CabSpottingFinal-4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Business case:\n",
    "\n",
    "Use Case: For this data science challenge you are provided with a dataset containing mobility traces of ~500 taxi cabs in San Francisco collected over ~30 days. The format of each mobility trace file is the following - each line contains [latitude, longitude, occupancy, time], e.g.: [37.75134 -122.39488 0 1213084687], where latitude and longitude are in decimal degrees, occupancy shows if a cab has a fare (1 = occupied, 0 = free) and time is in UNIX epoch format.\n",
    "The goal of this data science challenge is twofold:\n",
    "1. To calculate the potential for yearly reduction on CO2 emissions, caused by the taxi cabs roaming without passengers. In your calculation please assume that the taxi cab fleet is changing at the rate of 10% per month (from combustion engine-powered vehicles to electric vehicles). Assume also that the average passenger vehicle emits about 404 grams of CO2 per mile.\n",
    "2. To build a predictor for taxi drivers, predicting the next place a passenger will hail a cab.\n",
    "\n",
    "Bonus question:\n",
    "3. Identify clusters of taxi cabs that you find being relevant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Methodology"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before applying Data Science techniques, we can make use of various problem solving methodologies and their tools to decompose problems\n",
    "\n",
    "✓ Lean Six Sigma \n",
    "\n",
    "✓ TRIZ (Theory of Inventive Problem Solving) \n",
    "\n",
    "✓ CRISP-DM \n",
    "\n",
    "✓ Design Thinking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CRISP-DM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ftp://public.dhe.ibm.com/software/analytics/spss/documentation/modeler/18.0/en/ModelerCRISPDM.pdf\n",
    "\n",
    "CRISP-DM (CRoss Industry Process for Data Mining) is a process model that describes commonly used approaches that data mining experts use to tackle problems. It is a guide that allows data mining projects to be completed faster with higher quality and less resources. It is an evolutionary and iterative process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP 1 – BUSINESS UNDERSTANDING "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly an in-depth analysis of the business objectives and needs has to be done. Current situation must be accessed and from these insights, the goals of carrying out the processes must be defined. This should follow the setting up of a plan to proceed. In our case, our aim is finding yearly CO2 emission reduction, predicting for taxi drivers, predicting the next place a passenger will hail a cab and Identify clusters of taxi cabs that you find being relevant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1 - CO2 emission reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to find yearly CO2 emission reduction, we need to find distance via longitude and latitude in Miles. It should be in miles metric because in the question it is given by average passenger vehicle emits about 404 grams of CO2 per mile. I use only 10% of .txt files. Within those files I keept only occupancy set to zero. Because in the question asked that by the taxi cabs roaming without passengers. \n",
    "\n",
    "I calculate the distances of all drivers to have the total distance of interest for one month. I multiply the result by 12 and get the yearly distance of interest for the CO2 emissions\n",
    "Then, I assume that the taxi cab fleet is changing at the rate of 10% per month. This policy means that the number of taxis with combustion engines is reduced by 10% every month. Accordingly I assume that the distance for CO2 emissions is also reduced by 10% every month. Applying this policy for 12 months gives me the new distance for the CO2 emissions with EVs.\n",
    "\n",
    "Then, I take the two distances, divide them by 0.1. Because I only took 10% of the files for my calculation and thus approximate the total distance of all taxis. I get the CO2 reduction by multiplying the total distance with the number of CO2 grams per mile and comparing the difference in terms of percentage.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2 - Next place a passenger will hail a cab (Occupancy Model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To predict the next place a passenger will hail a cab, I divided the question in to two. First question is what the next place that cab will go is. Second question is predicting a customer hail the cab.\n",
