Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "# import gdown\n",
    "# gdown.download_folder('https://drive.google.com/drive/folders/1fBs3klrABIbNxvReGZ96lLdOnLTRYEZ5?usp=sharing')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "# !curl --remote-name https://raw.githubusercontent.com/aymanmostafa11/Taxi-Ride-Fare-Prediction/main/helpers.py"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn import linear_model\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from helpers import Model , PreProcessing\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "RANDOM_STATE = 42"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "dataPath = 'taxi'"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "taxiRides = pd.read_csv(dataPath + '/taxi-rides-classification.csv')\n",
    "weather = pd.read_csv(dataPath + '/weather.csv')\n",
    "\n",
    "# taxiRides = pd.read_csv('taxi/taxi-rides-classification.csv')\n",
    "# weather = pd.read_csv('taxi/weather.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Taxi Rides has 554456 Rows and 10 Columns\n"
     ]
    },
    {
     "data": {
      "text/plain": "   distance cab_type    time_stamp         destination  \\\n0      0.62     Uber  1.544770e+12            West End   \n1      2.27     Uber  1.543400e+12   Boston University   \n2      2.00     Lyft  1.544920e+12            Back Bay   \n3      3.98     Lyft  1.543620e+12  Financial District   \n4      1.49     Lyft  1.543580e+12            Back Bay   \n\n                    source  surge_multiplier  \\\n0         Haymarket Square               1.0   \n1              Beacon Hill               1.0   \n2         Haymarket Square               1.0   \n3  Northeastern University               1.0   \n4  Northeastern University               1.0   \n\n                                     id                            product_id  \\\n0  c1b4a572-24b2-484d-aac5-b4f97b790480  8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a   \n1  f9e7e7e6-b309-479b-9f90-bf26a1870c81  997acbb5-e102-41e1-b155-9df7de0a73f2   \n2  154e8438-46f5-4e5f-9872-15cc95e85eed                                  lyft   \n3  6bdc30a6-9a80-487b-914e-8d9b82ee4acb                             lyft_plus   \n4  0cb12fe9-cd72-4feb-b341-4a566b2e6643                                  lyft   \n\n       name RideCategory  \n0      Taxi      unknown  \n1  UberPool        cheap  \n2      Lyft     moderate  \n3   Lyft XL    expensive  \n4      Lyft        cheap  ",
      "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>distance</th>\n      <th>cab_type</th>\n      <th>time_stamp</th>\n      <th>destination</th>\n      <th>source</th>\n      <th>surge_multiplier</th>\n      <th>id</th>\n      <th>product_id</th>\n      <th>name</th>\n      <th>RideCategory</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.62</td>\n      <td>Uber</td>\n      <td>1.544770e+12</td>\n      <td>West End</td>\n      <td>Haymarket Square</td>\n      <td>1.0</td>\n      <td>c1b4a572-24b2-484d-aac5-b4f97b790480</td>\n      <td>8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a</td>\n      <td>Taxi</td>\n      <td>unknown</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2.27</td>\n      <td>Uber</td>\n      <td>1.543400e+12</td>\n      <td>Boston University</td>\n      <td>Beacon Hill</td>\n      <td>1.0</td>\n      <td>f9e7e7e6-b309-479b-9f90-bf26a1870c81</td>\n      <td>997acbb5-e102-41e1-b155-9df7de0a73f2</td>\n      <td>UberPool</td>\n      <td>cheap</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2.00</td>\n      <td>Lyft</td>\n      <td>1.544920e+12</td>\n      <td>Back Bay</td>\n      <td>Haymarket Square</td>\n      <td>1.0</td>\n      <td>154e8438-46f5-4e5f-9872-15cc95e85eed</td>\n      <td>lyft</td>\n      <td>Lyft</td>\n      <td>moderate</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>3.98</td>\n      <td>Lyft</td>\n      <td>1.543620e+12</td>\n      <td>Financial District</td>\n      <td>Northeastern University</td>\n      <td>1.0</td>\n      <td>6bdc30a6-9a80-487b-914e-8d9b82ee4acb</td>\n      <td>lyft_plus</td>\n      <td>Lyft XL</td>\n      <td>expensive</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>1.49</td>\n      <td>Lyft</td>\n      <td>1.543580e+12</td>\n      <td>Back Bay</td>\n      <td>Northeastern University</td>\n      <td>1.0</td>\n      <td>0cb12fe9-cd72-4feb-b341-4a566b2e6643</td>\n      <td>lyft</td>\n      <td>Lyft</td>\n      <td>cheap</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"Taxi Rides has {taxiRides.shape[0]} Rows and {taxiRides.shape[1]} Columns\")\n",
    "taxiRides.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weather has 6276 Rows and 8 Columns\n"
     ]
    },
    {
     "data": {
      "text/plain": "    temp            location  clouds  pressure    rain  time_stamp  humidity  \\\n0  42.42            Back Bay     1.0   1012.14  0.1228  1545003901      0.77   \n1  42.43         Beacon Hill     1.0   1012.15  0.1846  1545003901      0.76   \n2  42.50   Boston University     1.0   1012.15  0.1089  1545003901      0.76   \n3  42.11              Fenway     1.0   1012.13  0.0969  1545003901      0.77   \n4  43.13  Financial District     1.0   1012.14  0.1786  1545003901      0.75   \n\n    wind  \n0  11.25  \n1  11.32  \n2  11.07  \n3  11.09  \n4  11.49  ",
      "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>temp</th>\n      <th>location</th>\n      <th>clouds</th>\n      <th>pressure</th>\n      <th>rain</th>\n      <th>time_stamp</th>\n      <th>humidity</th>\n      <th>wind</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>42.42</td>\n      <td>Back Bay</td>\n      <td>1.0</td>\n      <td>1012.14</td>\n      <td>0.1228</td>\n      <td>1545003901</td>\n      <td>0.77</td>\n      <td>11.25</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>42.43</td>\n      <td>Beacon Hill</td>\n      <td>1.0</td>\n      <td>1012.15</td>\n      <td>0.1846</td>\n      <td>1545003901</td>\n      <td>0.76</td>\n      <td>11.32</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>42.50</td>\n      <td>Boston University</td>\n      <td>1.0</td>\n      <td>1012.15</td>\n      <td>0.1089</td>\n      <td>1545003901</td>\n      <td>0.76</td>\n      <td>11.07</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>42.11</td>\n      <td>Fenway</td>\n      <td>1.0</td>\n      <td>1012.13</td>\n      <td>0.0969</td>\n      <td>1545003901</td>\n      <td>0.77</td>\n      <td>11.09</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>43.13</td>\n      <td>Financial District</td>\n      <td>1.0</td>\n      <td>1012.14</td>\n      <td>0.1786</td>\n      <td>1545003901</td>\n      <td>0.75</td>\n      <td>11.49</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"Weather has {weather.shape[0]} Rows and {weather.shape[1]} Columns\")\n",
