGGRAVATED P.O. - HANDS, FISTS, FEET, NO / MIN...</td>\n",
       "      <td>SMALL RETAIL STORE</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>08B</td>\n",
       "      <td>1183071.0</td>\n",
       "      <td>1847869.0</td>\n",
       "      <td>2023</td>\n",
       "      <td>12/09/2023 03:41:24 PM</td>\n",
       "      <td>41.737751</td>\n",
       "      <td>-87.604856</td>\n",
       "      <td>(41.737750767, -87.604855911)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13203321</td>\n",
       "      <td>JG415333</td>\n",
       "      <td>09/06/2023 05:00:00 PM</td>\n",
       "      <td>002XX N Wells st</td>\n",
       "      <td>1320</td>\n",
       "      <td>CRIMINAL DAMAGE</td>\n",
       "      <td>TO VEHICLE</td>\n",
       "      <td>PARKING LOT / GARAGE (NON RESIDENTIAL)</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1174694.0</td>\n",
       "      <td>1901831.0</td>\n",
       "      <td>2023</td>\n",
       "      <td>11/04/2023 03:40:18 PM</td>\n",
       "      <td>41.886018</td>\n",
       "      <td>-87.633938</td>\n",
       "      <td>(41.886018055, -87.633937881)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID Case Number                    Date                      Block  \\\n",
       "1     25953    JE240540  05/24/2021 03:06:00 PM        020XX N LARAMIE AVE   \n",
       "2     26038    JE279849  06/26/2021 09:24:00 AM      062XX N MC CORMICK RD   \n",
       "3  13279676    JG507211  11/09/2023 07:30:00 AM           019XX W BYRON ST   \n",
       "4  13274752    JG501049  11/12/2023 07:59:00 AM  086XX S COTTAGE GROVE AVE   \n",
       "6  13203321    JG415333  09/06/2023 05:00:00 PM           002XX N Wells st   \n",
       "\n",
       "   IUCR     Primary Type                                        Description  \\\n",
       "1  0110         HOMICIDE                                FIRST DEGREE MURDER   \n",
       "2  0110         HOMICIDE                                FIRST DEGREE MURDER   \n",
       "3  0620         BURGLARY                                     UNLAWFUL ENTRY   \n",
       "4  0454          BATTERY  AGGRAVATED P.O. - HANDS, FISTS, FEET, NO / MIN...   \n",
       "6  1320  CRIMINAL DAMAGE                                         TO VEHICLE   \n",
       "\n",
       "                     Location Description  Arrest  Domestic  ...  Ward  \\\n",
       "1                                  STREET    True     False  ...  36.0   \n",
       "2                             PARKING LOT    True     False  ...  50.0   \n",
       "3                               APARTMENT   False     False  ...  47.0   \n",
       "4                      SMALL RETAIL STORE    True     False  ...   6.0   \n",
       "6  PARKING LOT / GARAGE (NON RESIDENTIAL)   False     False  ...  42.0   \n",
       "\n",
       "  Community Area FBI Code X Coordinate Y Coordinate  Year  \\\n",
       "1           19.0      01A    1141387.0    1913179.0  2021   \n",
       "2           13.0      01A    1152781.0    1941458.0  2021   \n",
       "3            5.0       05    1162518.0    1925906.0  2023   \n",
       "4           44.0      08B    1183071.0    1847869.0  2023   \n",
       "6           32.0       14    1174694.0    1901831.0  2023   \n",
       "\n",
       "               Updated On   Latitude  Longitude                       Location  \n",
       "1  11/18/2023 03:39:49 PM  41.917838 -87.755969  (41.917838056, -87.755968972)  \n",
       "2  11/18/2023 03:39:49 PM  41.995219 -87.713355  (41.995219444, -87.713354912)  \n",
       "3  11/18/2023 03:39:49 PM  41.952345 -87.677975  (41.952345086, -87.677975059)  \n",
       "4  12/09/2023 03:41:24 PM  41.737751 -87.604856  (41.737750767, -87.604855911)  \n",
       "6  11/04/2023 03:40:18 PM  41.886018 -87.633938  (41.886018055, -87.633937881)  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_crimes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9edf0d6",
   "metadata": {},
   "source": [
    "### Race Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "86f5f57d",
   "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>community_area_number</th>\n",
       "      <th>community_name</th>\n",
       "      <th>total_population</th>\n",
       "      <th>asian</th>\n",
       "      <th>black</th>\n",
       "      <th>hispanic</th>\n",
       "      <th>white</th>\n",
       "      <th>other</th>\n",
       "      <th>asian_percent</th>\n",
       "      <th>black_percent</th>\n",
       "      <th>hispanic_percent</th>\n",
       "      <th>white_percent</th>\n",
       "      <th>other_percent</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Rogers Park</td>\n",
       "      <td>55,643</td>\n",
       "      <td>3,018</td>\n",
       "      <td>15,059</td>\n",
       "      <td>10,537</td>\n",
       "      <td>24,644</td>\n",
       "      <td>2,385</td>\n",
       "      <td>0.054239</td>\n",
       "      <td>0.270636</td>\n",
       "      <td>0.189368</td>\n",
       "      <td>0.442895</td>\n",
       "      <td>0.042863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>West Ridge</td>\n",
       "      <td>78,763</td>\n",
       "      <td>16,556</td>\n",
       "      <td>9,166</td>\n",
       "      <td>15,514</td>\n",
       "      <td>33,123</td>\n",
       "      <td>4,404</td>\n",
       "      <td>0.210200</td>\n",
       "      <td>0.116374</td>\n",
       "      <td>0.196971</td>\n",
       "      <td>0.420540</td>\n",
       "      <td>0.055915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Uptown</td>\n",
       "      <td>59,453</td>\n",
       "      <td>6,173</td>\n",
       "      <td>10,752</td>\n",
       "      <td>8,911</td>\n",
       "      <td>31,611</td>\n",
       "      <td>2,006</td>\n",
       "      <td>0.103830</td>\n",
       "      <td>0.180849</td>\n",
       "      <td>0.149883</td>\n",
       "      <td>0.531697</td>\n",
       "      <td>0.033741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>41,350</td>\n",
       "      <td>3,771</td>\n",
       "      <td>1,234</td>\n",
       "      <td>7,373</td>\n",
       "      <td>26,590</td>\n",
       "      <td>2,382</td>\n",
       "      <td>0.091197</td>\n",
       "      <td>0.029843</td>\n",
       "      <td>0.178307</td>\n",
       "      <td>0.643047</td>\n",
       "      <td>0.057606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>North Center</td>\n",
       "      <td>36,142</td>\n",
       "      <td>2,156</td>\n",
       "      <td>923</td>\n",
       "      <td>4,323</td>\n",
       "      <td>27,005</td>\n",
       "      <td>1,735</td>\n",
       "      <td>0.059654</td>\n",
       "      <td>0.025538</td>\n",
       "      <td>0.119612</td>\n",
       "      <td>0.747192</td>\n",
       "      <td>0.048005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   community_area_number  community_name total_population   asian   black  \\\n",
       "0                      1     Rogers Park           55,643   3,018  15,059   \n",
       "1                      2      West Ridge           78,763  16,556   9,166   \n",
       "2                      3          Uptown           59,453   6,173  10,752   \n",
       "3                      4  Lincoln Square           41,350   3,771   1,234   \n",
       "4                      5    North Center           36,142   2,156     923   \n",
       "\n",
       "  hispanic   white  other  asian_percent  black_percent  hispanic_percent  \\\n",
       "0   10,537  24,644  2,385       0.054239       0.270636          0.189368   \n",
       "1   15,514  33,123  4,404       0.210200       0.116374          0.196971   \n",
       "2    8,911  31,611  2,006       0.103830       0.180849          0.149883   \n",
       "3    7,373  26,590  2,382       0.091197       0.029843          0.178307   \n",
       "4    4,323  27,005  1,735       0.059654       0.025538          0.119612   \n",
       "\n",
       "   white_percent  other_percent  \n",
       "0       0.442895       0.042863  \n",
       "1       0.420540       0.055915  \n",
       "2       0.531697       0.033741  \n",
       "3       0.643047       0.057606  \n",
       "4       0.747192       0.048005  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_race.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d9f937cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleaning the Race dataset\n",
    "# Rename columns for clarity -- [TAB] dataset changed, this is no longer necessary. Commented out for now.\n",
    "#df_race.rename(columns={'Race and Ethnicity in Chicago Community Areas': 'Community Area', 'Unnamed: 1': 'Data'}, inplace=True)\n",
    "#df_race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "88d8b2e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>community_area_number</th>\n",
       "      <th>community_name</th>\n",
       "      <th>total_population</th>\n",
       "      <th>asian</th>\n",
       "      <th>black</th>\n",
       "      <th>hispanic</th>\n",
       "      <th>white</th>\n",
       "      <th>other</th>\n",
       "      <th>asian_percent</th>\n",
       "      <th>black_percent</th>\n",
       "      <th>hispanic_percent</th>\n",
       "      <th>white_percent</th>\n",
       "      <th>other_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Rogers Park</td>\n",
       "      <td>55,643</td>\n",
       "      <td>3,018</td>\n",
       "      <td>15,059</td>\n",
       "      <td>10,537</td>\n",
       "      <td>24,644</td>\n",
       "      <td>2,385</td>\n",
       "      <td>0.054239</td>\n",
       "      <td>0.270636</td>\n",
       "      <td>0.189368</td>\n",
       "      <td>0.442895</td>\n",
       "      <td>0.042863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>West Ridge</td>\n",
       "      <td>78,763</td>\n",
       "      <td>16,556</td>\n",
       "      <td>9,166</td>\n",
       "      <td>15,514</td>\n",
       "      <td>33,123</td>\n",
       "      <td>4,404</td>\n",
       "      <td>0.210200</td>\n",
       "      <td>0.116374</td>\n",
       "      <td>0.196971</td>\n",
       "      <td>0.420540</td>\n",
       "      <td>0.055915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Uptown</td>\n",
       "      <td>59,453</td>\n",
       "      <td>6,173</td>\n",
       "      <td>10,752</td>\n",
       "      <td>8,911</td>\n",
       "      <td>31,611</td>\n",
       "      <td>2,006</td>\n",
       "      <td>0.103830</td>\n",
       "      <td>0.180849</td>\n",
       "      <td>0.149883</td>\n",
       "      <td>0.531697</td>\n",
       "      <td>0.033741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>41,350</td>\n",
       "      <td>3,771</td>\n",
       "      <td>1,234</td>\n",
       "      <td>7,373</td>\n",
       "      <td>26,590</td>\n",
       "      <td>2,382</td>\n",
       "      <td>0.091197</td>\n",
       "      <td>0.029843</td>\n",
       "      <td>0.178307</td>\n",
       "      <td>0.643047</td>\n",
       "      <td>0.057606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>North Center</td>\n",
       "      <td>36,142</td>\n",
       "      <td>2,156</td>\n",
       "      <td>923</td>\n",
       "      <td>4,323</td>\n",
       "      <td>27,005</td>\n",
       "      <td>1,735</td>\n",
       "      <td>0.059654</td>\n",
       "      <td>0.025538</td>\n",
       "      <td>0.119612</td>\n",
       "      <td>0.747192</td>\n",
       "      <td>0.048005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>Washington Heights</td>\n",
       "      <td>26,940</td>\n",
       "      <td>47</td>\n",
       "      <td>25,722</td>\n",
       "      <td>393</td>\n",
       "      <td>451</td>\n",
       "      <td>327</td>\n",
       "      <td>0.001745</td>\n",
       "      <td>0.954788</td>\n",
       "      <td>0.014588</td>\n",
       "      <td>0.016741</td>\n",
       "      <td>0.012138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74</td>\n",
       "      <td>Mount Greenwood</td>\n",
       "      <td>18,840</td>\n",
       "      <td>303</td>\n",
       "      <td>609</td>\n",
       "      <td>1,975</td>\n",
       "      <td>15,287</td>\n",
       "      <td>666</td>\n",
       "      <td>0.016083</td>\n",
       "      <td>0.032325</td>\n",
       "      <td>0.104830</td>\n",
       "      <td>0.811412</td>\n",
       "      <td>0.035350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>Morgan Park</td>\n",
       "      <td>20,526</td>\n",
       "      <td>86</td>\n",
       "      <td>12,607</td>\n",
       "      <td>910</td>\n",
       "      <td>6,237</td>\n",
       "      <td>686</td>\n",
       "      <td>0.004190</td>\n",