    "\n",
    "In the first question, we can think it as time series problem. I build a model that predict the location in t by using as previous location in t-1, t-2 and t-3. First I create simple neural network model with one input layer, one hidden layer and one output layer for one taxi driver. Results are not good therefore I predict longitude and latitude separately with Gradient Boosting algorithm. In order to increase performance of GBM model, we can change the previous window and hyper-parameter tuning. Because of time constrains I wrote hyper-parameter code but not run it. LSTM could be also very useful for this assignment.\n",
    "\n",
    "In the second question, I build a Random Forest Classifier to predict whether next location is a pick-up point or not. I subset 10% of the total data and all data and using timestamp data I generate day of week, hour and time data. I think also adding holiday and weather data will be very useful to our model. So I added holiday data as a flag and retrain Random Forest Classifier model. There is only 3 holidays in the given period of time which are Memorial Day(26th of May), Independence Day(4th of July) and Labor Day(1st of September). Result of Holiday Flag added models accuracy more accurate than previous model.  Also precision of predicting hailing a cab is increase 1%. Therefore we can use holiday flag for our model and it gives other data such as weather will be valuable.\n",
    "\n",
    "Notes: I do not do hyper-parameter optimization because of the time constrain. Other algorithms logistic regression, SVM, XgBoost, light GBM etc. could have been used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding next pick-up point (Final Occupancy Model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get the next pick-up location by combining the next location predictor for each taxi driver and the occupancy classifier.\n",
    "\n",
    "The next location prediction coordinates can be fed into the pick-up point classifier which will indicate the next pick-up location with a certain probability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus Question - Identify clusters of taxi cabs that you find being relevant. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I apply two different method to identify cluster of taxi cabs.\n",
    "\n",
    "First is using DBSCAN (Density-Based Spatial Clustering of Applications with Noise) that is a popular unsupervised learning method utilized in model building and machine learning algorithms. I subset 10% of the total data and divided into two in terms of occupancy flag. I looked other cabs where they are driving in the past and where they will go after it. By doing so, I clustered its cabs together based on similarity of behavior.\n",
    "\n",
    "My second method is implementing RFM methodology in to this question. I subset 10% of the total data and I found total miles per cab, total occupied miles per cab and average active minutes per day. Using mindset of RFM, I split the data into 4 in terms of quantile. Using this quantiles, I create 7 sample of segments. I gave the definition of segments in the coding phase. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP 2 – DATA UNDERSTANDING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have mobility traces of ~500 taxi cabs in San Francisco collected over ~30 days. The format of each mobility trace file is the following - each line contains [latitude, longitude, occupancy, time] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import datetime\n",
    "from os import listdir\n",
    "from os.path import join as jp\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "from pandas import Series\n",
    "import matplotlib.pyplot as plt\n",
    "from math import radians, degrees, sin, cos, asin, acos, sqrt\n",
    "import tensorflow\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Input, Activation, BatchNormalization, Dropout\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/mustafaozturk'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os \n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All files are 537\n",
      "\n",
      "53 randomly selected files\n"
     ]
    }
   ],
   "source": [