    "weather.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Data Cleaning"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Taxi Rides"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 554456 entries, 0 to 554455\n",
      "Data columns (total 10 columns):\n",
      "distance            554456 non-null float64\n",
      "cab_type            554456 non-null object\n",
      "time_stamp          554456 non-null float64\n",
      "destination         554456 non-null object\n",
      "source              554456 non-null object\n",
      "surge_multiplier    554456 non-null float64\n",
      "id                  554456 non-null object\n",
      "product_id          554456 non-null object\n",
      "name                554456 non-null object\n",
      "RideCategory        554456 non-null object\n",
      "dtypes: float64(3), object(7)\n",
      "memory usage: 42.3+ MB\n"
     ]
    }
   ],
   "source": [
    "taxiRides.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Null"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null Values in columns\n"
     ]
    },
    {
     "data": {
      "text/plain": "distance            0\ncab_type            0\ntime_stamp          0\ndestination         0\nsource              0\nsurge_multiplier    0\nid                  0\nproduct_id          0\nname                0\nRideCategory        0\ndtype: int64"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Null Values in columns\")\n",
    "taxiRides.isnull().sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## product_id and name"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value counts of 'product_id' feature\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "55c66225-fbe7-4fd5-9072-eab1ece5e23e    44204\n8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a    44135\n997acbb5-e102-41e1-b155-9df7de0a73f2    44108\n6d318bcc-22a3-4af6-bddd-b409bfce1546    44038\n9a0e7b09-b92b-4c41-9779-2ad22b4d779d    44024\n6f72dfc5-27f1-42e8-84db-ccc7a75f6969    43983\n6c84fd89-3f11-4782-9b50-97c468b19529    43977\nlyft_plus                               41142\nlyft_lux                                41041\nlyft                                    41015\nlyft_luxsuv                             40979\nlyft_premier                            40969\nlyft_line                               40841\nName: product_id, dtype: int64"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"Value counts of 'product_id' feature\\n\")\n",
    "taxiRides['product_id'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value counts of 'name' feature\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "UberX           44204\nTaxi            44135\nUberPool        44108\nBlack SUV       44038\nWAV             44024\nUberXL          43983\nBlack           43977\nLyft XL         41142\nLux Black       41041\nLyft            41015\nLux Black XL    40979\nLux             40969\nShared          40841\nName: name, dtype: int64"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"Value counts of 'name' feature\\n\")\n",
    "taxiRides['name'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "   distance cab_type    time_stamp         destination  \\\n0      0.62     Uber  1.544770e+12            West End   \n1      2.27     Uber  1.543400e+12   Boston University   \n2      2.00     Lyft  1.544920e+12            Back Bay   \n3      3.98     Lyft  1.543620e+12  Financial District   \n4      1.49     Lyft  1.543580e+12            Back Bay   \n\n                    source  surge_multiplier  \\\n0         Haymarket Square               1.0   \n1              Beacon Hill               1.0   \n2         Haymarket Square               1.0   \n3  Northeastern University               1.0   \n4  Northeastern University               1.0   \n\n                                     id                            product_id  \\\n0  c1b4a572-24b2-484d-aac5-b4f97b790480  8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a   \n1  f9e7e7e6-b309-479b-9f90-bf26a1870c81  997acbb5-e102-41e1-b155-9df7de0a73f2   \n2  154e8438-46f5-4e5f-9872-15cc95e85eed                                  lyft   \n3  6bdc30a6-9a80-487b-914e-8d9b82ee4acb                             lyft_plus   \n4  0cb12fe9-cd72-4feb-b341-4a566b2e6643                                  lyft   \n\n       name RideCategory  \n0      Taxi      unknown  \n1  UberPool        cheap  \n2      Lyft     moderate  \n3   Lyft XL    expensive  \n4      Lyft        cheap  ",