       "      <td>0.614197</td>\n",
       "      <td>0.044334</td>\n",
       "      <td>0.303859</td>\n",
       "      <td>0.033421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76</td>\n",
       "      <td>O'Hare</td>\n",
       "      <td>13,285</td>\n",
       "      <td>2,096</td>\n",
       "      <td>892</td>\n",
       "      <td>1,091</td>\n",
       "      <td>8,921</td>\n",
       "      <td>285</td>\n",
       "      <td>0.157772</td>\n",
       "      <td>0.067143</td>\n",
       "      <td>0.082123</td>\n",
       "      <td>0.671509</td>\n",
       "      <td>0.021453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77</td>\n",
       "      <td>Edgewater</td>\n",
       "      <td>58,693</td>\n",
       "      <td>8,059</td>\n",
       "      <td>7,781</td>\n",
       "      <td>9,323</td>\n",
       "      <td>31,412</td>\n",
       "      <td>2,118</td>\n",
       "      <td>0.137308</td>\n",
       "      <td>0.132571</td>\n",
       "      <td>0.158843</td>\n",
       "      <td>0.535192</td>\n",
       "      <td>0.036086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    community_area_number      community_name total_population   asian  \\\n",
       "0                       1         Rogers Park           55,643   3,018   \n",
       "1                       2          West Ridge           78,763  16,556   \n",
       "2                       3              Uptown           59,453   6,173   \n",
       "3                       4      Lincoln Square           41,350   3,771   \n",
       "4                       5        North Center           36,142   2,156   \n",
       "..                    ...                 ...              ...     ...   \n",
       "72                     73  Washington Heights           26,940      47   \n",
       "73                     74     Mount Greenwood           18,840     303   \n",
       "74                     75         Morgan Park           20,526      86   \n",
       "75                     76              O'Hare           13,285   2,096   \n",
       "76                     77           Edgewater           58,693   8,059   \n",
       "\n",
       "     black hispanic   white  other  asian_percent  black_percent  \\\n",
       "0   15,059   10,537  24,644  2,385       0.054239       0.270636   \n",
       "1    9,166   15,514  33,123  4,404       0.210200       0.116374   \n",
       "2   10,752    8,911  31,611  2,006       0.103830       0.180849   \n",
       "3    1,234    7,373  26,590  2,382       0.091197       0.029843   \n",
       "4      923    4,323  27,005  1,735       0.059654       0.025538   \n",
       "..     ...      ...     ...    ...            ...            ...   \n",
       "72  25,722      393     451    327       0.001745       0.954788   \n",
       "73     609    1,975  15,287    666       0.016083       0.032325   \n",
       "74  12,607      910   6,237    686       0.004190       0.614197   \n",
       "75     892    1,091   8,921    285       0.157772       0.067143   \n",
       "76   7,781    9,323  31,412  2,118       0.137308       0.132571   \n",
       "\n",
       "    hispanic_percent  white_percent  other_percent  \n",
       "0           0.189368       0.442895       0.042863  \n",
       "1           0.196971       0.420540       0.055915  \n",
       "2           0.149883       0.531697       0.033741  \n",
       "3           0.178307       0.643047       0.057606  \n",
       "4           0.119612       0.747192       0.048005  \n",
       "..               ...            ...            ...  \n",
       "72          0.014588       0.016741       0.012138  \n",
       "73          0.104830       0.811412       0.035350  \n",
       "74          0.044334       0.303859       0.033421  \n",
       "75          0.082123       0.671509       0.021453  \n",
       "76          0.158843       0.535192       0.036086  \n",
       "\n",
       "[77 rows x 13 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Include cleaning up of race dataset here\n",
    "df_race"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b71400c",
   "metadata": {},
   "source": [
    "### Weather Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f71ce6c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'datetime', 'tempmax', 'tempmin', 'temp', 'feelslikemax',\n",
       "       'feelslikemin', 'feelslike', 'dew', 'humidity', 'precip', 'precipprob',\n",
       "       'precipcover', 'preciptype', 'snow', 'snowdepth', 'windgust',\n",
       "       'windspeed', 'winddir', 'sealevelpressure', 'cloudcover', 'visibility',\n",
       "       'solarradiation', 'solarenergy', 'uvindex', 'severerisk', 'sunrise',\n",
       "       'sunset', 'moonphase', 'conditions', 'description', 'icon', 'stations'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weather.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6e9566ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Include cleaning up of weather dataset here\n",
    "df_weather = df_weather[['datetime', 'temp', 'precip', 'snow']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b5e9b9c",
   "metadata": {},
   "source": [
    "### Gas Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ebe5ca4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly Chicago, IL Regular Reformulated Retail Gasoline Prices  (Dollars per Gallon)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5/2/2022</td>\n",
       "      <td>4.467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5/9/2022</td>\n",
       "      <td>4.672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5/16/2022</td>\n",
       "      <td>4.929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5/23/2022</td>\n",
       "      <td>5.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5/30/2022</td>\n",
       "      <td>5.264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>9/4/2023</td>\n",
       "      <td>4.189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>9/11/2023</td>\n",
       "      <td>4.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>9/18/2023</td>\n",
       "      <td>4.068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>9/25/2023</td>\n",
       "      <td>3.962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>10/2/2023</td>\n",
       "      <td>3.814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  \\\n",
       "0    5/2/2022   \n",
       "1    5/9/2022   \n",
       "2   5/16/2022   \n",
       "3   5/23/2022   \n",
       "4   5/30/2022   \n",
       "..        ...   \n",
       "70   9/4/2023   \n",
       "71  9/11/2023   \n",
       "72  9/18/2023   \n",
       "73  9/25/2023   \n",
       "74  10/2/2023   \n",
       "\n",
       "    Weekly Chicago, IL Regular Reformulated Retail Gasoline Prices  (Dollars per Gallon)  \n",
       "0                                               4.467                                     \n",
       "1                                               4.672                                     \n",
       "2                                               4.929                                     \n",
       "3                                               5.133                                     \n",
       "4                                               5.264                                     \n",
       "..                                                ...                                     \n",
       "70                                              4.189                                     \n",
       "71                                              4.108                                     \n",
       "72                                              4.068                                     \n",
       "73                                              3.962                                     \n",
       "74                                              3.814                                     \n",
       "\n",
       "[75 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Include cleaning up of gas dataset here\n",
    "df_gas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c735f86d",
   "metadata": {},
   "source": [
    "### Income Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "637a9086",
   "metadata": {},
   "source": [
    "- We define low income as earning below 40,000.\n",
    "- We define high income as earning above 130,000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "76f86643",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>community_area_number</th>\n",
       "      <th>community_name</th>\n",
       "      <th>total_population</th>\n",
       "      <th>median_household_income</th>\n",
       "      <th>low_income</th>\n",
       "      <th>middle_income</th>\n",
       "      <th>high_income</th>\n",
       "      <th>below_10_income</th>\n",
       "      <th>10_to_15_income</th>\n",
       "      <th>15_to_20_income</th>\n",
       "      <th>...</th>\n",
       "      <th>35_to_40_income_percent</th>\n",
       "      <th>40_to_45_income_percent</th>\n",
       "      <th>45_to_50_income_percent</th>\n",
       "      <th>50_to_60_income_percent</th>\n",
       "      <th>60_to_75_income_percent</th>\n",
       "      <th>75_to_100_income_percent</th>\n",
       "      <th>100_to_125_income_percent</th>\n",
       "      <th>125_to_150_income_percent</th>\n",
       "      <th>150_to_200_income_percent</th>\n",
       "      <th>above_200_income_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Rogers Park</td>\n",
       "      <td>24916</td>\n",
       "      <td>46164</td>\n",
       "      <td>10925</td>\n",
       "      <td>11113</td>\n",
       "      <td>2878</td>\n",
       "      <td>2652</td>\n",
       "      <td>1836</td>\n",
       "      <td>1097</td>\n",
       "      <td>...</td>\n",
       "      <td>0.055025</td>\n",
       "      <td>0.053058</td>\n",
       "      <td>0.034034</td>\n",
       "      <td>0.078584</td>\n",
       "      <td>0.084524</td>\n",
       "      <td>0.123936</td>\n",
       "      <td>0.071882</td>\n",
       "      <td>0.041018</td>\n",
       "      <td>0.040616</td>\n",
       "      <td>0.033874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>West Ridge</td>\n",
       "      <td>25869</td>\n",
       "      <td>57621</td>\n",
       "      <td>8498</td>\n",
       "      <td>12503</td>\n",
       "      <td>4868</td>\n",
       "      <td>1447</td>\n",
       "      <td>1039</td>\n",
       "      <td>1200</td>\n",
       "      <td>...</td>\n",
       "      <td>0.044880</td>\n",
       "      <td>0.050833</td>\n",
       "      <td>0.043836</td>\n",
       "      <td>0.096795</td>\n",
       "      <td>0.096641</td>\n",
       "      <td>0.116781</td>\n",
       "      <td>0.078434</td>\n",
       "      <td>0.056322</td>\n",
       "      <td>0.063358</td>\n",
       "      <td>0.068499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Uptown</td>\n",
       "      <td>30970</td>\n",
       "      <td>57576</td>\n",
       "      <td>11102</td>\n",
       "      <td>13694</td>\n",
       "      <td>6174</td>\n",
       "      <td>3200</td>\n",
       "      <td>1838</td>\n",
       "      <td>1286</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037359</td>\n",
       "      <td>0.036067</td>\n",
       "      <td>0.038683</td>\n",
       "      <td>0.084759</td>\n",
       "      <td>0.090701</td>\n",
       "      <td>0.118728</td>\n",
       "      <td>0.073232</td>\n",
       "      <td>0.060510</td>\n",
       "      <td>0.070520</td>\n",
       "      <td>0.068324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>18481</td>\n",
       "      <td>79696</td>\n",
       "      <td>4218</td>\n",
       "      <td>8901</td>\n",
       "      <td>5362</td>\n",
       "      <td>768</td>\n",
       "      <td>329</td>\n",
       "      <td>417</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034847</td>\n",
       "      <td>0.040799</td>\n",
       "      <td>0.045560</td>\n",
       "      <td>0.061090</td>\n",
       "      <td>0.092636</td>\n",
       "      <td>0.134246</td>\n",
       "      <td>0.107299</td>\n",
       "      <td>0.092365</td>\n",
       "      <td>0.079866</td>\n",
       "      <td>0.117905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>North Center</td>\n",
       "      <td>14237</td>\n",
       "      <td>132628</td>\n",
       "      <td>1926</td>\n",
       "      <td>4837</td>\n",
       "      <td>7474</td>\n",
       "      <td>323</td>\n",
       "      <td>215</td>\n",