    "path = '/Users/mustafaozturk/Desktop/cases/PMI Case 2/cabspottingdata'\n",
    "\n",
    "all_files = [file_name for file_name in listdir(path) if file_name.endswith('.txt')]\n",
    "print(f'All files are {len(all_files)}')\n",
    "\n",
    "PerofFiles = 0.1\n",
    "\n",
    "random.Random(1923).shuffle(all_files)\n",
    "SelectedFiles = all_files[:int(PerofFiles * len(all_files))]\n",
    "\n",
    "print(f'\\n{len(SelectedFiles)} randomly selected files')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP 3 – DATA PREPERATION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most data used for data mining was originally collected and preserved for other purposes and needs some refinement before it is ready to use for modeling.\n",
    "\n",
    "The data preparation phase includes five tasks. These are\n",
    "\n",
    "Selecting data\n",
    "\n",
    "Cleaning data\n",
    "\n",
    "Constructing data\n",
    "\n",
    "Integrating data\n",
    "\n",
    "Formatting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ConverttoDF(file_name: str):\n",
    "    df = pd.read_csv(jp(path, file_name), sep=' ', header=None)\n",
    "    df.index = file_name.split('.')[0] + \"_\" + df.index.map(str)\n",
    "    df.columns = ['Latitude', 'Longitude', 'Occupancy', 'Timestamp']\n",
    "    df['Taxi'] = file_name.split('.')[0]\n",
    "    return df\n",
    "\n",
    "def PreviousCoordinates(df: DataFrame):\n",
    "    df['PrevLatitude'] = df.shift(1)['Latitude']\n",
    "    df['PrevLongitude'] = df.shift(1)['Longitude']\n",
    "    df = df.dropna()\n",
    "    return df\n",
    "\n",
    "def EstimatedDistance(row: Series):\n",
    "    # formulation of finding distance via longitude and latitude in Miles from\n",
    "    # https://gist.github.com/rochacbruno/2883505#gistcomment-1394026\n",
    "    Longitude = row['Longitude']\n",
    "    Latitude = row['Latitude']\n",
    "    PrevLongitude = row['PrevLongitude']\n",
    "    PrevLatitude = row['PrevLatitude']\n",
    "    Longitude, Latitude, PrevLongitude, PrevLatitude = map(radians, \n",
    "                                                                    [Longitude, Latitude, \n",
    "                                                                    PrevLongitude, PrevLatitude])\n",
    "    try:\n",
    "        return 3959 * (acos(sin(Latitude) * sin(PrevLatitude) + cos(Latitude) * cos(PrevLatitude) * \n",
    "                                cos(Longitude - PrevLongitude)))\n",
    "    except:\n",
    "        return 0.0\n",
    "\n",
    "def DistanceCalculation(df: DataFrame):\n",
    "    df['Miles'] = 0.0\n",
    "    df['Miles'] = df.apply(lambda row: EstimatedDistance(row), axis=1)\n",
    "    return df\n",
    "\n",
    "Data = pd.DataFrame()\n",
    "for f in SelectedFiles:\n",
    "    df = ConverttoDF(file_name=f)\n",
    "    df = PreviousCoordinates(df=df)\n",
    "    df = DistanceCalculation(df=df)\n",
    "    Data = pd.concat([Data, df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Occupancy</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Taxi</th>\n",
       "      <th>PrevLatitude</th>\n",
       "      <th>PrevLongitude</th>\n",
       "      <th>Miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>new_ajsnedsi_1</td>\n",
       "      <td>37.75124</td>\n",
       "      <td>-122.39527</td>\n",
       "      <td>0</td>\n",
       "      <td>1211022396</td>\n",
       "      <td>new_ajsnedsi</td>\n",
       "      <td>37.75167</td>\n",
       "      <td>-122.39460</td>\n",
       "      <td>0.047145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>new_ajsnedsi_2</td>\n",
       "      <td>37.75027</td>\n",
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       "      <td>0.067080</td>\n",
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       "      <td>new_ajsnedsi_3</td>\n",
       "      <td>37.75224</td>\n",
       "      <td>-122.39650</td>\n",
       "      <td>0</td>\n",
       "      <td>1211022516</td>\n",
       "      <td>new_ajsnedsi</td>\n",
       "      <td>37.75027</td>\n",
       "      <td>-122.39532</td>\n",
       "      <td>0.150617</td>\n",
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       "    <tr>\n",
       "      <td>new_ajsnedsi_4</td>\n",
       "      <td>37.75281</td>\n",
       "      <td>-122.39083</td>\n",
       "      <td>0</td>\n",
       "      <td>1211022577</td>\n",
       "      <td>new_ajsnedsi</td>\n",