      "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>distance</th>\n      <th>cab_type</th>\n      <th>time_stamp</th>\n      <th>destination</th>\n      <th>source</th>\n      <th>surge_multiplier</th>\n      <th>id</th>\n      <th>product_id</th>\n      <th>name</th>\n      <th>RideCategory</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.62</td>\n      <td>Uber</td>\n      <td>1.544770e+12</td>\n      <td>West End</td>\n      <td>Haymarket Square</td>\n      <td>1.0</td>\n      <td>c1b4a572-24b2-484d-aac5-b4f97b790480</td>\n      <td>8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a</td>\n      <td>Taxi</td>\n      <td>unknown</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2.27</td>\n      <td>Uber</td>\n      <td>1.543400e+12</td>\n      <td>Boston University</td>\n      <td>Beacon Hill</td>\n      <td>1.0</td>\n      <td>f9e7e7e6-b309-479b-9f90-bf26a1870c81</td>\n      <td>997acbb5-e102-41e1-b155-9df7de0a73f2</td>\n      <td>UberPool</td>\n      <td>cheap</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2.00</td>\n      <td>Lyft</td>\n      <td>1.544920e+12</td>\n      <td>Back Bay</td>\n      <td>Haymarket Square</td>\n      <td>1.0</td>\n      <td>154e8438-46f5-4e5f-9872-15cc95e85eed</td>\n      <td>lyft</td>\n      <td>Lyft</td>\n      <td>moderate</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>3.98</td>\n      <td>Lyft</td>\n      <td>1.543620e+12</td>\n      <td>Financial District</td>\n      <td>Northeastern University</td>\n      <td>1.0</td>\n      <td>6bdc30a6-9a80-487b-914e-8d9b82ee4acb</td>\n      <td>lyft_plus</td>\n      <td>Lyft XL</td>\n      <td>expensive</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>1.49</td>\n      <td>Lyft</td>\n      <td>1.543580e+12</td>\n      <td>Back Bay</td>\n      <td>Northeastern University</td>\n      <td>1.0</td>\n      <td>0cb12fe9-cd72-4feb-b341-4a566b2e6643</td>\n      <td>lyft</td>\n      <td>Lyft</td>\n      <td>cheap</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxiRides.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "product_id and name represent the same feature so we can drop one of them"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Encoding Timestamps to date"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [],
   "source": [
    "weatherDate = pd.to_datetime(weather['time_stamp'], unit='s').apply(lambda x: x.strftime(('%Y-%m-%d')))\n",
    "taxiRidesDate = pd.to_datetime(taxiRides['time_stamp'], unit='ms').apply(lambda x: x.strftime(('%Y-%m-%d')))\n",
    "weather['date'] = weatherDate\n",
    "taxiRides['date'] = taxiRidesDate"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "    temp            location  clouds  pressure    rain  time_stamp  humidity  \\\n0  42.42            Back Bay     1.0   1012.14  0.1228  1545003901      0.77   \n1  42.43         Beacon Hill     1.0   1012.15  0.1846  1545003901      0.76   \n2  42.50   Boston University     1.0   1012.15  0.1089  1545003901      0.76   \n3  42.11              Fenway     1.0   1012.13  0.0969  1545003901      0.77   \n4  43.13  Financial District     1.0   1012.14  0.1786  1545003901      0.75   \n\n    wind        date  \n0  11.25  2018-12-16  \n1  11.32  2018-12-16  \n2  11.07  2018-12-16  \n3  11.09  2018-12-16  \n4  11.49  2018-12-16  ",
      "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>temp</th>\n      <th>location</th>\n      <th>clouds</th>\n      <th>pressure</th>\n      <th>rain</th>\n      <th>time_stamp</th>\n      <th>humidity</th>\n      <th>wind</th>\n      <th>date</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>42.42</td>\n      <td>Back Bay</td>\n      <td>1.0</td>\n      <td>1012.14</td>\n      <td>0.1228</td>\n      <td>1545003901</td>\n      <td>0.77</td>\n      <td>11.25</td>\n      <td>2018-12-16</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>42.43</td>\n      <td>Beacon Hill</td>\n      <td>1.0</td>\n      <td>1012.15</td>\n      <td>0.1846</td>\n      <td>1545003901</td>\n      <td>0.76</td>\n      <td>11.32</td>\n      <td>2018-12-16</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>42.50</td>\n      <td>Boston University</td>\n      <td>1.0</td>\n      <td>1012.15</td>\n      <td>0.1089</td>\n      <td>1545003901</td>\n      <td>0.76</td>\n      <td>11.07</td>\n      <td>2018-12-16</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>42.11</td>\n      <td>Fenway</td>\n      <td>1.0</td>\n      <td>1012.13</td>\n      <td>0.0969</td>\n      <td>1545003901</td>\n      <td>0.77</td>\n      <td>11.09</td>\n      <td>2018-12-16</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>43.13</td>\n      <td>Financial District</td>\n      <td>1.0</td>\n      <td>1012.14</td>\n      <td>0.1786</td>\n      <td>1545003901</td>\n      <td>0.75</td>\n      <td>11.49</td>\n      <td>2018-12-16</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Joining Dataframes based on date"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [
    "taxiRides.drop(['time_stamp'],axis = 1, inplace = True)\n",
    "weather.drop(['time_stamp'],axis = 1, inplace = True)\n",
    "\n",
    "mergedData = pd.merge(taxiRides,weather.drop_duplicates(subset=['date', 'location']), how = 'left', left_on=['date', 'source'], right_on=['date', 'location'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "distance                 0\ncab_type                 0\ndestination              0\nsource                   0\nsurge_multiplier         0\nid                       0\nproduct_id               0\nname                     0\nRideCategory             0\ndate                     0\ntemp                     0\nlocation                 0\nclouds                   0\npressure                 0\nrain                480515\nhumidity                 0\nwind                     0\ndtype: int64"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergedData.isnull().sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Rain Feature"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows with null rain value statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": "              temp       clouds    pressure  rain     humidity         wind\ncount  5382.000000  5382.000000  5382.00000   0.0  5382.000000  5382.000000\nmean     38.461557     0.633618  1008.92490   NaN     0.747293     6.375199\nstd       6.169892     0.312919    13.46695   NaN     0.124451     3.542713\nmin      19.620000     0.000000   988.25000   NaN     0.450000     0.290000\n25%      35.022500     0.400000   997.39000   NaN     0.660000     3.190000\n50%      39.360000     0.680000  1009.56000   NaN     0.730000     6.210000\n75%      41.470000     0.930000  1020.95000   NaN     0.850000     9.627500\nmax      55.410000     1.000000  1035.12000   NaN     0.990000    18.180000",