       "      <td>163</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032521</td>\n",
       "      <td>0.011730</td>\n",
       "      <td>0.010114</td>\n",
       "      <td>0.033153</td>\n",
       "      <td>0.058510</td>\n",
       "      <td>0.119969</td>\n",
       "      <td>0.106272</td>\n",
       "      <td>0.073119</td>\n",
       "      <td>0.136124</td>\n",
       "      <td>0.315727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>Washington Heights</td>\n",
       "      <td>9331</td>\n",
       "      <td>53182</td>\n",
       "      <td>3784</td>\n",
       "      <td>4464</td>\n",
       "      <td>1083</td>\n",
       "      <td>1005</td>\n",
       "      <td>647</td>\n",
       "      <td>422</td>\n",
       "      <td>...</td>\n",
       "      <td>0.039331</td>\n",
       "      <td>0.033115</td>\n",
       "      <td>0.031079</td>\n",
       "      <td>0.084557</td>\n",
       "      <td>0.106419</td>\n",
       "      <td>0.098918</td>\n",
       "      <td>0.124317</td>\n",
       "      <td>0.039546</td>\n",
       "      <td>0.059908</td>\n",
       "      <td>0.016611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74</td>\n",
       "      <td>Mount Greenwood</td>\n",
       "      <td>6945</td>\n",
       "      <td>97101</td>\n",
       "      <td>1099</td>\n",
       "      <td>3243</td>\n",
       "      <td>2603</td>\n",
       "      <td>272</td>\n",
       "      <td>172</td>\n",
       "      <td>115</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020878</td>\n",
       "      <td>0.040029</td>\n",
       "      <td>0.029230</td>\n",
       "      <td>0.058603</td>\n",
       "      <td>0.066811</td>\n",
       "      <td>0.161555</td>\n",
       "      <td>0.110727</td>\n",
       "      <td>0.109575</td>\n",
       "      <td>0.152628</td>\n",
       "      <td>0.112599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>Morgan Park</td>\n",
       "      <td>8020</td>\n",
       "      <td>63517</td>\n",
       "      <td>2732</td>\n",
       "      <td>3607</td>\n",
       "      <td>1681</td>\n",
       "      <td>684</td>\n",
       "      <td>197</td>\n",
       "      <td>298</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038404</td>\n",
       "      <td>0.050623</td>\n",
       "      <td>0.030424</td>\n",
       "      <td>0.048130</td>\n",
       "      <td>0.108728</td>\n",
       "      <td>0.104489</td>\n",
       "      <td>0.107357</td>\n",
       "      <td>0.066708</td>\n",
       "      <td>0.076185</td>\n",
       "      <td>0.066708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76</td>\n",
       "      <td>O'Hare</td>\n",
       "      <td>6460</td>\n",
       "      <td>60532</td>\n",
       "      <td>2073</td>\n",
       "      <td>3608</td>\n",
       "      <td>779</td>\n",
       "      <td>241</td>\n",
       "      <td>302</td>\n",
       "      <td>276</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019814</td>\n",
       "      <td>0.035604</td>\n",
       "      <td>0.032817</td>\n",
       "      <td>0.105728</td>\n",
       "      <td>0.111455</td>\n",
       "      <td>0.183901</td>\n",
       "      <td>0.089009</td>\n",
       "      <td>0.043344</td>\n",
       "      <td>0.051393</td>\n",
       "      <td>0.025851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77</td>\n",
       "      <td>Edgewater</td>\n",
       "      <td>28890</td>\n",
       "      <td>58037</td>\n",
       "      <td>10026</td>\n",
       "      <td>13064</td>\n",
       "      <td>5800</td>\n",
       "      <td>2299</td>\n",
       "      <td>1537</td>\n",
       "      <td>1126</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041468</td>\n",
       "      <td>0.047006</td>\n",
       "      <td>0.042818</td>\n",
       "      <td>0.076220</td>\n",
       "      <td>0.096054</td>\n",
       "      <td>0.104534</td>\n",
       "      <td>0.085566</td>\n",
       "      <td>0.052163</td>\n",
       "      <td>0.072170</td>\n",
       "      <td>0.076428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    community_area_number      community_name  total_population  \\\n",
       "0                       1         Rogers Park             24916   \n",
       "1                       2          West Ridge             25869   \n",
       "2                       3              Uptown             30970   \n",
       "3                       4      Lincoln Square             18481   \n",
       "4                       5        North Center             14237   \n",
       "..                    ...                 ...               ...   \n",
       "72                     73  Washington Heights              9331   \n",
       "73                     74     Mount Greenwood              6945   \n",
       "74                     75         Morgan Park              8020   \n",
       "75                     76              O'Hare              6460   \n",
       "76                     77           Edgewater             28890   \n",
       "\n",
       "    median_household_income  low_income  middle_income  high_income  \\\n",
       "0                     46164       10925          11113         2878   \n",
       "1                     57621        8498          12503         4868   \n",
       "2                     57576       11102          13694         6174   \n",
       "3                     79696        4218           8901         5362   \n",
       "4                    132628        1926           4837         7474   \n",
       "..                      ...         ...            ...          ...   \n",
       "72                    53182        3784           4464         1083   \n",
       "73                    97101        1099           3243         2603   \n",
       "74                    63517        2732           3607         1681   \n",
       "75                    60532        2073           3608          779   \n",
       "76                    58037       10026          13064         5800   \n",
       "\n",
       "    below_10_income  10_to_15_income  15_to_20_income  ...  \\\n",
       "0              2652             1836             1097  ...   \n",
       "1              1447             1039             1200  ...   \n",
       "2              3200             1838             1286  ...   \n",
       "3               768              329              417  ...   \n",
       "4               323              215              163  ...   \n",
       "..              ...              ...              ...  ...   \n",
       "72             1005              647              422  ...   \n",
       "73              272              172              115  ...   \n",
       "74              684              197              298  ...   \n",
       "75              241              302              276  ...   \n",
       "76             2299             1537             1126  ...   \n",
       "\n",
       "    35_to_40_income_percent  40_to_45_income_percent  45_to_50_income_percent  \\\n",
       "0                  0.055025                 0.053058                 0.034034   \n",
       "1                  0.044880                 0.050833                 0.043836   \n",
       "2                  0.037359                 0.036067                 0.038683   \n",
       "3                  0.034847                 0.040799                 0.045560   \n",
       "4                  0.032521                 0.011730                 0.010114   \n",
       "..                      ...                      ...                      ...   \n",
       "72                 0.039331                 0.033115                 0.031079   \n",
       "73                 0.020878                 0.040029                 0.029230   \n",
       "74                 0.038404                 0.050623                 0.030424   \n",
       "75                 0.019814                 0.035604                 0.032817   \n",
       "76                 0.041468                 0.047006                 0.042818   \n",
       "\n",
       "    50_to_60_income_percent  60_to_75_income_percent  \\\n",
       "0                  0.078584                 0.084524   \n",
       "1                  0.096795                 0.096641   \n",
       "2                  0.084759                 0.090701   \n",
       "3                  0.061090                 0.092636   \n",
       "4                  0.033153                 0.058510   \n",
       "..                      ...                      ...   \n",
       "72                 0.084557                 0.106419   \n",
       "73                 0.058603                 0.066811   \n",
       "74                 0.048130                 0.108728   \n",
       "75                 0.105728                 0.111455   \n",
       "76                 0.076220                 0.096054   \n",
       "\n",
       "    75_to_100_income_percent  100_to_125_income_percent  \\\n",
       "0                   0.123936                   0.071882   \n",
       "1                   0.116781                   0.078434   \n",
       "2                   0.118728                   0.073232   \n",
       "3                   0.134246                   0.107299   \n",
       "4                   0.119969                   0.106272   \n",
       "..                       ...                        ...   \n",
       "72                  0.098918                   0.124317   \n",
       "73                  0.161555                   0.110727   \n",
       "74                  0.104489                   0.107357   \n",
       "75                  0.183901                   0.089009   \n",
       "76                  0.104534                   0.085566   \n",
       "\n",
       "    125_to_150_income_percent  150_to_200_income_percent  \\\n",
       "0                    0.041018                   0.040616   \n",
       "1                    0.056322                   0.063358   \n",
       "2                    0.060510                   0.070520   \n",
       "3                    0.092365                   0.079866   \n",
       "4                    0.073119                   0.136124   \n",
       "..                        ...                        ...   \n",
       "72                   0.039546                   0.059908   \n",
       "73                   0.109575                   0.152628   \n",
       "74                   0.066708                   0.076185   \n",
       "75                   0.043344                   0.051393   \n",
       "76                   0.052163                   0.072170   \n",
       "\n",
       "    above_200_income_percent  \n",
       "0                   0.033874  \n",
       "1                   0.068499  \n",
       "2                   0.068324  \n",
       "3                   0.117905  \n",
       "4                   0.315727  \n",
       "..                       ...  \n",
       "72                  0.016611  \n",
       "73                  0.112599  \n",
       "74                  0.066708  \n",
       "75                  0.025851  \n",
       "76                  0.076428  \n",
       "\n",
       "[77 rows x 42 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Include cleaning up of income dataset here \n",
    "df_income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0f63e411",
   "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>community_area_number</th>\n",
       "      <th>low_income</th>\n",
       "      <th>middle_income</th>\n",
       "      <th>high_income</th>\n",
       "      <th>median_household_income</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>10925</td>\n",
       "      <td>11113</td>\n",
       "      <td>2878</td>\n",
       "      <td>46164</td>\n",
       "      <td>0.438473</td>\n",
       "      <td>0.446019</td>\n",
       "      <td>0.115508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>8498</td>\n",
       "      <td>12503</td>\n",
       "      <td>4868</td>\n",
       "      <td>57621</td>\n",
       "      <td>0.328501</td>\n",
       "      <td>0.483320</td>\n",
       "      <td>0.188179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>11102</td>\n",
       "      <td>13694</td>\n",
       "      <td>6174</td>\n",
       "      <td>57576</td>\n",
       "      <td>0.358476</td>\n",
       "      <td>0.442170</td>\n",
       "      <td>0.199354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4218</td>\n",
       "      <td>8901</td>\n",
       "      <td>5362</td>\n",
       "      <td>79696</td>\n",
       "      <td>0.228234</td>\n",
       "      <td>0.481630</td>\n",