       "      <td>37.75224</td>\n",
       "      <td>-122.39650</td>\n",
       "      <td>0.312262</td>\n",
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       "    <tr>\n",
       "      <td>new_ajsnedsi_5</td>\n",
       "      <td>37.76503</td>\n",
       "      <td>-122.39311</td>\n",
       "      <td>0</td>\n",
       "      <td>1211022637</td>\n",
       "      <td>new_ajsnedsi</td>\n",
       "      <td>37.75281</td>\n",
       "      <td>-122.39083</td>\n",
       "      <td>0.853509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                Latitude  Longitude  Occupancy   Timestamp          Taxi  \\\n",
       "new_ajsnedsi_1  37.75124 -122.39527          0  1211022396  new_ajsnedsi   \n",
       "new_ajsnedsi_2  37.75027 -122.39532          0  1211022456  new_ajsnedsi   \n",
       "new_ajsnedsi_3  37.75224 -122.39650          0  1211022516  new_ajsnedsi   \n",
       "new_ajsnedsi_4  37.75281 -122.39083          0  1211022577  new_ajsnedsi   \n",
       "new_ajsnedsi_5  37.76503 -122.39311          0  1211022637  new_ajsnedsi   \n",
       "\n",
       "                PrevLatitude  PrevLongitude     Miles  \n",
       "new_ajsnedsi_1      37.75167     -122.39460  0.047145  \n",
       "new_ajsnedsi_2      37.75124     -122.39527  0.067080  \n",
       "new_ajsnedsi_3      37.75027     -122.39532  0.150617  \n",
       "new_ajsnedsi_4      37.75224     -122.39650  0.312262  \n",
       "new_ajsnedsi_5      37.75281     -122.39083  0.853509  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using describe function, very fast data analysis can be done. \n",
    "\n",
    "When I checked the count field below, I can say that there is no nan value there are no outliers.\n",
    "\n",
    "By inspecting the data, I found that some coordinates are places in the Pacific ocean but we assume that this is a  GPS error. \n",
    "\n",
    "Maximum time is 06/10/2008 @ 9:09am (UTC) and minimum time is 05/17/2008 @ 10:00am (UTC)\n",
    "\n",
    "Occupancy and vacancy rate seems to be equally distributed. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Occupancy</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>PrevLatitude</th>\n",
       "      <th>PrevLongitude</th>\n",
       "      <th>Miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>1079188.00</td>\n",
       "      <td>1079188.00</td>\n",
       "      <td>1079188.00</td>\n",
       "      <td>1079188.00</td>\n",
       "      <td>1079188.00</td>\n",
       "      <td>1079188.00</td>\n",
       "      <td>1079188.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>37.76</td>\n",
       "      <td>-122.41</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1212048057.47</td>\n",
       "      <td>37.76</td>\n",
       "      <td>-122.41</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.50</td>\n",
       "      <td>588164.74</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>35.92</td>\n",
       "      <td>-122.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1211018413.00</td>\n",
       "      <td>35.92</td>\n",
       "      <td>-122.61</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>37.76</td>\n",
       "      <td>-122.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1211547971.00</td>\n",
       "      <td>37.76</td>\n",
       "      <td>-122.43</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>37.78</td>\n",
       "      <td>-122.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1212050459.50</td>\n",
       "      <td>37.78</td>\n",
       "      <td>-122.41</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>37.79</td>\n",
       "      <td>-122.40</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1212564613.25</td>\n",
       "      <td>37.79</td>\n",
       "      <td>-122.40</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>50.31</td>\n",
       "      <td>-122.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1213088997.00</td>\n",
       "      <td>50.31</td>\n",
       "      <td>-122.00</td>\n",
       "      <td>867.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Latitude  Longitude  Occupancy     Timestamp  PrevLatitude  \\\n",
       "count 1079188.00 1079188.00 1079188.00    1079188.00    1079188.00   \n",
       "mean       37.76    -122.41       0.45 1212048057.47         37.76   \n",
       "std         0.05       0.03       0.50     588164.74          0.05   \n",