      "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>temp</th>\n      <th>clouds</th>\n      <th>pressure</th>\n      <th>rain</th>\n      <th>humidity</th>\n      <th>wind</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>count</td>\n      <td>5382.000000</td>\n      <td>5382.000000</td>\n      <td>5382.00000</td>\n      <td>0.0</td>\n      <td>5382.000000</td>\n      <td>5382.000000</td>\n    </tr>\n    <tr>\n      <td>mean</td>\n      <td>38.461557</td>\n      <td>0.633618</td>\n      <td>1008.92490</td>\n      <td>NaN</td>\n      <td>0.747293</td>\n      <td>6.375199</td>\n    </tr>\n    <tr>\n      <td>std</td>\n      <td>6.169892</td>\n      <td>0.312919</td>\n      <td>13.46695</td>\n      <td>NaN</td>\n      <td>0.124451</td>\n      <td>3.542713</td>\n    </tr>\n    <tr>\n      <td>min</td>\n      <td>19.620000</td>\n      <td>0.000000</td>\n      <td>988.25000</td>\n      <td>NaN</td>\n      <td>0.450000</td>\n      <td>0.290000</td>\n    </tr>\n    <tr>\n      <td>25%</td>\n      <td>35.022500</td>\n      <td>0.400000</td>\n      <td>997.39000</td>\n      <td>NaN</td>\n      <td>0.660000</td>\n      <td>3.190000</td>\n    </tr>\n    <tr>\n      <td>50%</td>\n      <td>39.360000</td>\n      <td>0.680000</td>\n      <td>1009.56000</td>\n      <td>NaN</td>\n      <td>0.730000</td>\n      <td>6.210000</td>\n    </tr>\n    <tr>\n      <td>75%</td>\n      <td>41.470000</td>\n      <td>0.930000</td>\n      <td>1020.95000</td>\n      <td>NaN</td>\n      <td>0.850000</td>\n      <td>9.627500</td>\n    </tr>\n    <tr>\n      <td>max</td>\n      <td>55.410000</td>\n      <td>1.000000</td>\n      <td>1035.12000</td>\n      <td>NaN</td>\n      <td>0.990000</td>\n      <td>18.180000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weather['rain'].isnull().sum() / weather['rain'].shape[0]\n",
    "weather['rain'].hist()\n",
    "#Does null values of rain revolve around certain values?\n",
    "print(\"Rows with null rain value statistics\")\n",
    "weather[weather['rain'].isnull()].describe()\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows with non-null rain value statistics\n"
     ]
    },
    {
     "data": {
      "text/plain": "             temp      clouds     pressure        rain    humidity        wind\ncount  894.000000  894.000000   894.000000  894.000000  894.000000  894.000000\nmean    42.876644    0.943624  1005.557405    0.057652    0.864474    9.377103\nstd      2.951239    0.146752     7.841618    0.100758    0.093957    3.066719\nmin     34.100000    0.100000   988.910000    0.000200    0.520000    1.740000\n25%     41.482500    0.970000   999.750000    0.004900    0.860000    7.660000\n50%     43.820000    1.000000  1005.945000    0.014850    0.900000    9.890000\n75%     44.510000    1.000000  1011.237500    0.060925    0.920000   11.187500\nmax     51.240000    1.000000  1021.790000    0.780700    0.980000   17.160000",
      "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>temp</th>\n      <th>clouds</th>\n      <th>pressure</th>\n      <th>rain</th>\n      <th>humidity</th>\n      <th>wind</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>count</td>\n      <td>894.000000</td>\n      <td>894.000000</td>\n      <td>894.000000</td>\n      <td>894.000000</td>\n      <td>894.000000</td>\n      <td>894.000000</td>\n    </tr>\n    <tr>\n      <td>mean</td>\n      <td>42.876644</td>\n      <td>0.943624</td>\n      <td>1005.557405</td>\n      <td>0.057652</td>\n      <td>0.864474</td>\n      <td>9.377103</td>\n    </tr>\n    <tr>\n      <td>std</td>\n      <td>2.951239</td>\n      <td>0.146752</td>\n      <td>7.841618</td>\n      <td>0.100758</td>\n      <td>0.093957</td>\n      <td>3.066719</td>\n    </tr>\n    <tr>\n      <td>min</td>\n      <td>34.100000</td>\n      <td>0.100000</td>\n      <td>988.910000</td>\n      <td>0.000200</td>\n      <td>0.520000</td>\n      <td>1.740000</td>\n    </tr>\n    <tr>\n      <td>25%</td>\n      <td>41.482500</td>\n      <td>0.970000</td>\n      <td>999.750000</td>\n      <td>0.004900</td>\n      <td>0.860000</td>\n      <td>7.660000</td>\n    </tr>\n    <tr>\n      <td>50%</td>\n      <td>43.820000</td>\n      <td>1.000000</td>\n      <td>1005.945000</td>\n      <td>0.014850</td>\n      <td>0.900000</td>\n      <td>9.890000</td>\n    </tr>\n    <tr>\n      <td>75%</td>\n      <td>44.510000</td>\n      <td>1.000000</td>\n      <td>1011.237500</td>\n      <td>0.060925</td>\n      <td>0.920000</td>\n      <td>11.187500</td>\n    </tr>\n    <tr>\n      <td>max</td>\n      <td>51.240000</td>\n      <td>1.000000</td>\n      <td>1021.790000</td>\n      <td>0.780700</td>\n      <td>0.980000</td>\n      <td>17.160000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Rows with non-null rain value statistics\")\n",
    "weather[weather['rain'].notna()].describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "0.0030    85\n0.0020    51\n0.0040    23\n0.0060    17\n0.0050    15\n          ..\n0.1370     1\n0.0664     1\n0.7222     1\n0.0223     1\n0.1561     1\nName: rain, Length: 437, dtype: int64"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather['rain'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values of 0 in the rain feature :0\n"
     ]
    }
   ],
   "source": [
    "print(f\"Values of 0 in the rain feature :{(weather['rain'] == 0).sum()}\")\n",
    "#Rain feature nulls could indicate no rain"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [],
   "source": [