       "      <td>0.290136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1926</td>\n",
       "      <td>4837</td>\n",
       "      <td>7474</td>\n",
       "      <td>132628</td>\n",
       "      <td>0.135281</td>\n",
       "      <td>0.339749</td>\n",
       "      <td>0.524970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>3784</td>\n",
       "      <td>4464</td>\n",
       "      <td>1083</td>\n",
       "      <td>53182</td>\n",
       "      <td>0.405530</td>\n",
       "      <td>0.478405</td>\n",
       "      <td>0.116065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74</td>\n",
       "      <td>1099</td>\n",
       "      <td>3243</td>\n",
       "      <td>2603</td>\n",
       "      <td>97101</td>\n",
       "      <td>0.158243</td>\n",
       "      <td>0.466955</td>\n",
       "      <td>0.374802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>2732</td>\n",
       "      <td>3607</td>\n",
       "      <td>1681</td>\n",
       "      <td>63517</td>\n",
       "      <td>0.340648</td>\n",
       "      <td>0.449751</td>\n",
       "      <td>0.209601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76</td>\n",
       "      <td>2073</td>\n",
       "      <td>3608</td>\n",
       "      <td>779</td>\n",
       "      <td>60532</td>\n",
       "      <td>0.320898</td>\n",
       "      <td>0.558514</td>\n",
       "      <td>0.120588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77</td>\n",
       "      <td>10026</td>\n",
       "      <td>13064</td>\n",
       "      <td>5800</td>\n",
       "      <td>58037</td>\n",
       "      <td>0.347040</td>\n",
       "      <td>0.452198</td>\n",
       "      <td>0.200762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    community_area_number  low_income  middle_income  high_income  \\\n",
       "0                       1       10925          11113         2878   \n",
       "1                       2        8498          12503         4868   \n",
       "2                       3       11102          13694         6174   \n",
       "3                       4        4218           8901         5362   \n",
       "4                       5        1926           4837         7474   \n",
       "..                    ...         ...            ...          ...   \n",
       "72                     73        3784           4464         1083   \n",
       "73                     74        1099           3243         2603   \n",
       "74                     75        2732           3607         1681   \n",
       "75                     76        2073           3608          779   \n",
       "76                     77       10026          13064         5800   \n",
       "\n",
       "    median_household_income  low_income_percent  middle_income_percent  \\\n",
       "0                     46164            0.438473               0.446019   \n",
       "1                     57621            0.328501               0.483320   \n",
       "2                     57576            0.358476               0.442170   \n",
       "3                     79696            0.228234               0.481630   \n",
       "4                    132628            0.135281               0.339749   \n",
       "..                      ...                 ...                    ...   \n",
       "72                    53182            0.405530               0.478405   \n",
       "73                    97101            0.158243               0.466955   \n",
       "74                    63517            0.340648               0.449751   \n",
       "75                    60532            0.320898               0.558514   \n",
       "76                    58037            0.347040               0.452198   \n",
       "\n",
       "    high_income_percent  \n",
       "0              0.115508  \n",
       "1              0.188179  \n",
       "2              0.199354  \n",
       "3              0.290136  \n",
       "4              0.524970  \n",
       "..                  ...  \n",
       "72             0.116065  \n",
       "73             0.374802  \n",
       "74             0.209601  \n",
       "75             0.120588  \n",
       "76             0.200762  \n",
       "\n",
       "[77 rows x 8 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income = df_income[['community_area_number', 'low_income', 'middle_income', 'high_income', 'median_household_income',\n",
    "                       'low_income_percent', 'middle_income_percent', 'high_income_percent']]\n",
    "df_income"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbd4b830",
   "metadata": {},
   "source": [
    "## EDA and Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "908a15c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date range of trips: 01/01/2023 01:00:00 PM to 12/31/2022 12:00:00 PM\n"
     ]
    }
   ],
   "source": [
    "min_start_date = df_e_scooter_trips['Start Time'].min()\n",
    "max_end_date = df_e_scooter_trips['End Time'].max()\n",
    "\n",
    "print(f\"Date range of trips: {min_start_date} to {max_end_date}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c8c75579",
   "metadata": {},
   "outputs": [],
   "source": [
    "vendor_counts = df_e_scooter_trips['Vendor'].value_counts(normalize = True) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "69d16d0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate the pie chart\n",
    "plt.figure(figsize=(10, 7))  # Set the figure size for better readability\n",
    "plt.pie(vendor_counts, labels=vendor_counts.index, autopct='%1.1f%%', startangle=140, textprops={'fontsize': 14})\n",
    "plt.title('Distribution of Trips by Vendor', fontsize = 15)\n",
    "plt.axis('equal')  # Equal aspect ratio ensures the pie chart is circular\n",
    "\n",
    "# Display the chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "365721a1",
   "metadata": {},
   "source": [
    "### Vendor Distribution Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d529a958",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lime    52.110969\n",
      "Lyft    38.322075\n",
      "Spin     6.233664\n",
      "Link     3.333292\n",
      "Name: Vendor, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate the percentage of trips by each vendor\n",
    "vendor_distribution = df_e_scooter_trips['Vendor'].value_counts(normalize = True) * 100\n",
    "print(vendor_distribution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c8d9bdda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Near North Side    479130\n",
       "Near West Side     409160\n",
       "Loop               353414\n",
       "West Town          288500\n",
       "Lincoln Park       265325\n",
       "                    ...  \n",
       "Forest Glen          1788\n",
       "Burnside             1482\n",
       "Edison Park          1051\n",
       "Mount Greenwood       643\n",
       "Ohare                 496\n",
       "Name: Start Community Area Name, Length: 77, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_e_scooter_trips['Start Community Area Name'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d560b88a",
   "metadata": {},
   "source": [
    "We need to prepare data from the df_e_scooter_trips and df_weather datasets. First, we extract relevant features such as the hour of the day and day of the week from the timestamps, and merge the e-scooter data with weather conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3a100d35",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count variable added\n",
    "df_e_scooter_trips['eda_count'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d0183bb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correct datetime format to match the date strings\n",
    "datetime_format = '%m/%d/%Y %I:%M:%S %p'  # Adjusting to match \"MM/DD/YYYY HH:MM:SS AM/PM\"\n",
    "\n",
    "# Convert 'Start Time' and 'End Time' to datetime using the correct format\n",
    "df_e_scooter_trips['Start Time'] = pd.to_datetime(df_e_scooter_trips['Start Time'], format=datetime_format)\n",
    "df_e_scooter_trips['End Time'] = pd.to_datetime(df_e_scooter_trips['End Time'], format=datetime_format)\n",
    "\n",
    "# Extract hour of day and day of the week\n",
    "df_e_scooter_trips['hour_of_day'] = df_e_scooter_trips['Start Time'].dt.hour\n",
    "df_e_scooter_trips['day_of_week'] = df_e_scooter_trips['Start Time'].dt.dayofweek\n",
    "df_e_scooter_trips['is_weekend'] = df_e_scooter_trips['day_of_week'].apply(lambda x: 1 if x > 4 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "88460289",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure it's datetime format\n",
    "df_weather['datetime'] = pd.to_datetime(df_weather['datetime'])\n",
    "\n",
    "# Prepare df_e_scooter_trips for merging\n",
    "df_e_scooter_trips['date_for_merge'] = df_e_scooter_trips['Start Time'].dt.normalize()\n",
    "\n",
    "# Perform the merge\n",
    "df_e_scooter_trips = pd.merge(df_e_scooter_trips, df_weather, left_on='date_for_merge', right_on='datetime', how='left')\n",
    "\n",
    "# Drop the temporary merging column\n",
    "df_e_scooter_trips.drop('date_for_merge', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e49283d8",
   "metadata": {},
   "source": [
    "### Type of riders partition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bada52e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type_of_rider</th>\n",
       "      <th>eda_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Morning Commutes</td>\n",
       "      <td>564087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Late Night Riders</td>\n",
       "      <td>587900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lunchtime Travelers</td>\n",
       "      <td>1068504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afternoon Riders</td>\n",
       "      <td>1576550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         type_of_rider  eda_count\n",
       "3     Morning Commutes     564087\n",
       "1    Late Night Riders     587900\n",
       "2  Lunchtime Travelers    1068504\n",
       "0     Afternoon Riders    1576550"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from itertools import chain\n",
    "\n",
    "# Type of riders\n",
    "# Dictionary for time segments\n",
    "time_segments = {\n",
    "    'Morning Commutes': range(5, 11),  # 5 AM to 10 AM\n",
    "    'Lunchtime Travelers': range(11, 16),  # 10 AM to 3 PM\n",
    "    'Afternoon Riders': range(16, 21),  # 3 PM to 8 PM\n",
    "    'Late Night Riders': list(chain(range(21, 24), range(0, 1)))  # 8 PM to Midnight to 1 AM\n",
    "}\n",
    "\n",
    "# Lambda function to segment the times\n",
    "df_e_scooter_trips['type_of_rider'] = df_e_scooter_trips['hour_of_day'].apply(lambda x: next((segment for segment, hours in time_segments.items() if x in hours), None))\n",
    "\n",
    "# Group by segment and sum the counts\n",
    "segmented_data = df_e_scooter_trips.groupby('type_of_rider')['eda_count'].sum().reset_index()\n",
    "sorted_segmented_data = segmented_data.sort_values(by = 'eda_count')\n",
    "sorted_segmented_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d9111ad5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating graph showing the number of electric scooter trips by hour in the day\n",
    "plt.figure(figsize = (12, 6))\n",
    "sns.set(style = 'whitegrid')\n",
    "\n",
    "# Plot\n",
    "eda_data_plot = sns.barplot(x = 'type_of_rider', y = 'eda_count', data = sorted_segmented_data)\n",
    "\n",
    "# Labels and titles\n",
    "plt.title('Trips by Type of Rider', fontsize = 20)\n",
    "plt.xlabel('Type of Rider', fontsize = 15)\n",
    "plt.ylabel('Number of Trips', fontsize = 15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5e0fe863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>Trip Distance</th>\n",
       "      <th>Trip Duration</th>\n",
       "      <th>Start Community Area Number</th>\n",
       "      <th>End Community Area Number</th>\n",
       "      <th>Start Centroid Latitude</th>\n",
       "      <th>Start Centroid Longitude</th>\n",
       "      <th>End Centroid Latitude</th>\n",
       "      <th>End Centroid Longitude</th>\n",
       "      <th>eda_count</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>is_weekend</th>\n",
       "      <th>temp</th>\n",
       "      <th>precip</th>\n",
       "      <th>snow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>132616397</td>\n",
       "      <td>47345621</td>\n",
       "      <td>1291093.0</td>\n",
       "      <td>1285752.0</td>\n",
       "      <td>2.197761e+06</td>\n",