       "min        35.92    -122.61       0.00 1211018413.00         35.92   \n",
       "25%        37.76    -122.43       0.00 1211547971.00         37.76   \n",
       "50%        37.78    -122.41       0.00 1212050459.50         37.78   \n",
       "75%        37.79    -122.40       1.00 1212564613.25         37.79   \n",
       "max        50.31    -122.00       1.00 1213088997.00         50.31   \n",
       "\n",
       "       PrevLongitude      Miles  \n",
       "count     1079188.00 1079188.00  \n",
       "mean         -122.41       0.33  \n",
       "std             0.03       1.47  \n",
       "min          -122.61       0.00  \n",
       "25%          -122.43       0.03  \n",
       "50%          -122.41       0.15  \n",
       "75%          -122.40       0.31  \n",
       "max          -122.00     867.64  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "Data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average vacant distance for each taxi driver that I select"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Taxi\n",
      "new_abmuyawm    2107.93\n",
      "new_acvebr      3865.09\n",
      "new_afweka      2363.08\n",
      "new_ajsnedsi    3065.84\n",
      "new_amknywr     2059.95\n",
      "new_ancedvab     779.90\n",
      "new_andyol      2324.05\n",
      "new_aniajcr     2299.33\n",
      "new_anupwr      1808.28\n",
      "new_artgagvu    2264.14\n",
      "new_asigno      2801.32\n",
      "new_atidfi      2697.00\n",
      "new_easnsvu     2724.33\n",
      "new_ecdiwovu    2639.48\n",
      "new_eglohabs    2327.33\n",
      "new_eicmynts    1541.58\n",
      "new_ejcrur      2129.92\n",
      "new_elswcky     2373.92\n",
      "new_eoffrij     2407.09\n",
      "new_eybyay      3067.37\n",
      "new_icedpa      1329.37\n",
      "new_ictlypt     1943.14\n",
      "new_idjoat       350.20\n",
      "new_idjojwa      432.45\n",
      "new_idodly      2453.26\n",
      "new_igglitby    3141.53\n",
      "new_ikujfurk    2029.61\n",
      "new_imhacy       488.72\n",
      "new_indtwrat    2752.54\n",
      "new_iofplik     2756.06\n",
      "new_iovhar      2590.61\n",
      "new_ipdraw       406.54\n",
      "new_mfeuer        16.90\n",
      "new_oasthul     1855.25\n",
      "new_obsimjio    2097.10\n",
      "new_ocwyit      1763.95\n",
      "new_ogikligu    2210.60\n",
      "new_ojvighta    2769.50\n",
      "new_oksbosfl    2288.03\n",
      "new_omdrid       703.10\n",
      "new_omluaj      2906.39\n",
      "new_ononma      2403.01\n",
      "new_ophugbe     2864.15\n",
      "new_osckro      2898.44\n",
      "new_osoccef     2811.96\n",
      "new_otvasdar    2591.74\n",
      "new_oydtesa     2267.14\n",
      "new_ucvepnuv    2362.86\n",
      "new_ugdifbl     1752.66\n",
      "new_ujtosh      1549.18\n",
      "new_ujtrud      2353.32\n",
      "new_upthin     52263.62\n",
      "new_uvreoipy    2882.19\n",
      "Name: Miles, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "NoPassanger = Data[Data['Occupancy'] == 0]\n",
    "#Distance Without Passanger\n",
    "DistanceWOPassanger = NoPassanger.groupby(by=['Taxi'])['Miles'].sum()\n",
    "print(DistanceWOPassanger)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP 4 – MODELLING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After data preparation, our data is already in good shape, and now you can search for useful patterns in your data.\n",
    "The modeling phase includes four tasks. These are\n",
    "\n",
    "Selecting modeling techniques\n",
    "\n",
    "Designing test(s)\n",
    "\n",
    "Building model(s)\n",
    "\n",
    "Assessing model(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1: CO2 Reduction Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The distance for CO2 in one year (combustion engine-powered vehicles) is approx 1967185.0 Miles\n",
      "\n",
      "The distance for CO2 after 0 month(s) of Electric Vehicles is approx 163932.056 Miles\n",
      "The distance for CO2 after 1 month(s) of Electric Vehicles is approx 147538.85 Miles\n",
      "The distance for CO2 after 2 month(s) of Electric Vehicles is approx 132784.965 Miles\n",
      "The distance for CO2 after 3 month(s) of Electric Vehicles is approx 119506.469 Miles\n",
      "The distance for CO2 after 4 month(s) of Electric Vehicles is approx 107555.822 Miles\n",
      "The distance for CO2 after 5 month(s) of Electric Vehicles is approx 96800.24 Miles\n",