    "weather['rain'].fillna(0,inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Data Visualization"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "taxiRides['cab_type'].hist()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1c0c7a79cc8>"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "taxiRides['surge_multiplier'].hist()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Preprocessing"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "   distance cab_type         destination                   source  \\\n0      0.62     Uber            West End         Haymarket Square   \n1      2.27     Uber   Boston University              Beacon Hill   \n2      2.00     Lyft            Back Bay         Haymarket Square   \n3      3.98     Lyft  Financial District  Northeastern University   \n4      1.49     Lyft            Back Bay  Northeastern University   \n\n   surge_multiplier                                    id  \\\n0               1.0  c1b4a572-24b2-484d-aac5-b4f97b790480   \n1               1.0  f9e7e7e6-b309-479b-9f90-bf26a1870c81   \n2               1.0  154e8438-46f5-4e5f-9872-15cc95e85eed   \n3               1.0  6bdc30a6-9a80-487b-914e-8d9b82ee4acb   \n4               1.0  0cb12fe9-cd72-4feb-b341-4a566b2e6643   \n\n                             product_id      name RideCategory        date  \\\n0  8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a      Taxi      unknown  2018-12-14   \n1  997acbb5-e102-41e1-b155-9df7de0a73f2  UberPool        cheap  2018-11-28   \n2                                  lyft      Lyft     moderate  2018-12-16   \n3                             lyft_plus   Lyft XL    expensive  2018-11-30   \n4                                  lyft      Lyft        cheap  2018-11-30   \n\n    temp                 location  clouds  pressure    rain  humidity   wind  \n0  27.34         Haymarket Square    0.15   1033.40     NaN      0.81   3.04  \n1  33.67              Beacon Hill    0.12    991.20     NaN      0.82   5.37  \n2  42.34         Haymarket Square    1.00   1012.15  0.2068      0.77  11.49  \n3  38.46  Northeastern University    0.96   1017.24     NaN      0.66   3.31  \n4  38.46  Northeastern University    0.96   1017.24     NaN      0.66   3.31  ",
      "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>distance</th>\n      <th>cab_type</th>\n      <th>destination</th>\n      <th>source</th>\n      <th>surge_multiplier</th>\n      <th>id</th>\n      <th>product_id</th>\n      <th>name</th>\n      <th>RideCategory</th>\n      <th>date</th>\n      <th>temp</th>\n      <th>location</th>\n      <th>clouds</th>\n      <th>pressure</th>\n      <th>rain</th>\n      <th>humidity</th>\n      <th>wind</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.62</td>\n      <td>Uber</td>\n      <td>West End</td>\n      <td>Haymarket Square</td>\n      <td>1.0</td>\n      <td>c1b4a572-24b2-484d-aac5-b4f97b790480</td>\n      <td>8cf7e821-f0d3-49c6-8eba-e679c0ebcf6a</td>\n      <td>Taxi</td>\n      <td>unknown</td>\n      <td>2018-12-14</td>\n      <td>27.34</td>\n      <td>Haymarket Square</td>\n      <td>0.15</td>\n      <td>1033.40</td>\n      <td>NaN</td>\n      <td>0.81</td>\n      <td>3.04</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2.27</td>\n      <td>Uber</td>\n      <td>Boston University</td>\n      <td>Beacon Hill</td>\n      <td>1.0</td>\n      <td>f9e7e7e6-b309-479b-9f90-bf26a1870c81</td>\n      <td>997acbb5-e102-41e1-b155-9df7de0a73f2</td>\n      <td>UberPool</td>\n      <td>cheap</td>\n      <td>2018-11-28</td>\n      <td>33.67</td>\n      <td>Beacon Hill</td>\n      <td>0.12</td>\n      <td>991.20</td>\n      <td>NaN</td>\n      <td>0.82</td>\n      <td>5.37</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2.00</td>\n      <td>Lyft</td>\n      <td>Back Bay</td>\n      <td>Haymarket Square</td>\n      <td>1.0</td>\n      <td>154e8438-46f5-4e5f-9872-15cc95e85eed</td>\n      <td>lyft</td>\n      <td>Lyft</td>\n      <td>moderate</td>\n      <td>2018-12-16</td>\n      <td>42.34</td>\n      <td>Haymarket Square</td>\n      <td>1.00</td>\n      <td>1012.15</td>\n      <td>0.2068</td>\n      <td>0.77</td>\n      <td>11.49</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>3.98</td>\n      <td>Lyft</td>\n      <td>Financial District</td>\n      <td>Northeastern University</td>\n      <td>1.0</td>\n      <td>6bdc30a6-9a80-487b-914e-8d9b82ee4acb</td>\n      <td>lyft_plus</td>\n      <td>Lyft XL</td>\n      <td>expensive</td>\n      <td>2018-11-30</td>\n      <td>38.46</td>\n      <td>Northeastern University</td>\n      <td>0.96</td>\n      <td>1017.24</td>\n      <td>NaN</td>\n      <td>0.66</td>\n      <td>3.31</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>1.49</td>\n      <td>Lyft</td>\n      <td>Back Bay</td>\n      <td>Northeastern University</td>\n      <td>1.0</td>\n      <td>0cb12fe9-cd72-4feb-b341-4a566b2e6643</td>\n      <td>lyft</td>\n      <td>Lyft</td>\n      <td>cheap</td>\n      <td>2018-11-30</td>\n      <td>38.46</td>\n      <td>Northeastern University</td>\n      <td>0.96</td>\n      <td>1017.24</td>\n      <td>NaN</td>\n      <td>0.66</td>\n      <td>3.31</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergedData.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [],
   "source": [
    "preProcessing = PreProcessing()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [],
   "source": [
    "columnsToDrop = ['id', 'date', 'product_id', 'location']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [],
   "source": [
    "mergedData.drop(columnsToDrop,axis = 1,inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "   distance cab_type         destination                   source  \\\n0      0.62     Uber            West End         Haymarket Square   \n1      2.27     Uber   Boston University              Beacon Hill   \n2      2.00     Lyft            Back Bay         Haymarket Square   \n3      3.98     Lyft  Financial District  Northeastern University   \n4      1.49     Lyft            Back Bay  Northeastern University   \n\n   surge_multiplier      name RideCategory   temp  clouds  pressure    rain  \\\n0               1.0      Taxi      unknown  27.34    0.15   1033.40     NaN   \n1               1.0  UberPool        cheap  33.67    0.12    991.20     NaN   \n2               1.0      Lyft     moderate  42.34    1.00   1012.15  0.2068   \n3               1.0   Lyft XL    expensive  38.46    0.96   1017.24     NaN   \n4               1.0      Lyft        cheap  38.46    0.96   1017.24     NaN   \n\n   humidity   wind  \n0      0.81   3.04  \n1      0.82   5.37  \n2      0.77  11.49  \n3      0.66   3.31  \n4      0.66   3.31  ",