       "      <td>-4.599212e+06</td>\n",
       "      <td>2.198108e+06</td>\n",
       "      <td>-4.599992e+06</td>\n",
       "      <td>52513</td>\n",
       "      <td>191371</td>\n",
       "      <td>23334</td>\n",
       "      <td>3653104.1</td>\n",
       "      <td>6192.805</td>\n",
       "      <td>168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>278601</td>\n",
       "      <td>123792</td>\n",
       "      <td>2558.0</td>\n",
       "      <td>2559.0</td>\n",
       "      <td>4.188226e+03</td>\n",
       "      <td>-8.768162e+03</td>\n",
       "      <td>4.187877e+03</td>\n",
       "      <td>-8.767900e+03</td>\n",
       "      <td>100</td>\n",
       "      <td>306</td>\n",
       "      <td>33</td>\n",
       "      <td>6761.8</td>\n",
       "      <td>4.506</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2411890</td>\n",
       "      <td>71043</td>\n",
       "      <td>1103.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1.549065e+03</td>\n",
       "      <td>-3.244520e+03</td>\n",
       "      <td>1.549037e+03</td>\n",
       "      <td>-3.244431e+03</td>\n",
       "      <td>38</td>\n",
       "      <td>145</td>\n",
       "      <td>19</td>\n",
       "      <td>2566.9</td>\n",
       "      <td>3.662</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>74786</td>\n",
       "      <td>35927</td>\n",
       "      <td>787.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>1.088637e+03</td>\n",
       "      <td>-2.280467e+03</td>\n",
       "      <td>1.088677e+03</td>\n",
       "      <td>-2.280416e+03</td>\n",
       "      <td>26</td>\n",
       "      <td>111</td>\n",
       "      <td>15</td>\n",
       "      <td>1742.4</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>123865</td>\n",
       "      <td>58085</td>\n",
       "      <td>787.0</td>\n",
       "      <td>814.0</td>\n",
       "      <td>1.340901e+03</td>\n",
       "      <td>-2.806448e+03</td>\n",
       "      <td>1.340906e+03</td>\n",
       "      <td>-2.806750e+03</td>\n",
       "      <td>32</td>\n",
       "      <td>102</td>\n",
       "      <td>11</td>\n",
       "      <td>2163.2</td>\n",
       "      <td>0.542</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hour_of_day  Trip Distance  Trip Duration  Start Community Area Number  \\\n",
       "0            0      132616397       47345621                    1291093.0   \n",
       "1            1         278601         123792                       2558.0   \n",
       "2            2        2411890          71043                       1103.0   \n",
       "3            3          74786          35927                        787.0   \n",
       "4            4         123865          58085                        787.0   \n",
       "\n",
       "   End Community Area Number  Start Centroid Latitude  \\\n",
       "0                  1285752.0             2.197761e+06   \n",
       "1                     2559.0             4.188226e+03   \n",
       "2                     1125.0             1.549065e+03   \n",
       "3                      767.0             1.088637e+03   \n",
       "4                      814.0             1.340901e+03   \n",
       "\n",
       "   Start Centroid Longitude  End Centroid Latitude  End Centroid Longitude  \\\n",
       "0             -4.599212e+06           2.198108e+06           -4.599992e+06   \n",
       "1             -8.768162e+03           4.187877e+03           -8.767900e+03   \n",
       "2             -3.244520e+03           1.549037e+03           -3.244431e+03   \n",
       "3             -2.280467e+03           1.088677e+03           -2.280416e+03   \n",
       "4             -2.806448e+03           1.340906e+03           -2.806750e+03   \n",
       "\n",
       "   eda_count  day_of_week  is_weekend       temp    precip   snow  \n",
       "0      52513       191371       23334  3653104.1  6192.805  168.0  \n",
       "1        100          306          33     6761.8     4.506    0.0  \n",
       "2         38          145          19     2566.9     3.662    0.0  \n",
       "3         26          111          15     1742.4     0.847    0.0  \n",
       "4         32          102          11     2163.2     0.542    0.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_e_scooter_trips_plot = df_e_scooter_trips.groupby('hour_of_day').sum().reset_index()\n",
    "df_e_scooter_trips_plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "837e39d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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99NBDtGjRgm+//da8PE7LYeROpsAuchcKCgqidevWJd5qhsI/hADfffddsXWjn3/+eanX27aUyMjIYteb3rdvH3FxcTRu3JgWLVoAmC9dN3PmzGInrGVmZvLuu+/yr3/9q9iYb0SvXr0wGo188MEHxULZpUuXzJ/IaKuBYPny5eZ3I6DwcnkbNmygefPmNGvWDAcHBzZu3Mj7779fInAWLU944IEH/rJObm4u8+bNM4cnk8nEP/7xDy5dusTzzz8PFC5zaNeuHTt27Chxne2oqCiWLFnCd999V+EzoTf6/D700EOcPn2aY8eOmdvOnDlDVFRUhY35RhiNhW+Y3+ynnVbE83b58mXefvtt0tLS6N+/P3Xr1gX+txTo6hNLoXD5zdy5cwFK/eyG3r17k5KSwvLly2nQoMFNXUpT5HahJTEidyGDwcD06dPp3bt3iT/wjz76KM2bN+fHH38kKCiIdu3a8euvv/L999/TsmVLDhw4YJUxGY1GevXqRY8ePTh//jxxcXFUrVqVWbNmmbdp3749wcHBREZG4ufnh4+PDw4ODmzevJmzZ8/Sv39/81UlbpSHhwdvv/02M2bMIDAwkOeee47q1auzY8cOTp06hZ+f301f8s/aMjIyCAwMpEuXLmRmZrJp0yaqVq3Ke++9Z95m/PjxjBgxgsDAQLp168Y999xDfHw8//3vf3n88cdLXR51LXt7e6Kjozl8+LD5Z+HHH3/kiSeeKHY1oGnTpjFw4EDGjBmDt7c3jRs35sSJE2zbtg1nZ+dS39mxtht9fl944QXee+89goOD8ff3Jycnh9jYWJo0aXLT7+JYU9G68tjYWKpXr05gYOANvyNkqectPT292ImrOTk5nDt3jl27dnH+/Hk6duzI22+/bb7/wQcfpEWLFuzbt4+goCDatGnDhQsX2Lx5Mzk5OVSrVo0LFy6UqOPn58esWbM4c+ZMiUtZitxpFNhF7lIPP/www4YNY8mSJSXu++c//8k//vEPvv32W44ePYqnpyeffvopsbGxVgvsM2fOJDo6mnXr1pGXl8dTTz3Fm2++WWJddVhYGI899hj//ve/+fLLL7Gzs6Nhw4aMGjXKPAN/swYPHsyDDz7I8uXL+frrrzGZTDRq1Ijhw4fTt2/fW+rbmsLDw9mzZw9fffUVVapUoVOnTowZM6bYJfKeffZZli9fzr/+9S++/fZb0tPTeeCBBxgxYgSvvvpqua4UU7VqVVasWMG7777Lv//9b1xcXBg5ciTDhw83z/BC4ez0unXrWLp0Kdu3b+f777/H1dWVXr16lbisYkW6ked30KBB5Ofn8/nnn/Of//yHunXrMnz4cDp06ECfPn0qZfzX4+7uztixY/n0009ZuXIljRo1uuHAbqnnLSMjg8WLF5tvG41G7rnnHpo1a4a/vz+9e/cu9vNWpUoVli5dyrx589i1axc///wzbm5ueHt78/rrr/OPf/yDzZs3l7gWvbOzMx06dGDHjh02++6XiKUYTLfy0W4iIlJpFi1axOLFi4t9aJW1dO7cmfT0dJucXZa7U0FBAZ06dcLd3Z3PP/+8socjYlVawy4iIiK3nS+++IJz587xwgsvVPZQRKxOS2JERETktjF27FiOHj3KiRMneOihh/Dz86vsIYlYnWbYRURE5LZx3333kZiYSIsWLVi6dKnFP5NBxBZpDbuIiIiIiA3TkpgyFBQUkJWVhb29vUU+GlpEREREpDQmk4nc3Fxq1KhR6lW7FNjLkJWVxa+//lrZwxARERGRu0STJk2oWbNmiXYF9jIUrYlr0qRJhX8in4iIiIjcPXJycvj111/LPCdDgb0MRctgHBwccHR0rOTRiIiIiMidrqxl2LpKjIiIiIiIDVNgFxERERGxYQrsIiIiIiI2TIFdRERERMSGKbCLiIiIiNgwBXYRERERERumwC4iIiIiYsMU2EVEREREbJgCu4iIiIiIDVNgFxERERGxYQrsIiIiIiI2TIFdRERERMSGVWhgf//99+nRowd+fn588sknAEycOBFfX1969epFr169+OabbwA4fPgwffr0oWvXrkyePJm8vDwAEhMTGThwIN26deP1118nKysLgPT0dIYNG0b37t0ZOHAgKSkpAOTk5BASEkL37t0JDAzk+PHjFbnLIiIiIiK3pMIC+549e/jvf//Ll19+ydq1a4mMjOT3338nPj6eFStWEB0dTXR0NF26dAEgJCSE8PBwNm3ahMlkYvXq1QBMnTqVoKAg4uLi8PT0ZOnSpQAsWLAALy8vYmNj6devHzNmzAAgMjKSatWqERsby6RJk5g4cWJF7bKIiIjcgLx8023Vr0hFMVZUoccff5zPPvsMo9FIUlIS+fn5VK1alcTERCZNmkRSUhJdunRh5MiRnD17litXrtCqVSsA+vTpw8KFC+nXrx979+5lyZIl5vZBgwYREhLCtm3bWLlyJQD+/v5MmzaN3Nxctm3bxpgxYwBo164daWlpJCYm8sADD1TUrouIiEg5GO0MfLQu2eL9vtLH1eJ9ilSkCl0SY29vz8KFC/Hz86NDhw7k5eXRvn17Zs6cyerVq9m3bx9r1qwhOTkZFxcX8+NcXFxISkriwoULODk5YTQai7UDxR5jNBpxcnIiLS2t1L7OnTtXgXstIiIiInLzKmyGvcjo0aN59dVXee211/j+++/Ns+UAwcHBREVF0ahRIwwGg7ndZDJhMBjM/1/t2ttXP6ZKlSolHlPUXl7x8fHl3lZERERuXtu2ba3W9/79+63Wt4i1VVhgP378ODk5OTzyyCNUq1YNX19fYmJicHZ2pmvXrkBhmDYajbi5uZlPGgVITU3F1dWV2rVrk5GRQX5+PnZ2dqSkpODqWvg2l6urK6mpqbi5uZGXl0dWVhbOzs7UqVOH5ORk6tevX6yv8vL09MTR0dGC3wkRERGpaNZ8MSByq7Kzs687SVxhS2JOnz5NWFgYOTk55OTksGXLFtq1a8fMmTO5ePEiubm5rFq1ii5duuDu7o6jo6P51XB0dDTe3t7Y29vj5eVFTEwMAFFRUXh7ewPg4+NDVFQUADExMXh5eWFvb4+Pjw/R0dEA7Nu3D0dHR61fFxEREZHbRoXNsPv4+HDw4EF69+6NnZ0dvr6+jBw5knvvvZcBAwaQl5eHr68v/v7+AERERBAWFkZmZibNmzdn8ODBAEyZMoXQ0FCWLVtG3bp1mTdvHgBjxowhNDQUPz8/atasSUREBFC4zCY8PBw/Pz8cHByYO3duRe2yiIiI2Kj8fBN2dqUvq7XlvuXuZDCZTLrWUSmK3prQkhgREZGKU5FXiYldlWrxWgDdX7zfKv3Kneuvcqc+6VRERERExIYpsIuIiIiI2DAFdhERERERG6bALiIiIiJiwxTYRURERERsmAK7iIiIiIgNU2AXEREREbFhCuwiIiIiIjZMgV1ERERExIYpsIuIiIiI2DAFdhERERERG6bALiIiIiJiwxTYRURERERsmAK7iIiIiIgNU2AXEREREbFhCuwiIiIiIjZMgV1ERERExIYpsIuIiIiI2DAFdhERERERG6bALiIiIiJiwxTYRURERERsmAK7iIiIiIgNU2AXEREREbFhCuwiIiJSqtx8023Vr8idyljZAxARERHbZG9nYPT6Uxbvd2Ggh8X7FLmTaYZdRERERMSGKbCLiIiIiNgwBXYRERERERumwC4iIiIiYsMU2EVEREREbJgCu4iIiIiIDVNgFxERERGxYQrsIiIiIiI2TIFdRERERMSGKbCLiIiIWFlBnum27Ftsg7Eii73//vts2rQJg8FA3759GTp0KLt372bWrFlkZ2fTvXt3xo0bB8Dhw4eZPHkyWVlZeHl5MXXqVIxGI4mJiYSEhHD+/HkaNmxIREQENWrUID09nbfeeotTp05Ru3ZtFixYgIuLCzk5OUyePJn4+HiqVq1KREQEjRo1qsjdFhERkbtcFaOBvZ8kW6XvdkNdrdKv2I4Km2Hfs2cP//3vf/nyyy9Zu3YtkZGRHDlyhEmTJrF06VJiYmKIj49n+/btAISEhBAeHs6mTZswmUysXr0agKlTpxIUFERcXByenp4sXboUgAULFuDl5UVsbCz9+vVjxowZAERGRlKtWjViY2OZNGkSEydOrKhdFhERERG5ZRUW2B9//HE+++wzjEYj58+fJz8/n/T0dBo0aICHhwdGo5GAgADi4uI4c+YMV65coVWrVgD06dOHuLg4cnNz2bt3L127di3WDrBt2zYCAgIA8Pf3Z8eOHeTm5rJt2zZ69uwJQLt27UhLSyMxMbGidltERERE5JZU6Bp2e3t7Fi5ciJ+fHx06dCA5ORkXFxfz/a6uriQlJZVod3FxISkpiQsXLuDk5ITRaCzWDhR7jNFoxMnJibS0tFL7OnfuXEXsroiIiIjILavQNewAo0eP5tVXX+W1114jISEBg8Fgvs9kMmEwGCgoKCi1vej/q117++rHVKlSpcRjitrLKz4+vtzbioiI3Enatm1rtb73799fqfWsWcsW6smdpcIC+/Hjx8nJyeGRRx6hWrVq+Pr6EhcXh52dnXmblJQUXF1dcXNzIyUlxdyempqKq6srtWvXJiMjg/z8fOzs7MzbQ+HsfGpqKm5ubuTl5ZGVlYWzszN16tQhOTmZ+vXrF+urvDw9PXF0dLTQd0FERETA+gFW9eR2kp2dfd1J4gpbEnP69GnCwsLIyckhJyeHLVu20L9/f06cOMHJkyfJz89n48aNeHt74+7ujqOjo/nVYnR0NN7e3tjb2+Pl5UVMTAwAUVFReHt7A+Dj40NUVBQAMTExeHl5YW9vj4+PD9HR0QDs27cPR0dHHnjggYrabRERERGRW1JhM+w+Pj4cPHiQ3r17Y2dnh6+vL35+ftSuXZtRo0aRnZ2Nj48P3bp1AyAiIoKwsDAyMzNp3rw5gwcPBmDKlCmEhoaybNky6taty7x58wAYM2YMoaGh+Pn5UbNmTSIiIgAIDg4mPDwcPz8/HBwcmDt3bkXtsoiIiIjILTOYTCZdbb8URW9NaEmMiIjczUavP2XxPhcGepR530frLH+t8lf6lL4UNnZVqsVrAXR/8f5S23UddinLX+VOfdKpiIiIiIgNU2AXEREREbFhCuwiIiIiIjZMgV1ERERExIYpsIuIiIiI2DAFdhERERERG6bALiIiIiJiwxTYRURERERsmAK7iIiIiIgNU2AXEREREbFhCuwiIiIiIjZMgV1ERERExIYpsIuIiIiI2DAFdhERERERG6bALiIiIiJiwxTYRURERERsmAK7iIiIiIgNU2AXEREREbFhCuwiIiIiIjZMgV1ERERExIYpsIuIiIiI2DAFdhERkdtETn7BbdWviFiGsbIHICIiIuXjYFeFvmv/z+L9rnm+jcX7FBHL0Qy7iIiIiIgNU2AXEREREbFhCuwiIiIiIjZMgV1ERERExIYpsIuIiIiI2DAFdhERERERG6bALiIiIiJiwxTYRURERERsmAK7iIiIiIgNU2AXEREREbFhCuwiIiIiIjZMgV1ERERExIZVaGBfvHgxfn5++Pn5MXfuXAAmTpyIr68vvXr1olevXnzzzTcAHD58mD59+tC1a1cmT55MXl4eAImJiQwcOJBu3brx+uuvk5WVBUB6ejrDhg2je/fuDBw4kJSUFABycnIICQmhe/fuBAYGcvz48YrcZRERERGRW1JhgX337t3s3LmT9evXExUVxc8//8w333xDfHw8K1asIDo6mujoaLp06QJASEgI4eHhbNq0CZPJxOrVqwGYOnUqQUFBxMXF4enpydKlSwFYsGABXl5exMbG0q9fP2bMmAFAZGQk1apVIzY2lkmTJjFx4sSK2mURERERkVtWYYHdxcWF0NBQHBwcsLe3p1GjRiQmJpKYmMikSZMICAhg4cKFFBQUcObMGa5cuUKrVq0A6NOnD3FxceTm5rJ37166du1arB1g27ZtBAQEAODv78+OHTvIzc1l27Zt9OzZE4B27dqRlpZGYmJiRe22iIiISIUryDPdln1L6YwVVahx48bmrxMSEoiNjWXlypXs2bOHKVOmULNmTYYPH86aNWto3LgxLi4u5u1dXFxISkriwoULODk5YTQai7UDJCcnmx9jNBpxcnIiLS2tWHvRY86dO8cDDzxQEbstIiIiUuGqGA0kLDhnlb4fHOtmlX6lbBUW2IscO3aM4cOHM2HCBB566CGWLFlivi84OJioqCgaNWqEwWAwt5tMJgwGg/n/q117++rHVKlSpcRjitrLKz4+vtzbioiIWFPbtm2t1vf+/fvvqnrWrHU31hPrqtDAvn//fkaPHs2kSZPw8/Pj6NGjJCQkmJe4mEwmjEYjbm5u5pNGAVJTU3F1daV27dpkZGSQn5+PnZ0dKSkpuLq6AuDq6kpqaipubm7k5eWRlZWFs7MzderUITk5mfr16xfrq7w8PT1xdHS04HdBRETE9lg74Kme6knZsrOzrztJXGFr2M+ePcuIESOIiIjAz88PKAzoM2fO5OLFi+Tm5rJq1Sq6dOmCu7s7jo6O5ldv0dHReHt7Y29vj5eXFzExMQBERUXh7e0NgI+PD1FRUQDExMTg5eWFvb09Pj4+REdHA7Bv3z4cHR21HEZEREREbhsVNsO+fPlysrOzmT17trmtf//+DBs2jAEDBpCXl4evry/+/v4AREREEBYWRmZmJs2bN2fw4MEATJkyhdDQUJYtW0bdunWZN28eAGPGjCE0NBQ/Pz9q1qxJREQEULjMJjw8HD8/PxwcHMyXkxQRERERuR3cVGBPS0tjz549NG/eHA8Pj3I9JiwsjLCwsFLvGzhwYIm2Zs2asWbNmhLt7u7uREZGlmh3dnbmgw8+KNHu6OjInDlzyjVGERERERFbU64lMUeOHMHX15e9e/eSkZFBv379GDt2LD169GDnzp3WHqOIiIiIyF2rXIF9zpw5NGnShEaNGhEVFcXly5fZvXs3r7/+OgsWLLDyEEVERERE7l7lCuw//fQTb731FrVr12bHjh0888wz1K5dm549e3Ls2DFrj1FERERE5K5VrsDu4OCAyWQiJyeHvXv38tRTTwGFa9lr1Khh1QGKiIiIiNzNynXS6eOPP87cuXOpVasWUHgJxSNHjjBjxgw6dOhg1QGKiIiIiNzNyjXD/u6772I0Gjly5Ahz5szBycmJ6OhoqlatyqRJk6w9RhERERGRu1a5Ztjvu+8+Fi1aVKztrbfews7OziqDEhERERGRQuW+DvuPP/7IihUr+PXXX6lSpQqPPvooL730Es2aNbPm+ERERGxWTn4+DlaYvLJWvyJyeypXYP/mm28YPXo0bdu2xdvbm4KCAn766Sf69u3LRx99RPv27a09ThEREZvjYGdH7zVbLN5vVN9nLd6niNy+yhXY33//fUaMGMHIkSOLtc+fP5+///3vrF271iqDExERERG525XrpNNTp07h7+9foj0wMJDffvvN4oMSEREREZFC5QrsrVu3ZvPmzSXa9+zZQ4sWLSw+KBERERERKVSuJTFPPfUUixYt4tChQ7Rr1w6j0cjPP/9MdHQ0vXv35oMPPjBv+9prr1ltsCIiIiIid5tyBfZ///vf3HfffRw6dIhDhw6Z2++//3527txpvm0wGBTYRUREREQsqFyBfevWrdYeh4iIiIiIlKLMwJ6UlESdOnXMX19P0XYiIiIiImJZZQb2Z555hp07d3Lffffh4+ODwWAosY3JZMJgMHD48GGrDlJERERE5G5VZmD/9NNPueeeewD47LPPKmxAIiIiIiLyP2UG9scff9z89ZIlSwgLC6Nx48YVMigRERERESlUruuwHzlyhKpVq1p7LCIiIiIico1yXSVmyJAhhIeH8+qrr1KvXj0cHR2L3a+TTkVERERErKNcgX3ZsmXk5OTw/fffFzv5VCedioiIiIhYV7kC+0cffWTtcYiIiIiISCnKDOyPPPKI+bKOV5+AKiIiIiIiFafMk05NJlNFjkNEROSW5eTn31b9ioiUR7mWxIiIiNwOHOzsCFgTZfF+N/TtbfE+RUTK67qB/euvv8bJyekvOwkICLDYgERERERE5H+uG9inTp36lx0YDAYFdhERERERK7luYN+1axf33XdfRY1FRERERESuUeZJp1dfb11ERERERCqHrhIjIiIiImLDygzsI0eOpHr16hU5FhERERERuUaZa9hHjhxZkeMQERERkduUKa8Ag7HMeWCb6/d2o+uwi4iIiMgtMRircO4fRyzer9ubzSze5+1IL1lERERERGxYmYF97ty5XLx4EYDExESLnIS6ePFi/Pz88PPzY+7cuQDs3r2bgIAAfH19mT9/vnnbw4cP06dPH7p27crkyZPJy8szj2XgwIF069aN119/naysLADS09MZNmwY3bt3Z+DAgaSkpACQk5NDSEgI3bt3JzAwkOPHj9/yfoiIiIiIVJQyA/uKFSvIyMgA4Nlnn+XChQu3VGj37t3s3LmT9evXExUVxc8//8zGjRuZNGkSS5cuJSYmhvj4eLZv3w5ASEgI4eHhbNq0CZPJxOrVq4HCD3MKCgoiLi4OT09Pli5dCsCCBQvw8vIiNjaWfv36MWPGDAAiIyOpVq0asbGxTJo0iYkTJ97SfoiIiIiIVKQy17DXq1ePkSNH8sgjj2AymZg+fTqOjo6lbjtr1qy/LOTi4kJoaCgODg4ANGrUiISEBBo0aICHhwcAAQEBxMXF8fDDD3PlyhVatWoFQJ8+fVi4cCH9+vVj7969LFmyxNw+aNAgQkJC2LZtGytXrgTA39+fadOmkZuby7Zt2xgzZgwA7dq1Iy0tjcTERB544IFyfotERERERCpPmTPsERERNGjQgKSkJAwGA8nJyZw7d67Uf+XRuHFjcwBPSEggNjYWg8GAi4uLeRtXV1eSkpJITk4u1u7i4kJSUhIXLlzAyckJo9FYrB0o9hij0YiTkxNpaWml9lXeMYuIiIiIVLYyZ9gfffRR3n//fQA6d+7MokWLuPfee2+54LFjxxg+fDgTJkzAzs6OhIQE830mkwmDwUBBQUGxT1otai/6/2plfSKryWSiSpUqJR5T1F5e8fHx5d5WREQqV9u2ba3W9/79+1XvDqpnzVqqZ91ad6NyXdZx69atmEwmtm/fzrFjxzAajTRu3Jj27dtjZ2dX7mL79+9n9OjRTJo0CT8/P/bs2WM+ORQgJSUFV1dX3NzcirWnpqbi6upK7dq1ycjIID8/Hzs7O/P2UDg7n5qaipubG3l5eWRlZeHs7EydOnVITk6mfv36xfoqL09PzzKXAomIyN3D2gFI9VRP9Sq/VmXJzs6+7iRxuaaa//zzT55//nmGDx/O8uXLWbZsGX/729/o168ff/75Z7kGcvbsWUaMGEFERAR+fn4AtGzZkhMnTnDy5Eny8/PZuHEj3t7euLu74+joaH5FFR0djbe3N/b29nh5eRETEwNAVFQU3t7eAPj4+BAVFQVATEwMXl5e2Nvb4+PjQ3R0NAD79u3D0dFR69dFRERE5LZRrhn2WbNmkZ+fz1dffUWjRo0A+O233wgJCSEiIoLp06f/ZR/Lly8nOzub2bNnm9v69+/P7NmzGTVqFNnZ2fj4+NCtWzegcA19WFgYmZmZNG/enMGDBwMwZcoUQkNDWbZsGXXr1mXevHkAjBkzhtDQUPz8/KhZsyYREREABAcHEx4ejp+fHw4ODubLSYqIiIiI3A7KFdi3bdvG0qVLzWEd4OGHH2by5MmMGjWqXIE9LCyMsLCwUu/78ssvS7Q1a9aMNWvWlGh3d3cnMjKyRLuzszMffPBBiXZHR0fmzJnzl+MTEREREbFF5VoSYzKZuOeee0q0Ozs7c/nyZYsPSkRE7gw5+fm3Vb8iIraoXDPsrVq14l//+hczZ840n2San5/Phx9+SIsWLaw6QBERuX052Nnhv2alxfvd2HegxfsUEbFV5Qrsb731FkFBQXTp0sUc0A8ePEhmZiYff/yxVQcoIiIiInI3K9eSmCZNmhAdHU23bt24dOkS+fn59OrVi9jYWDw9Pa09RhERERGRu1a5Ztih8GTPCRMmWHMsIiIiIiJyjfJ/5KeIiIiIiFQ4BXYRERERERumwC4iIiIiYsPKFdhDQ0M5ceKEtcciIiIiIiLXKFdg37x5M/b29tYei4iIiIjIXzLlFdxW/d6qcl0lJiAggIULFzJixAjc3d0xGst9cRkREREREYsyGKuQ9P73Fu+3zpgOFu/TEsqVvL///nsSEhLYsGEDBoOBKlWKT8zHx8dbZXAiIiIiIne7cgX24cOHW3scIiIiIiJSinIF9sDAQGuPQ0RERERESlHuyzru3buXV155hc6dO3PmzBkWLVpEVFSUFYcmIiIiIiLlCuzbt2/nlVdeoW7duqSmplJQUIDBYGDy5MmsXbvW2mMUEREREblrlSuwL168mAkTJvDee+9hZ2cHwMiRI3n77bf5+OOPrTpAEREREZG7WbkC+2+//Ya3t3eJ9k6dOnHq1CmLD0pERERERAqVK7Dfe++9pQbz+Ph47r//fosPSkRERERECpUrsL/wwgtMnTqV7du3A/DHH3+wZs0a3nvvPV1BRkRERETEisp9HfaMjAxGjRpFTk4Of/vb3zAajQwdOpQRI0ZYe4wiIiIiInetcgV2g8FASEgII0aM4Pjx49jb2/Pggw9StWpVa49PREREROSuVq7ADnDlyhViYmI4duwYDg4ONG7cmB49emA0lrsLERERERG5QeVK2ydOnCA4OJjLly/z0EMPUVBQwIoVK1iyZAkfffQRHh4e1h6niIiIiMhdqVwnnYaFhdGmTRt27NjBF198wdq1a/n222/x8PBg6tSp1h6jiIiIiMhdq1yB/dChQ4wZM4YaNWqY25ydnQkJCWHv3r1WG5yIiIiIyN2uXIHdw8ODkydPlmhPSkrCzc3N4oMSEREREZFCZa5h/7//+z/z1z179mTy5MmMGzeOVq1aYWdnxy+//MLcuXN1WUcRERERESsqM7AHBQVhMBgwmUzmtvDw8BLbTZ06lf79+1tndCIiIiIid7kyA/uWLVsqchwiIiIiIlKKMgO7u7t7RY5DRERERERKUa7rsJ86dYr58+dz7NgxcnJySty/adMmiw9MRERERETKGdjffvttkpKS6N69O1WrVrX2mERERERE5P9XrsD+yy+/sHLlSpo3b27t8YiIiIiIyFXKdR32Bg0acPnyZWuPRURERERErlGuGfZ33nmH9957j6FDh1KvXj2qVCme89u0aVOuYpmZmfTv358PPviAevXqMXHiRPbv30+1atUAGDlyJF26dOHw4cNMnjyZrKwsvLy8mDp1KkajkcTEREJCQjh//jwNGzYkIiKCGjVqkJ6ezltvvcWpU6eoXbs2CxYswMXFhZycHCZPnkx8fDxVq1YlIiKCRo0a3eC3SERERESk8pQrsJ84cYLjx48TGhpa4j6DwcDhw4f/so8DBw4QFhZGQkKCuS0+Pp4VK1bg6upabNuQkBCmT59Oq1atmDRpEqtXryYoKIipU6cSFBSEn58fS5YsYenSpYSEhLBgwQK8vLz48MMPiYqKYsaMGSxYsIDIyEiqVatGbGwse/fuZeLEiaxevbo8uywiIiIiYhPKtSRm4cKF9O3bl40bN7Jly5Zi/zZv3lyuQqtXr2bKlCnmcH758mUSExOZNGkSAQEBLFy4kIKCAs6cOcOVK1do1aoVAH369CEuLo7c3Fz27t1L165di7UDbNu2jYCAAAD8/f3ZsWMHubm5bNu2jZ49ewLQrl070tLSSExMLP93R0RERESkkpVrhj0zM5NXXnmFevXq3XShGTNmFLudmppK+/btmTJlCjVr1mT48OGsWbOGxo0b4+LiYt7OxcWFpKQkLly4gJOTE0ajsVg7QHJysvkxRqMRJycn0tLSirUXPebcuXM88MADN70fIiK3s5z8PBzsynXot4l+RUSknIG9a9eubN68mSFDhlissIeHB0uWLDHfDg4OJioqikaNGmEwGMztJpMJg8Fg/v9q196++jFVqlQp8Zii9hsRHx9/Q9uLiGU90rwZ1avWsErfl65kcfjnI1bp21a1bdsWv7UfWrzfr54fxv79+0utZy2qp3q3Us+atVTPurUqo15lK1dgd3d3Z/78+Xz99dc0aNDAPMtd5L333rvhwkePHiUhIcG8xMVkMmE0GnFzcyMlJcW8XWpqKq6urtSuXZuMjAzy8/Oxs7MjJSXFvLzG1dWV1NRU3NzcyMvLIysrC2dnZ+rUqUNycjL169cv1teN8PT0xNHR8Yb3T0QsZ+j6blbp95PAOKv/UbubVPT3UvVUT/Xujnp38r4Vyc7Ovu4kcbmmm/fs2UOLFi2ws7Pj9OnTJCQkFPt3M0wmEzNnzuTixYvk5uayatUqunTpgru7O46OjuZXN9HR0Xh7e2Nvb4+XlxcxMTEAREVF4e3tDYCPjw9RUVEAxMTE4OXlhb29PT4+PkRHRwOwb98+HB0dtRxGRERERG4r5Zphj4yMtHjhZs2aMWzYMAYMGEBeXh6+vr74+/sDEBERQVhYGJmZmTRv3pzBgwcDMGXKFEJDQ1m2bBl169Zl3rx5AIwZM4bQ0FD8/PyoWbMmERERQOEym/DwcPz8/HBwcGDu3LkW3w8REREREWsqV2D/v//7v+veX97rsANs3brV/PXAgQMZOHBgiW2aNWvGmjVrSrS7u7uX+uLB2dmZDz74oES7o6Mjc+bMKffYRERERERsTbkCe1BQkPnEzyIGgwGDwUCVKlV0YqaIiIiI3LFMefkYjHaV1m+5AvuWLVuK3c7Pz+fEiRO8//77vPXWWzc3QhERERGR24DBaEfy4liL9+s6snu5tiv3VWKuVb9+fWrUqMHUqVPZsGHDjY1ORMRG5eTn4mBnf9v1LSIid65b+pSL++67j5MnT1pqLCIilc7Bzp4eUW9ape+Y3v+wSr8iInJnu+mTTjMzM/n0009p3LixxQclIiIiIiKFbvqkUyhcKvP3v//dKgMTEREREZGbPOkUwN7e/oY/NVRERERERG7MTZ90KiIiIiIi1ldmYH/nnXfK1YHBYGDatGkWG5CIiIiIiPxPmYE9ISHhug88ffo0Z8+exWg0KrCLiIiIiFhJmYE9MjKy1Pa8vDw++OADfvzxRx555BFmzpxptcGJiIiIiNztbug67L/88gsTJ07kxIkTvPHGGwwfPhw7O8t/TKuIiIiIiBQqV2DPyclh8eLFLF++nObNm7Nu3Toefvhha49NREREROSu95eB/aeffmLy5MmcOXOG8ePHM3ToUKpUqVIRYxMRERERueuVGdizs7OZN28eK1asoHXr1ixbtoz69etX5NhERO54Ofl5ONjd0OpEm+hbREQqTplH8p49e/LHH3/g4eHBU089RUxMTJmdvPbaa1YZnIjInc7Bzojfeut8YvRXgSFW6VdERCpWmYE9NzeXunXrkpeXxxdffFFmBwaDQYFdRERERMRKygzsW7durchxiIiIiIhIKXT2qIiIiIiIDVNgFxERERGxYQrsIiIiIiI2TIFdRERERMSGKbCLiIiIiNgwBXYRERERERumwC4iIiIiYsMU2EVEREREbJgCu4iIiIiIDVNgFxERERGxYQrsIiIiIiI2TIFdRERERMSGKbCLiIiIiNgwBXYRERERERumwC4iIiIiYsMU2EVEREREbJgCu4iIiIiIDavQwJ6ZmYm/vz+nT58GYPfu3QQEBODr68v8+fPN2x0+fJg+ffrQtWtXJk+eTF5eHgCJiYkMHDiQbt268frrr5OVlQVAeno6w4YNo3v37gwcOJCUlBQAcnJyCAkJoXv37gQGBnL8+PGK3F0RERERkVtWYYH9wIEDDBgwgISEBACuXLnCpEmTWLp0KTExMcTHx7N9+3YAQkJCCA8PZ9OmTZhMJlavXg3A1KlTCQoKIi4uDk9PT5YuXQrAggUL8PLyIjY2ln79+jFjxgwAIiMjqVatGrGxsUyaNImJEydW1O6KiIiIiFhEhQX21atXM2XKFFxdXQE4ePAgDRo0wMPDA6PRSEBAAHFxcZw5c4YrV67QqlUrAPr06UNcXBy5ubns3buXrl27FmsH2LZtGwEBAQD4+/uzY8cOcnNz2bZtGz179gSgXbt2pKWlkZiYWFG7LCIiIiJyy4wVVaho1rtIcnIyLi4u5tuurq4kJSWVaHdxcSEpKYkLFy7g5OSE0Wgs1n5tX0ajEScnJ9LS0krt69y5czzwwANW208REREREUuqsMB+rYKCAgwGg/m2yWTCYDCU2V70/9WuvX31Y6pUqVLiMUXtNyI+Pv6GthcRy2rbtq1V+9+/f7/qWamW6qmeLde7k3737vR6lf2zUhn1rlVpgd3Nzc18cihASkoKrq6uJdpTU1NxdXWldu3aZGRkkJ+fj52dnXl7KJydT01Nxc3Njby8PLKysnB2dqZOnTokJydTv379Yn3dCE9PTxwdHS2wxyJii6z9R+1uqncn75vqqZ7qVV69O3nfiuplZ2dfd5K40i7r2LJlS06cOMHJkyfJz89n48aNeHt74+7ujqOjo/nVRnR0NN7e3tjb2+Pl5UVMTAwAUVFReHt7A+Dj40NUVBQAMTExeHl5YW9vj4+PD9HR0QDs27cPR0dHLYcRERERkdtKpc2wOzo6Mnv2bEaNGkV2djY+Pj5069YNgIiICMLCwsjMzKR58+YMHjwYgClTphAaGsqyZcuoW7cu8+bNA2DMmDGEhobi5+dHzZo1iYiIACA4OJjw8HD8/PxwcHBg7ty5lbOzIiIiIiI3qcID+9atW81fd+jQgS+//LLENs2aNWPNmjUl2t3d3YmMjCzR7uzszAcffFCi3dHRkTlz5tziiEVEREREKo8+6VRERERExIYpsIvIDcvLz7mt+hUREbmdVdoadhG5fRntHJixqqvF+5384iaL9ykiInK70wy7iIiIiIgNU2AXEREREbFhCuwiIiIiIjZMgV1ERERExIYpsIuIiIiI2DAFdhERERERG6bALiIiIiJiwxTYRURERERsmAK7iIiIiIgNU2AXEREREbFhCuwiIiIiIjZMgV1ERERExIYpsIuIiIiI2DAFdhERERERG6bALiIiIiJiwxTYRURERERsmAK7iIiIiIgNU2AXEREREbFhCuwiIiIiIjZMgV1ERERExIYpsIuIiIiI2DAFdhERERERG6bALiIiIiJiwxTYRURERERsmAK7iIiIiIgNU2AXEREREbFhCuwiIiIiIjZMgV1ERERExIYpsIuIiIiI2DAFdhERERERG6bALiIiIiJiw4yVPQCA4OBg0tLSMBoLhzNt2jSysrKYNWsW2dnZdO/enXHjxgFw+PBhJk+eTFZWFl5eXkydOhWj0UhiYiIhISGcP3+ehg0bEhERQY0aNUhPT+ett97i1KlT1K5dmwULFuDi4lKZuysiIiIiUm6VPsNuMplISEggOjra/K9p06ZMmjSJpUuXEhMTQ3x8PNu3bwcgJCSE8PBwNm3ahMlkYvXq1QBMnTqVoKAg4uLi8PT0ZOnSpQAsWLAALy8vYmNj6devHzNmzKi0fRURERERuVGVHth///13AF5++WV69uzJihUrOHjwIA0aNMDDwwOj0UhAQABxcXGcOXOGK1eu0KpVKwD69OlDXFwcubm57N27l65duxZrB9i2bRsBAQEA+Pv7s2PHDnJzc294nKa8fAvsbcX3LSIiIiK3t0pfEpOenk6HDh145513yM3NZfDgwbzyyivFlq24urqSlJREcnJysXYXFxeSkpK4cOECTk5O5iU1Re1AsccYjUacnJxIS0ujTp06NzROg9GOlGUrbnV3S+Xy+iCr9CsiIiIit79KD+ytW7emdevW5tt9+/Zl4cKFtG3b1txmMpkwGAwUFBRgMBhKtBf9f7Vrb1/9mCpVyv/GQnx8PECx8VjD/v37rdq/iCVZ8/fh2t+Fiv7dUz3L1VI91bPlenfS796dXq+yf1Yqo961Kj2w79u3j9zcXDp06AAUBmp3d3dSUlLM26SkpODq6oqbm1ux9tTUVFxdXalduzYZGRnk5+djZ2dn3h4KZ+dTU1Nxc3MjLy+PrKwsnJ2dyz0+T09PHB0dLbOz12HtXyyR20VF/y6o3u1ZS/VUT/Xunnp38r4V1cvOzjZPEpem0tewZ2RkMHfuXLKzs8nMzGT9+vWMHz+eEydOcPLkSfLz89m4cSPe3t64u7vj6OhofiUSHR2Nt7c39vb2eHl5ERMTA0BUVBTe3t4A+Pj4EBUVBUBMTAxeXl7Y29tXyr6KiIiIiNyoSp9h79SpEwcOHKB3794UFBQQFBRE69atmT17NqNGjSI7OxsfHx+6desGQEREBGFhYWRmZtK8eXMGDx4MwJQpUwgNDWXZsmXUrVuXefPmATBmzBhCQ0Px8/OjZs2aREREVNq+ioiIiIjcqEoP7ABjx45l7Nixxdo6dOjAl19+WWLbZs2asWbNmhLt7u7uREZGlmh3dnbmgw8+sNhYRUREREQqUqUviRERERERkbIpsIuIiIiI2DAFdhERERERG6bALiIiIiJiwxTYRe4Aefk5t1W/IiIiUn42cZUYEbk1RjsH/hnZ1eL9Dg/eZPE+RURE5MZohl1ERERExIYpsIuIiIiI2DAFdhERERERG6bALiIiIiJiwxTYRURERERsmAK7iIiIiIgNU2AXEREREbFhCuwiIiIiIjZMgV1ERERExIYpsIuIiIiI2DAFdhERERERG6bALiIiIiJiwxTYRURERERsmAK7iIiIiIgNU2AXEREREbFhCuwiIiIiIjZMgV1ERERExIYpsIuIiIiI2DAFdhERERERG6bALiIiIiJiwxTYRURERERsmAK7iIiIiIgNU2AXEREREbFhCuwiVpCfl3Nb9SsiIiK2y1jZAxC5E9kZHVj1STeL9/vi0DiL9ykiIiK2TTPsIiIiIiI2TIFdRERERMSGKbCLiIiIiNgwBXYRERERERt2VwT2DRs20KNHD3x9fVm5cmVlD0dEREREpNzu+KvEJCUlMX/+fNatW4eDgwP9+/fniSee4OGHH67soYmIiIiI/KU7foZ99+7dtG/fHmdnZ6pXr07Xrl2Ji9Ol8URERETk9nDHz7AnJyfj4uJivu3q6srBgwf/8nEmkwmAnJz/fVBNnoO95QcIZGdnW6VfqVxGe2eL93m9nxWHCq5XzVhx9WraWb7W9eo529Wo4HpVK7ae0aHCahXWs/yx8/r17Cq03j1Gy899Xb+eqULr1bDLr9B6DnZ5FVavitHyta5XD/uKrZfvWLH18qoWVFgtgDxHi5f7i3rW+10vyptF+fNaBlNZ99whli1bRnZ2NmPHjgVg9erVxMfHM23atOs+LiMjg19//bUCRigiIiIiAk2aNKFmzZol2u/4GXY3Nzf27dtnvp2SkoKrq+tfPq5GjRo0adIEe3t7DAaDNYcoIiIiIncxk8lEbm4uNWqU/g7vHR/Yn3zySRYtWkRaWhrVqlXj66+/5r333vvLx1WpUqXUVzgiIiIiIpZWtWrZyyPv+MBep04dxo0bx+DBg8nNzaVv3760aNGisoclIiIiIlIud/wadhERERGR29kdf1lHEREREZHbmQK7iIiIiIgNU2AXEREREbFhCuwiIiIiIjZMgV1ERERExIYpsFvYhg0b6NGjB76+vqxcudLq9TIzM/H39+f06dNWr7V48WL8/Pzw8/Nj7ty5Vq8H8P7779OjRw/8/Pz45JNPKqTmnDlzCA0NtXqd4OBg/Pz86NWrF7169eLAgQNWrbd161b69OlD9+7dmT59ulVrffHFF+b96tWrF23btv3LTxe+VdHR0eafzzlz5li1FsCHH35I165dCQgIYNmyZVapce3v9+7duwkICMDX15f58+dXSE2ACRMmsG7dOqvXWrVqFf7+/gQEBDBx4kTzR3Vbq97nn3+On58fPXr0YM6cOWV+JLil6hVZsWIFwcHBFq1VWr2JEyfi6+tr/j385ptvrFrvxx9/5IUXXsDPz4/x48db9fnbvn17sWNM+/btGT58uNXqAezcuZOePXvi7+/PhAkTrP7zuW7dOnr06EFAQADTp08nLy/PYrVK+3tureNLWdkhNzeXl156iR9++MFitcqqZ81jS2n1rHJsMYnFnDt3ztSpUyfThQsXTFlZWaaAgADTsWPHrFbvp59+Mvn7+5uaN29uOnXqlNXqmEwm065du0wvvviiKTs725STk2MaPHiw6euvv7ZqzR9++MHUv39/U25uruny5cumTp06mY4fP27Vmrt37zY98cQTprffftuqdQoKCkwdO3Y05ebmWrVOkT/++MPUsWNH09mzZ005OTmmAQMGmLZt21YhtX/99VdTly5dTOfPn7dajUuXLpnatWtnOn/+vCk3N9fUt29f065du6xWb9euXSZ/f39TRkaGKS8vzzR8+HDTpk2bLFrj2t/vy5cvm3x8fEx//PGHKTc31/Tyyy9b/Dm8tua5c+dMw4cPN7Vo0cK0du1aq9b6/fffTV26dDFlZGSYCgoKTBMmTDB98sknVqv3xx9/mLp06WLKysoy5eXlmV588UXTd999Z7V6RY4dO2Z6+umnTYMGDbJYrbLq+fv7m5KSkixap6x6GRkZpqeeesp0+PBhk8lkMo0bN860cuVKq9W7WnJysunZZ581nThxwqr1vL29Tb/99pvJZDKZRo0aZVq9erXV6h0/ftz09NNPm5+/KVOmmD7++GOL1Crt7/mGDRuscnwpKzscP37c9OKLL5oee+wx03//+18L7FXZ9f75z39a7dhSWr1PPvnEKscWzbBb0O7du2nfvj3Ozs5Ur16drl27EhcXZ7V6q1evZsqUKbi6ulqtRhEXFxdCQ0NxcHDA3t6eRo0akZiYaNWajz/+OJ999hlGo5Hz58+Tn59P9erVrVbvzz//ZP78+bz22mtWq1Hk999/B+Dll1+mZ8+erFixwqr1vvnmG3r06IGbmxv29vbMnz+fli1bWrVmkXfffZdx48ZRu3Ztq9XIz8+noKCAy5cvk5eXR15eHo6Ojlar98svv9CxY0ecnJyws7Pj6aefZvPmzRatce3v98GDB2nQoAEeHh4YjUYCAgIsfny5tuaGDRt49tln6d69u0XrlFbLwcGBKVOm4OTkhMFgoEmTJhY9xlxbz8PDg6+++orq1auTnp5OZmYmtWrVslo9gJycHMLDwxk9erTF6pRV7/LlyyQmJjJp0iQCAgJYuHAhBQUFVqu3a9cuWrVqRbNmzQAICwujS5cuVqt3tblz59K/f38efPBBq9bLz88nMzOT/Px8srOzLXqMubbe0aNHadWqlfl2p06dLHaMKe3veUJCglWOL2VlhzVr1vDKK69Y/O9QafVycnKsdmwprZ7BYLDKseWO/6TTipScnIyLi4v5tqurKwcPHrRavRkzZlit72s1btzY/HVCQgKxsbH8+9//tnpde3t7Fi5cyMcff0y3bt2oU6eO1WqFh4czbtw4zp49a7UaRdLT0+nQoQPvvPMOubm5DB48mIYNG/LUU09Zpd7Jkyext7fntdde4+zZszzzzDOMHTvWKrWutnv3bq5cuWKVwHc1JycnxowZQ/fu3alWrRrt2rWjTZs2VqvXvHlzZs6cyfDhw6lWrRpbt261+HKKa3+/Szu+JCUlWbXmK6+8AsD+/fstWqe0Wu7u7ri7uwOQlpbGypUrmTVrltXqQeHxZfXq1cyZM4cWLVqYw6a16v3jH//g+eefp169eharU1a91NRU2rdvz5QpU6hZsybDhw9nzZo1vPDCC1apd/LkSapXr864ceP4/fffadOmjUWXFpb19y4hIYE9e/ZY/O9haf29++67BAcH4+TkRL169ejWrZvV6jVr1ozZs2dz9uxZXF1diYuLIzU11SK1Svt7PmjQIKscX8rKDkUvrj799NNbrnEj9Sx9bCmrnjWOLZpht6CCggIMBoP5tslkKnb7TnDs2DFefvllJkyYYNHZjOsZPXo033//PWfPnmX16tVWqfHFF19Qt25dOnToYJX+r9W6dWvmzp1LzZo1qV27Nn379mX79u1Wq5efn8/333/PzJkzWbVqFQcPHmT9+vVWq1fkP//5D0OHDrV6nSNHjrB27Vq+/fZbvvvuO6pUqcLy5cutVq9Dhw706dOH4OBgXnnlFdq2bYu9vb3V6sHdcXwBSEpK4qWXXuL555/niSeesHq9F154gR9++IH777+fxYsXW63Orl27OHv2LM8//7zValzNw8ODJUuW4OrqSrVq1QgODrb6MWbnzp2MHz+edevWcfnyZT788EOr1SuyatUqgoKCcHBwsGqdlJQUIiIi2LhxIzt37qRly5YWfUF5rYYNG/Lmm2/y+uuvM3DgQJo2bWrxY8zVf889PDysenyp6OxQWj1rHltKq2fpY4sCuwW5ubmRkpJivp2SklIhy1Uqyv79+xkyZAhvvvkmgYGBVq93/PhxDh8+DEC1atXw9fXl6NGjVqkVExPDrl276NWrFwsXLmTr1q3MnDnTKrUA9u3bx/fff2++bTKZMBqt94bX/fffT4cOHahduzZVq1blueees+q7P1D49v/evXvp3LmzVetA4clgHTp04L777sPBwYE+ffqwZ88eq9XLzMzE19eXDRs2EBkZiYODAx4eHlarB3f+8QUKf+f79+9PYGAgI0aMsGqts2fPmt85MBqN+Pn5We34ArBx40aOHTtGr169CAsLIz4+3qrvch09epRNmzaZb1fEMaZly5Z4eHhgZ2dH9+7drX6MAdiyZQs9evSwep19+/bRpEkT6tevT5UqVXjhhReseozJzs6mRYsWREVF8Z///Ic6depY9Bhz7d9zax5fKjo7lFbPmseWa+tZ69iiwG5BTz75JN9//z1paWlcvnyZr7/+Gm9v78oelkWcPXuWESNGEBERgZ+fX4XUPH36NGFhYeTk5JCTk8OWLVto27atVWp98sknbNy4kejoaEaPHk3nzp2ZNGmSVWoBZGRkMHfuXLKzs8nMzGT9+vUWXe95rU6dOrFz507S09PJz8/nu+++o3nz5larB4WB4cEHH7TqeQdFmjVrxu7du7l06RImk4mtW7fy2GOPWa3e6dOneeONN8jLyyMjI4M1a9ZYfdlPy5YtOXHiBCdPniQ/P5+NGzfeMccXKHwR9Le//Y0xY8bw8ssvW71eRkYGISEhpKenYzKZ2LRpk9WOLwCzZs0iNjaW6Ohopk+fjqenJwsWLLBaPZPJxMyZM7l48SK5ubmsWrXKqseYjh078vPPP5uXFH777bdWP8akpaVx5coVq79YBmjSpAkHDx40L0vZsmWLVY8xly5dYsiQIWRmZpKTk8OKFSss9sKktL/n1jq+VHR2KK2eNY8tpdWz1rFFa9gtqE6dOowbN47BgweTm5tL3759adGiRWUPyyKWL19OdnY2s2fPNrf179+fAQMGWK2mj48PBw8epHfv3tjZ2eHr61thLxasrVOnThw4cIDevXtTUFBAUFAQrVu3tlq9li1b8sorrxAUFERubi5PPfWU1d+aP3XqFG5ublatUaRjx4788ssv9OnTB3t7ex577DGGDRtmtXrNmjXD19eXnj17kp+fz5AhQ6wa9gAcHR2ZPXs2o0aNIjs7Gx8fH4uuoa1sa9asITU1lU8++cR8CdfOnTszZswYq9Rr0qQJw4YNo3///tjZ2eHl5VUhy7cqSrNmzRg2bBgDBgwgLy8PX19f/P39rVavbt26TJs2jddee43s7GweeeQR3n77bavVg8IXzhV1jGnUqBFjxoxh8ODB2NnZ0aBBA6teqvbee+9lxIgRvPjii+Tl5ZkvSWgJZf09t8bxpaKzQ2n1evToYbVjS1n7Z41ji8Fk6TOlRERERETEYrQkRkRERETEhimwi4iIiIjYMAV2EREREREbpsAuIiIiImLDFNhFRERERGyYAruIyG0oODiYyZMnl3rfkCFDLPqx8Jby3Xff0blzZx577DE+++yzEveHhobStGlT87/mzZvz5JNPMm7cOE6ePFkJIxYRsQ26DruIiFSIBQsW0LBhQz777DOcnZ1L3cbLy8v8gUI5OTmcPn2a+fPn079/f1avXl0hH5IjImJrNMMuIiIVIiMjg5YtW1KvXj2cnJxK3cbe3h4XFxdcXFxwd3fniSee4KOPPsLBwYF58+ZV8IhFRGyDAruIyB0uMTGRcePG0aFDB1q3bs0bb7zBqVOnzPd37tyZpUuXFnvM1W2LFi0iODiY0aNH06ZNG+bPn19qnX379jFo0CBat27Nk08+yfTp07l8+TIATZs25eTJkyxZsoSmTZve0PidnJzo06cPW7ZsITs7G4AjR47w6quv4uXlhaenJ127diUqKgqAb775hkcffZSUlBRzHyaTiU6dOvHpp5/eUG0REVugwC4icgfLzMxkwIABXLx4kY8++ojIyEgyMjIYNGgQGRkZ5e5nz549eHh4sH79evr27Vvi/gMHDjBkyBAee+wx1qxZw6xZs9iyZQvjxo0DYOfOnbi7u/Pyyy+zc+fOG96PJk2akJ2dTUJCApcuXeLll1/G1dWV1atXEx0dTbt27QgLCyM1NZVnnnmGWrVq8dVXX5kfv3fvXlJSUiz28e4iIhVJgV1E5DYVFRVF69atS/zbs2ePeZvo6GjS09OZN28ezZs3x9PTk/fff5+LFy/y5ZdflruWwWBg1KhRNGjQoNR15B9//DGenp68/fbbNGrUCB8fH959912+/fZbjh07houLC3Z2dlSvXh0XF5cb3td77rkHKHwBcvnyZYYMGUJYWBgPPfQQjRo1Yvjw4eTm5pKQkIC9vT3+/v7F9i86Ohpvb29q1659w7VFRCqbTjoVEblNPffcc4wfP75E+9tvv23++tixYzz00EPFTvKsXbs2jRo14tdffy13LRcXF6pWrVrm/ceOHcPHx6dYm5eXl/m+xo0bl7tWaTIzMwGoVasW9913H0FBQURFRXH48GESEhI4cuQIAPn5+QD06dOHyMhIjh8/Tr169di0aROzZs26pTGIiFQWBXYRkduUk5MTDRo0KNF+dbB2dHQs9bEFBQXY29uX2XdeXl6ZfZamtDomkwkAo/HW/9T8/PPPVK9enQcffJDk5GRefPFF6tSpQ6dOnXjmmWdwdXXl+eefN2//6KOP0qxZMzZs2EDTpk2xs7PjmWeeueVxiIhUBgV2EZE72MMPP8zq1av5888/zbPsaWlpnDhxghdeeAEovDJL0Qw2FM5mnz9//obr/Pjjj8Xa9u/fD0CjRo1uYQ/g0qVLREdH061bN+zt7fnmm2/Iyspi5cqV2NnZAYXXeIf/vUgACAwMZM2aNZw6dYqAgIDrvkAREbFlWsMuInIH69mzJ7Vr12b8+PH88ssv/Pzzz4wfP55atWrh5+cHQKtWrfjqq6/48ccfOXbsGKGhoeYgXF6vvvoqhw4dYs6cOfz+++989913TJ06FR8fnxsK7Lm5uaSkpJCSkkJiYiK7du1iyJAh5ObmMnbsWADuvfdeMjMz2bRpE2fOnGHLli1MmTIFKLx2+9X7npCQwObNmwkMDLyh/RERsSWaYRcRuYM5OjqyfPlyZs+ezcCBA7Gzs6NDhw6sXLmSWrVqATB+/HjCw8MZMmQINWvW5OWXXy42414eTZo04YMPPmDBggVERkbi7OyMn5+fOWSX1759++jYsSMADg4O1KlTh44dOzJ//nzq1KkDQPfu3Tl06BDTp0/n0qVL1K9fnzfeeIMPP/yQQ4cO4e3tDRSu1e/YsSNnzpyhefPmNzQOERFbYjBd/f6hiIjIHaRPnz4EBAQwdOjQyh6KiMhN0wy7iIjccTZv3syBAwdISEjQchgRue0psIuIyB1n2bJlJCYmMmvWrGKXtBQRuR1pSYyIiIiIiA3TVWJERERERGyYAruIiIiIiA1TYBcRERERsWEK7CIiIiIiNkyBXURERETEhimwi4iIiIjYsP8PwX+i052ccYUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating graph showing the number of electric scooter trips by hour in the day\n",
    "plt.figure(figsize = (12, 6))\n",
    "sns.set(style = 'whitegrid')\n",
    "\n",
    "# Plot\n",
    "eda_data_plot = sns.barplot(x = 'hour_of_day', y = 'eda_count', data = df_e_scooter_trips_plot)\n",
    "\n",
    "# Labels and titles\n",
    "plt.title('Number of Trips per Hour in the Day', fontsize = 20)\n",
    "plt.xlabel('Hour of Day', fontsize = 15)\n",
    "plt.ylabel('Number of Trips', fontsize = 15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e9445797",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Line plot\n",
    "plt.figure(figsize = (12, 6))\n",
    "sns.set(style = 'whitegrid')\n",
    "\n",
    "graph1 = sns.lineplot(x = 'hour_of_day', y = 'eda_count', data = df_e_scooter_trips_plot)\n",
    "\n",
    "# Titles and labels\n",
    "plt.title('Number of Trips per Hour in the Day', fontsize = 20)\n",
    "plt.xlabel('Hour of day', fontsize = 15)\n",
    "plt.ylabel('Number of trips', fontsize = 15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f501896d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Demand per Area and Time:\n",
      "   Start Community Area Name  hour_of_day  demand_count\n",
      "0                Albany Park            0           419\n",
      "1                Albany Park            4             1\n",
      "2                Albany Park            5            78\n",
      "3                Albany Park            6           227\n",
      "4                Albany Park            7           624\n",
      "5                Albany Park            8           944\n",
      "6                Albany Park            9           945\n",
      "7                Albany Park           10           936\n",
      "8                Albany Park           11          1100\n",
      "9                Albany Park           12          1176\n",
      "10               Albany Park           13          1341\n",
      "11               Albany Park           14          1582\n",
      "12               Albany Park           15          2052\n",
      "13               Albany Park           16          2020\n",
      "14               Albany Park           17          2049\n",
      "15               Albany Park           18          1912\n",
      "16               Albany Park           19          1842\n",
      "17               Albany Park           20          1761\n",
      "18               Albany Park           21          1375\n",
      "19               Albany Park           22          1177\n",
      "20               Albany Park           23           965\n",
      "21            Archer Heights            0           178\n",
      "22            Archer Heights            5            68\n",
      "23            Archer Heights            6           147\n",
      "24            Archer Heights            7           224\n",
      "25            Archer Heights            8           489\n",
      "26            Archer Heights            9           424\n",
      "27            Archer Heights           10           559\n",
      "28            Archer Heights           11           654\n",
      "29            Archer Heights           12           839\n",
      "30            Archer Heights           13           994\n",
      "31            Archer Heights           14          1188\n",
      "32            Archer Heights           15          1692\n",
      "33            Archer Heights           16          1588\n",
      "34            Archer Heights           17          1616\n",
      "35            Archer Heights           18          1322\n",
      "36            Archer Heights           19          1236\n",
      "37            Archer Heights           20          1026\n",
      "38            Archer Heights           21           828\n",
      "39            Archer Heights           22           554\n",
      "40            Archer Heights           23           429\n",
      "41             Armour Square            0           314\n",
      "42             Armour Square            5            35\n",
      "43             Armour Square            6           140\n",
      "44             Armour Square            7           254\n",
      "45             Armour Square            8           463\n",
      "46             Armour Square            9           594\n",
      "47             Armour Square           10           658\n",
      "48             Armour Square           11           691\n",
      "49             Armour Square           12           765\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ensure all necessary columns are present\n",
    "if 'Start Community Area Name' in df_e_scooter_trips.columns:\n",
    "    # Group by community area and hour to aggregate trip counts\n",
    "    demand_per_area_time = df_e_scooter_trips.groupby(['Start Community Area Name', 'hour_of_day']).size().reset_index(name='demand_count')\n",
    "\n",
    "    # Sorting the results for better readability\n",
    "    demand_per_area_time = demand_per_area_time.sort_values(by=['Start Community Area Name', 'hour_of_day'])\n",
    "\n",
    "    # Display the demand data\n",
    "    print(\"Demand per Area and Time:\")\n",
    "    print(demand_per_area_time.head(50))  # Adjust the number to display more or fewer rows as needed\n",
    "else:\n",
    "    print(\"The column 'Start Community Area Name' does not exist in the dataset.\")\n",
    "\n",
    "# For demonstration, visualize the first community area's data\n",
    "first_area_name = demand_per_area_time['Start Community Area Name'].iloc[0]\n",
    "area_data = demand_per_area_time[demand_per_area_time['Start Community Area Name'] == first_area_name]\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(area_data['hour_of_day'], area_data['demand_count'], marker='o')\n",
    "plt.title(f'E-scooter Trip Count per Hour for {first_area_name}')\n",
    "plt.xlabel('Hour of Day')\n",
    "plt.ylabel('E-scooter Trip Count')\n",
    "plt.grid(True)\n",
    "plt.xticks(range(0, 24))  # Set x-axis ticks to show every hour\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21300781",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0589133e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group by date and hour to aggregate trip counts\n",
    "df_e_scooter_trips['date_trips'] = df_e_scooter_trips['Start Time'].dt.floor('H')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b49dff66",
   "metadata": {},
   "outputs": [],
   "source": [
    "hourly_demand = df_e_scooter_trips.groupby('date_trips').size().reset_index(name='trip_count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "103538f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_trips</th>\n",
       "      <th>trip_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-05-10 09:00:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-05-10 10:00:00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-05-10 11:00:00</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-05-10 12:00:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-05-10 13:00:00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10294</th>\n",
       "      <td>2023-09-30 16:00:00</td>\n",
       "      <td>1531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10295</th>\n",
       "      <td>2023-09-30 17:00:00</td>\n",
       "      <td>1391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10296</th>\n",
       "      <td>2023-09-30 18:00:00</td>\n",
       "      <td>1552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10297</th>\n",
       "      <td>2023-09-30 19:00:00</td>\n",
       "      <td>1407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10298</th>\n",
       "      <td>2023-09-30 20:00:00</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10299 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               date_trips  trip_count\n",
       "0     2022-05-10 09:00:00           4\n",
       "1     2022-05-10 10:00:00          10\n",
       "2     2022-05-10 11:00:00          11\n",
       "3     2022-05-10 12:00:00           3\n",
       "4     2022-05-10 13:00:00           9\n",
       "...                   ...         ...\n",
       "10294 2023-09-30 16:00:00        1531\n",
       "10295 2023-09-30 17:00:00        1391\n",
       "10296 2023-09-30 18:00:00        1552\n",
       "10297 2023-09-30 19:00:00        1407\n",
       "10298 2023-09-30 20:00:00          63\n",
       "\n",
       "[10299 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourly_demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e7db51b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge weather info into hourly demand df\n",
    "hourly_demand['date'] = hourly_demand['date_trips'].dt.date  # Extracting just the date for merging\n",
    "df_weather['datetime'] = pd.to_datetime(df_weather['datetime']).dt.date  # Making sure it's just the date\n",
    "hourly_demand = pd.merge(hourly_demand, df_weather, left_on='date', right_on='datetime', how='left')\n",
    "hourly_demand.drop(['date', 'datetime'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ff406543",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_trips</th>\n",