      "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>distance</th>\n      <th>cab_type</th>\n      <th>destination</th>\n      <th>source</th>\n      <th>surge_multiplier</th>\n      <th>name</th>\n      <th>RideCategory</th>\n      <th>temp</th>\n      <th>clouds</th>\n      <th>pressure</th>\n      <th>rain</th>\n      <th>humidity</th>\n      <th>wind</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.62</td>\n      <td>Uber</td>\n      <td>West End</td>\n      <td>Haymarket Square</td>\n      <td>1.0</td>\n      <td>Taxi</td>\n      <td>unknown</td>\n      <td>27.34</td>\n      <td>0.15</td>\n      <td>1033.40</td>\n      <td>NaN</td>\n      <td>0.81</td>\n      <td>3.04</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2.27</td>\n      <td>Uber</td>\n      <td>Boston University</td>\n      <td>Beacon Hill</td>\n      <td>1.0</td>\n      <td>UberPool</td>\n      <td>cheap</td>\n      <td>33.67</td>\n      <td>0.12</td>\n      <td>991.20</td>\n      <td>NaN</td>\n      <td>0.82</td>\n      <td>5.37</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2.00</td>\n      <td>Lyft</td>\n      <td>Back Bay</td>\n      <td>Haymarket Square</td>\n      <td>1.0</td>\n      <td>Lyft</td>\n      <td>moderate</td>\n      <td>42.34</td>\n      <td>1.00</td>\n      <td>1012.15</td>\n      <td>0.2068</td>\n      <td>0.77</td>\n      <td>11.49</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>3.98</td>\n      <td>Lyft</td>\n      <td>Financial District</td>\n      <td>Northeastern University</td>\n      <td>1.0</td>\n      <td>Lyft XL</td>\n      <td>expensive</td>\n      <td>38.46</td>\n      <td>0.96</td>\n      <td>1017.24</td>\n      <td>NaN</td>\n      <td>0.66</td>\n      <td>3.31</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>1.49</td>\n      <td>Lyft</td>\n      <td>Back Bay</td>\n      <td>Northeastern University</td>\n      <td>1.0</td>\n      <td>Lyft</td>\n      <td>cheap</td>\n      <td>38.46</td>\n      <td>0.96</td>\n      <td>1017.24</td>\n      <td>NaN</td>\n      <td>0.66</td>\n      <td>3.31</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergedData.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "data": {
      "text/plain": "distance                 0\ncab_type                 0\ndestination              0\nsource                   0\nsurge_multiplier         0\nname                     0\nRideCategory             0\ntemp                     0\nclouds                   0\npressure                 0\nrain                480515\nhumidity                 0\nwind                     0\ndtype: int64"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergedData.isnull().sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Encoding"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "###  <i>name</i> Feature"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [],
   "source": [
    "preProcessing.encodeManually(mergedData['name'], PreProcessing.nameFeatureMap)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [],
   "source": [
    "preProcessing.encodeManually(mergedData['RideCategory'], PreProcessing.labelFeatureMap)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Other Features"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non Integer Columns : ['cab_type', 'destination', 'source']\n"
     ]
    }
   ],
   "source": [
    "nonIntegerColumns = [col for col in mergedData.columns if mergedData[col].dtypes == object]\n",
    "print(f\"Non Integer Columns : {nonIntegerColumns}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [],
   "source": [
    "preProcessing.encode(mergedData,nonIntegerColumns)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Rain Feature Engineering"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "count    73941.00000\nmean         0.08632\nstd          0.07043\nmin          0.00230\n25%          0.02990\n50%          0.03250\n75%          0.14280\nmax          0.20880\nName: rain, dtype: float64"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergedData['rain'].describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Referring to google:\n",
    "<blockquote>Light rainfall is considered <b>less than 0.10 inches</b> of rain per hour. Moderate rainfall measures <b>0.10 to 0.30 inches</b> of rain per hour. Heavy rainfall is more than <b>0.30 inches</b>\n",
    " of rain per hour.</blockquote>\n",
    "0 : no rain <br>\n",
    "1 : light rain <br>\n",
    "2 : mid rain <br>\n",
    "3 : heavy rain (doesn't exist in the data)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [],
   "source": [
    "mergedData['rainType'] = 0"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [],
   "source": [
    "mergedData['rainType'][(mergedData['rain'] > 0) & (mergedData['rain'] < 0.1)] = 1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [],
   "source": [
    "mergedData['rainType'][(mergedData['rain'] > 0.1) & (mergedData['rain'] < 0.3)] = 2"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "data": {
      "text/plain": "0    480515\n1     42966\n2     30975\nName: rainType, dtype: int64"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergedData['rainType'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Clouds engineering\n",
    "making the assumption that clouds are on normalized [Okta Scale](https://polarpedia.eu/en/okta-scale/) that means values less than 0.1 are sunny days"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [],
   "source": [
    "mergedData['sunnyDay'] = 0"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [],
   "source": [
    "mergedData['sunnyDay'][mergedData['clouds'] <= 0.1] = 1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [
    {
     "data": {
      "text/plain": "0    546501\n1      7955\nName: sunnyDay, dtype: int64"
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergedData['sunnyDay'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Correlation Before PCA"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1152x720 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16, 10))\n",
    "\n",
    "ax = sns.heatmap(mergedData.corr(), annot = True)\n",
    "ax.tick_params(axis = 'x', rotation = 60)\n",
    "ax.tick_params(axis = 'y', rotation = 0)\n",
    "# these two lines are meant to fix a bug in matplotlib's current version\n",
    "# where the graph top and bot rows are cut in half (I copied them from stackoverflow)\n",
    "bottom, top = ax.get_ylim()\n",
    "ax.set_ylim(bottom + 0.5, top - 0.5)\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Dimentionality Reduction"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [],
   "source": [
    "subsetOfData = mergedData[['temp','sunnyDay','rainType','wind','pressure','humidity']]\n",
    "mergedData.drop(['temp','clouds','sunnyDay','rainType','rain','wind','pressure','humidity'],axis=1,inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [],
   "source": [
    "lowerDimensionWeatherData =preProcessing.reduceDimentionsOf(subsetOfData)\n",
    "mergedData['weatherState'] = lowerDimensionWeatherData"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Correlation Before PCA"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 720x720 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 10))\n",
    "\n",
    "ax = sns.heatmap(mergedData.corr(), annot = True)\n",
    "ax.tick_params(axis = 'x', rotation = 60)\n",
    "ax.tick_params(axis = 'y', rotation = 0)\n",
    "# these two lines are meant to fix a bug in matplotlib's current version\n",
    "# where the graph top and bot rows are cut in half (I copied them from stackoverflow)\n",
    "bottom, top = ax.get_ylim()\n",
    "ax.set_ylim(bottom + 0.5, top - 0.5)\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Model"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from scipy.stats import uniform"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [],
   "source": [
    "dataFeatures = mergedData.drop(['RideCategory'],axis=1)\n",
    "dataLabel = mergedData['RideCategory']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [
    {
     "data": {
      "text/plain": "   distance  cab_type  destination  source  surge_multiplier  name  \\\n0      0.62         1           12       5               1.0     0   \n1      2.27         1            2       1               1.0     1   \n2      2.00         0            0       5               1.0     1   \n3      3.98         0            4       8               1.0     2   \n4      1.49         0            0       8               1.0     1   \n\n   weatherState  \n0     26.134730  \n1    -16.492402  \n2      2.092880  \n3      8.657758  \n4      8.657758  ",
      "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>distance</th>\n      <th>cab_type</th>\n      <th>destination</th>\n      <th>source</th>\n      <th>surge_multiplier</th>\n      <th>name</th>\n      <th>weatherState</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.62</td>\n      <td>1</td>\n      <td>12</td>\n      <td>5</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>26.134730</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2.27</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>1</td>\n      <td>-16.492402</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2.00</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1.0</td>\n      <td>1</td>\n      <td>2.092880</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>3.98</td>\n      <td>0</td>\n      <td>4</td>\n      <td>8</td>\n      <td>1.0</td>\n      <td>2</td>\n      <td>8.657758</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>1.49</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8</td>\n      <td>1.0</td>\n      <td>1</td>\n      <td>8.657758</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFeatures.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [],
   "source": [
    "model = Model()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [],
   "source": [
    "splitData = model.splitData(dataFeatures,dataLabel)\n",
    "preProcessing.scale(splitData['trainFeatures'],'minMax',splitData['testFeatures'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Linear Logistic"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [],
   "source": [
    "# searchParameters = {'solver' : ['lbfgs', 'sag'],\n",
    "#                     'penalty': ['l2', 'none'],\n",
    "#                     'C' : uniform(loc=0, scale=10)}\n",
    "# logisticParamSearch = RandomizedSearchCV(linear_model.LogisticRegression(), searchParameters, n_iter = 2)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [],
   "source": [
    "# logisticParamSearch.fit(splitData['trainFeatures'], splitData['trainLabel'])\n",
    "# logisticParamSearch.best_params_"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy : 0.8348603583699309\n",
      "Training f1_score : 0.834860358369931\n",
      "\n",
      "Test Accuracy : 0.8341539515925405\n",
      "Test f1_score : 0.8341539515925404\n"
     ]
    }
   ],
   "source": [
    "params = {'solver' : 'lbfgs',\n",
    "          'multi_class' : 'multinomial', \n",
    "          'C' : 0.47469656914931324,\n",
    "          'penalty' : 'none' ,\n",
    "          'random_state':RANDOM_STATE}\n",
    "logisticRegression = model.fitClassificationModel(linear_model.LogisticRegression,\n",
    "                                                 params,\n",
    "                                                 evaluationFunction= metrics.f1_score,\n",
    "                                                 dataDictionary= splitData)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Polynomial Logistic"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [],
   "source": [
    "polyDegree = 3"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "outputs": [],
   "source": [
    "polyFeaturesTrain  = model.changeDegreeOf(splitData['trainFeatures'], polyDegree)\n",
    "polyFeaturesValidation = model.changeDegreeOf(splitData['testFeatures'], polyDegree)\n",
    "\n",
    "polyData = splitData.copy()\n",
    "polyData['trainFeatures'] = polyFeaturesTrain\n",
    "polyData['testFeatures']  = polyFeaturesValidation"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy : 0.8533695250290826\n",
      "Training f1_score : 0.8533695250290825\n",