       "      <th>trip_count</th>\n",
       "      <th>temp</th>\n",
       "      <th>precip</th>\n",
       "      <th>snow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-05-10 09:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>80.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-05-10 10:00:00</td>\n",
       "      <td>10</td>\n",
       "      <td>80.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-05-10 11:00:00</td>\n",
       "      <td>11</td>\n",
       "      <td>80.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-05-10 12:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>80.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-05-10 13:00:00</td>\n",
       "      <td>9</td>\n",
       "      <td>80.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10294</th>\n",
       "      <td>2023-09-30 16:00:00</td>\n",
       "      <td>1531</td>\n",
       "      <td>70.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10295</th>\n",
       "      <td>2023-09-30 17:00:00</td>\n",
       "      <td>1391</td>\n",
       "      <td>70.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10296</th>\n",
       "      <td>2023-09-30 18:00:00</td>\n",
       "      <td>1552</td>\n",
       "      <td>70.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10297</th>\n",
       "      <td>2023-09-30 19:00:00</td>\n",
       "      <td>1407</td>\n",
       "      <td>70.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10298</th>\n",
       "      <td>2023-09-30 20:00:00</td>\n",
       "      <td>63</td>\n",
       "      <td>70.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10299 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               date_trips  trip_count  temp  precip  snow\n",
       "0     2022-05-10 09:00:00           4  80.3     0.0   0.0\n",
       "1     2022-05-10 10:00:00          10  80.3     0.0   0.0\n",
       "2     2022-05-10 11:00:00          11  80.3     0.0   0.0\n",
       "3     2022-05-10 12:00:00           3  80.3     0.0   0.0\n",
       "4     2022-05-10 13:00:00           9  80.3     0.0   0.0\n",
       "...                   ...         ...   ...     ...   ...\n",
       "10294 2023-09-30 16:00:00        1531  70.7     0.0   0.0\n",
       "10295 2023-09-30 17:00:00        1391  70.7     0.0   0.0\n",
       "10296 2023-09-30 18:00:00        1552  70.7     0.0   0.0\n",
       "10297 2023-09-30 19:00:00        1407  70.7     0.0   0.0\n",
       "10298 2023-09-30 20:00:00          63  70.7     0.0   0.0\n",
       "\n",
       "[10299 rows x 5 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourly_demand"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cba5f80f",
   "metadata": {},
   "source": [
    "### Exogenous variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6810ec4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare exogenous variables, for example, temperature and precipitation\n",
    "exog_vars = hourly_demand[['temp', 'precip', 'snow']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1f62cb8",
   "metadata": {},
   "source": [
    "# Implementing the ARIMAX Model for Demand Forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e50379ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                     SARIMAX Results                                      \n",
      "==========================================================================================\n",
      "Dep. Variable:                         trip_count   No. Observations:                10299\n",
      "Model:             SARIMAX(1, 0, 1)x(1, 0, 1, 24)   Log Likelihood              -61725.618\n",
      "Date:                            Thu, 30 May 2024   AIC                         123467.236\n",
      "Time:                                    02:02:21   BIC                         123525.134\n",
      "Sample:                                         0   HQIC                        123486.808\n",
      "                                          - 10299                                         \n",
      "Covariance Type:                              opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "temp           6.3441      0.182     34.904      0.000       5.988       6.700\n",
      "precip      -129.0609      4.069    -31.721      0.000    -137.035    -121.086\n",
      "snow         -69.0340      5.271    -13.096      0.000     -79.366     -58.702\n",
      "ar.L1          0.8894      0.004    201.217      0.000       0.881       0.898\n",
      "ma.L1          0.5633      0.006     96.940      0.000       0.552       0.575\n",
      "ar.S.L24       0.5737      0.066      8.737      0.000       0.445       0.702\n",
      "ma.S.L24      -0.6366      0.062    -10.343      0.000      -0.757      -0.516\n",
      "sigma2      9559.0191     72.270    132.268      0.000    9417.372    9700.666\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                 132.30   Jarque-Bera (JB):             13503.43\n",
      "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               2.12   Skew:                            -0.52\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         8.52\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "# Define the ARIMAX model\n",
    "model = SARIMAX(hourly_demand['trip_count'],\n",
    "                order=(1, 0, 1),\n",
    "                seasonal_order=(1, 0, 1, 24),\n",
    "                exog=exog_vars,\n",
    "                enforce_stationarity=False,\n",
    "                enforce_invertibility=False)\n",
    "\n",
    "# Fit the model\n",
    "results = model.fit(disp=False)\n",
    "\n",
    "# Print out the summary of the model's performance\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "40fe8878",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trip_count        mean     mean_se  mean_ci_lower  mean_ci_upper\n",
      "10299      -574.913674   97.770236    -766.539815    -383.287532\n",
      "10300      -479.708462  172.434883    -817.674622    -141.742301\n",
      "10301      -381.175331  213.761533    -800.140237      37.789575\n",
      "10302      -301.830854  241.495478    -775.153294     171.491586\n",
      "10303      -236.443094  261.358981    -748.697285     275.811096\n",
      "10304      -177.668107  276.062226    -718.740128     363.403914\n",
      "10305      -105.787167  287.161058    -668.612498     457.038164\n",
      "10306        14.255407  295.646270    -565.200634     593.711448\n",
      "10307        70.128560  302.190148    -522.153246     662.410366\n",
      "10308       110.630347  307.268272    -491.604399     712.865094\n",
      "10309       147.370403  311.226866    -462.623046     757.363851\n",
      "10310       190.583306  314.323180    -425.478808     806.645419\n",
      "10311       213.287139  316.751224    -407.533851     834.108130\n",
      "10312       226.252106  318.658935    -398.307930     850.812141\n",
      "10313       233.893364  320.160067    -393.608837     861.395564\n",
      "10314       230.354508  321.342640    -399.465493     860.174509\n",
      "10315       252.332434  322.275096    -379.315147     883.980016\n",
      "10316       262.123303  323.010852    -370.966334     895.212941\n",
      "10317       258.256720  323.591722    -375.971401     892.484841\n",
      "10318       244.591348  324.050509    -390.535978     879.718675\n",
      "10319       262.987615  324.412994    -372.850169     898.825399\n",
      "10320       257.694847  324.699468    -378.704416     894.094111\n",
      "10321       255.366503  324.925918    -381.476594     892.209601\n",
      "10322       341.006440  325.104951    -296.187554     978.200434\n"
     ]
    }
   ],
   "source": [
    "# Diagnostics\n",
    "results.plot_diagnostics(figsize=(15, 12))\n",
    "plt.show()\n",
    "\n",
    "# Forecasting\n",
    "forecast = results.get_forecast(steps=24, exog=exog_vars[-24:])\n",
    "print(forecast.summary_frame())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edf97e43",
   "metadata": {},
   "source": [
    "# LSTM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fc64239",
   "metadata": {},
   "source": [
    "## Preparing Data for LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a3eef69d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare dataset for LSTM\n",
    "data = hourly_demand.set_index('date_trips')['trip_count'].values.reshape(-1, 1)\n",
    "\n",
    "# Normalize the dataset\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "data_scaled = scaler.fit_transform(data)\n",
    "\n",
    "# Function to create dataset for LSTM\n",
    "def create_dataset(data, look_back=1):\n",
    "    X, Y = [], []\n",
    "    for i in range(len(data)-look_back-1):\n",
    "        a = data[i:(i+look_back), 0]\n",
    "        X.append(a)\n",
    "        Y.append(data[i + look_back, 0])\n",
    "    return np.array(X), np.array(Y)\n",
    "\n",
    "# Split into train and test sets\n",
    "look_back = 3  # Number of past hours to use for forecasting\n",
    "X, y = create_dataset(data_scaled, look_back)\n",
    "train_size = int(len(X) * 0.67)\n",
    "test_size = len(X) - train_size\n",
    "X_train, X_test = X[:train_size], X[train_size:]\n",
    "y_train, y_test = y[:train_size], y[train_size:]\n",
    "\n",
    "# Reshape input to be\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec293a66",
   "metadata": {},
   "source": [
    "## Build and Tune LSTM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6b67f899",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tab\\AppData\\Roaming\\Python\\Python39\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\tab\\AppData\\Roaming\\Python\\Python39\\site-packages\\keras\\src\\backend\\common\\global_state.py:82: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tab\\AppData\\Roaming\\Python\\Python39\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n",
      "C:\\Users\\tab\\AppData\\Roaming\\Python\\Python39\\site-packages\\keras\\src\\saving\\saving_lib.py:415: UserWarning: Skipping variable loading for optimizer 'adam', because it has 2 variables whereas the saved optimizer has 12 variables. \n",
      "  saveable.load_own_variables(weights_store.get(inner_path))\n"
     ]
    }
   ],
   "source": [
    "# Define the LSTM model architecture as a hypermodel\n",
    "class LSTMHyperModel(HyperModel):\n",
    "    def __init__(self, input_shape):\n",
    "        self.input_shape = input_shape\n",
    "\n",
    "    def build(self, hp):\n",
    "        model = Sequential()\n",
    "        model.add(LSTM(\n",
    "            units=hp.Int('units', min_value=32, max_value=512, step=32),\n",
    "            input_shape=self.input_shape\n",
    "        ))\n",
    "        model.add(Dense(1, activation='linear'))\n",
    "        model.compile(\n",
    "            optimizer=keras.optimizers.Adam(\n",
    "                hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])),\n",
    "            loss='mean_squared_error'\n",
    "        )\n",
    "        return model\n",
    "\n",
    "# Instantiate the hypermodel\n",
    "input_shape = (X_train.shape[1], 1)  # Adjust based on your input feature shape\n",
    "hypermodel = LSTMHyperModel(input_shape)\n",
    "\n",
    "# Run random search\n",
    "tuner = RandomSearch(\n",
    "    hypermodel,\n",
    "    objective='val_loss',\n",
    "    max_trials=10,\n",
    "    executions_per_trial=2,\n",
    "    directory='my_dir',\n",
    "    project_name='lstm_e_scooter'\n",
    ")\n",
    "\n",
    "tuner.search(X_train, y_train, epochs=10, validation_split=0.2, verbose=0)\n",
    "\n",
    "# Get the best model\n",
    "best_model = tuner.get_best_models(num_models=1)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeca96d9",
   "metadata": {},
   "source": [
    "## LSTM Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2efaa111",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m107/107\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0032 \n",
      "Test Loss: 0.00554019445553422\n",
      "\u001b[1m107/107\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate on the test data\n",
    "test_loss = best_model.evaluate(X_test, y_test)\n",
    "print('Test Loss:', test_loss)\n",
    "\n",
    "# Optionally, predict and plot predictions\n",
    "predictions = best_model.predict(X_test)\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(y_test, label='Actual')\n",
    "plt.plot(predictions, label='Predicted')\n",
    "plt.title('E-Scooter Demand Forecast')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}