      "\n",
      "Test Accuracy : 0.8521624643797568\n",
      "Test f1_score : 0.8521624643797568\n"
     ]
    }
   ],
   "source": [
    "params = {'solver' : 'lbfgs',\n",
    "          'multi_class' : 'multinomial', \n",
    "          'C' : 0.47469656914931324,\n",
    "          'penalty' : 'none' ,\n",
    "          'random_state':RANDOM_STATE}\n",
    "polyModel = model.fitClassificationModel(linear_model.LogisticRegression,\n",
    "                                         params,\n",
    "                                         evaluationFunction= metrics.f1_score,\n",
    "                                         dataDictionary=polyData)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Decision Tree"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "outputs": [],
   "source": [
    "# searchParameters = {'criterion' : ['gini', 'entropy'],\n",
    "#                     'min_samples_split': [i for i in range(1, 1001, 100)],\n",
    "#                     'max_depth' : [i for i in range(10, 50, 5)]}\n",
    "# treeParamSearch = RandomizedSearchCV(DecisionTreeClassifier(), searchParameters, n_iter = 10)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "outputs": [],
   "source": [
    "# treeParamSearch.fit(dataFeatures, dataLabel)\n",
    "# treeParamSearch.best_params"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy : 0.8682151842800588\n",
      "Training f1_score : 0.8682151842800588\n",
      "\n",
      "Test Accuracy : 0.864417631569455\n",
      "Test f1_score : 0.864417631569455\n"
     ]
    }
   ],
   "source": [
    "params = {'criterion' : 'entropy', 'max_depth' : 30 ,'random_state' : RANDOM_STATE, 'min_samples_split' : 501}\n",
    "tree = model.fitClassificationModel(DecisionTreeClassifier,\n",
    "                                     params,\n",
    "                                     evaluationFunction= metrics.f1_score,\n",
    "                                     dataDictionary=splitData)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Random Forest"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "outputs": [],
   "source": [
    "searchParameters = {'criterion' : ['gini', 'entropy'],\n",
    "                    'min_samples_split': [i for i in range(1, 1001, 100)],\n",
    "                    'max_depth' : [i for i in range(10, 50, 5)],\n",
    "                    'n_estimators' : [i for i in range(10, 101, 10)]}\n",
    "forestParamSearch = RandomizedSearchCV(RandomForestClassifier(), searchParameters, n_iter = 2)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# forestParamSearch.fit(dataFeatures, dataLabel)\n",
    "# forestParamSearch.best_params_"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": true
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy : 0.8683527067119965\n",
      "Training f1_score : 0.8683527067119965\n",
      "\n",
      "Test Accuracy : 0.8646250405800238\n",
      "Test f1_score : 0.8646250405800238\n"
     ]
    }
   ],
   "source": [
    "params = {'criterion'  : 'entropy' , \n",
    "          'max_depth' : 30,\n",
    "          'min_samples_split' : 500,\n",
    "          'n_estimators' : 50, \n",
    "          'random_state' : RANDOM_STATE,}\n",
    "forest = model.fitClassificationModel(RandomForestClassifier,\n",
    "                                     params,\n",
    "                                     evaluationFunction= metrics.f1_score,\n",
    "                                     dataDictionary=splitData)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### SVM"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "outputs": [],
   "source": [
    "# params = {'C' : 10, 'random_state' : RANDOM_STATE}\n",
    "# svm = model.fitClassificationModel(SVC,\n",
    "#                                     params,\n",
    "#                                     evaluationFunction= metrics.f1_score,\n",
    "#                                     dataDictionary=splitScaledData)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Saving Encoders and Models"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    },
    "id": "TCWueuoSfpX2"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "models = {\n",
    "    'LogisticRegression':logisticRegression,\n",
    "    'PolynomialLogistic':polyModel,\n",
    "    'DecisionTree': tree,\n",
    "    'RandomForest': forest,\n",
    "    # 'SVM': svm\n",
    "}"
   ],
   "outputs": [],
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "id": "BEEH6Yb2fpX2"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "imputers = {}\n",
    "for feature in taxiRides.columns:\n",
    "    if taxiRides[feature].dtype == object:\n",
    "        imputers[feature]=taxiRides[feature].mode()[0]\n",
    "    else:\n",
    "        imputers[feature]=taxiRides[feature].mean()"
   ],
   "outputs": [],
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "id": "b5MaeV9xfpX3"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "classification_cache ={\n",
    "    'models': models,\n",
    "    'encoders': preProcessing.encoders,\n",
    "    'scalers': preProcessing.scalingCache,\n",
    "    'imputers': imputers,\n",
    "    'categoricalFeaturesValues': preProcessing.featuresUniqueValues\n",
    "}"
   ],
   "outputs": [],
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "id": "apiPf5hWfpX3"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "from pickle import dump\n",
    "filename = 'classification_cache'\n",
    "dump(classification_cache, open('classification_cache', 'wb'))"
   ],
   "outputs": [],
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "id": "zBJElBR1fpX3"
   }
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Classification.ipynb",
   "provenance": []
  },
  "interpreter": {
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  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.9.5 64-bit"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
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 },
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