      "The distance for CO2 after 6 month(s) of Electric Vehicles is approx 87120.216 Miles\n",
      "The distance for CO2 after 7 month(s) of Electric Vehicles is approx 78408.194 Miles\n",
      "The distance for CO2 after 8 month(s) of Electric Vehicles is approx 70567.375 Miles\n",
      "The distance for CO2 after 9 month(s) of Electric Vehicles is approx 63510.637 Miles\n",
      "The distance for CO2 after 10 month(s) of Electric Vehicles is approx 57159.574 Miles\n",
      "The distance for CO2 after 11 month(s) of Electric Vehicles is approx 51443.616 Miles\n",
      "\n",
      "The distance for CO2 after one year of Electric Vehicles  is approx 1176328.0 Miles\n"
     ]
    }
   ],
   "source": [
    "DistanceWOPassangerPerMonth = DistanceWOPassanger.sum()\n",
    "#We can assume that multiplying monthly distance by 12, we can find the yearly distance.\n",
    "DistanceWOPassangerPerYear = DistanceWOPassangerPerMonth * 12\n",
    "\n",
    "print(f'The distance for CO2 in one year (combustion engine-powered vehicles) '\n",
    "      f'is approx {round(DistanceWOPassangerPerYear)} Miles\\n')\n",
    "\n",
    "\n",
    "#electric vehicles\n",
    "DistanceWOPassangerPerYearEV = 0.0\n",
    "\n",
    "\n",
    "#assume that the taxi cab fleet is changing at the rate of 10% per month\n",
    "for month in range(12):\n",
    "    if month == 0:\n",
    "        DistanceWOPassangerPerMonthEV = DistanceWOPassangerPerMonth\n",
    "        DistanceWOPassangerPerYearEV = DistanceWOPassangerPerMonth\n",
    "    else:\n",
    "        DistanceWOPassangerPerMonthEV = DistanceWOPassangerPerMonthEV * 0.9\n",
    "        DistanceWOPassangerPerYearEV += DistanceWOPassangerPerMonthEV\n",
    "    print(f'The distance for CO2 after {month} month(s) of'\n",
    "          f' Electric Vehicles is approx {round(DistanceWOPassangerPerMonthEV, 3)} Miles')\n",
    "    \n",
    "print(f'\\nThe distance for CO2 after one year of Electric Vehicles '\n",
    "      f' is approx {round(DistanceWOPassangerPerYearEV)} Miles')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The CO2 emissions are reduced by 40.2 %\n"
     ]
    }
   ],
   "source": [
    "CO2GramsPerMiles = 404\n",
    "\n",
    "#CO2 emissions without electronic vehicles\n",
    "\n",
    "CO2EmissionWOEV = DistanceWOPassangerPerYear / PerofFiles * CO2GramsPerMiles\n",
    "\n",
    "#CO2 emissions with electronic vehicles\n",
    "\n",
    "CO2EmissionWEV = DistanceWOPassangerPerYearEV / PerofFiles * CO2GramsPerMiles\n",
    "\n",
    "CO2Reduction = round((CO2EmissionWOEV - CO2EmissionWEV) / CO2EmissionWOEV, 4)\n",
    "\n",
    "print(f'The CO2 emissions are reduced by {CO2Reduction * 100} %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2. To build a predictor for taxi drivers, predicting the next place a passenger will hail a cab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#San Francisco coordinate is 37.7749° N, 122.4194° W. \n",
    "#Therefore we can use 37 and 122 as offset value in order to normalized our data\n",
    "\n",
    "lat_offset = 37.0\n",
    "long_offset = -122.0\n",
    "taxi_driver = SelectedFiles[0]\n",
    "\n",
    "datapoints = []\n",
    "with open(file=jp(path, taxi_driver), mode='r') as f:\n",
    "    for line in f:\n",
    "        lat, long, occ, ts = line.split()\n",
    "        datapoints.append([float(lat) - lat_offset, float(long) - long_offset])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "TaxiDrivers = SelectedFiles[0]\n",
    "\n",
    "Data = []\n",
    "with open(file=jp(path, TaxiDrivers), mode='r') as f:\n",
    "     for line in f:\n",
    "        Latitude, Longitute, Occupation, TS = line.split()\n",
    "        Data.append([Latitude, Longitute])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to create a new dataset as a timeseries mindset, I follow below steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = []\n",
    "Y = []\n",
    "timeseries = 3\n",
    "for x in range(len(Data) - timeseries):\n",
    "    TimeSteps = Data[x:x + timeseries]\n",
    "    X.append([TS for TimeStep in TimeSteps for TS in TimeStep])\n",
    "    Y.append(Data[x + timeseries])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a timeseries approach so splitting train and test data should be first n% as train last (1-n) % as test. I select 80-20 split rule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "TraingTestSize = round(len(X) * 0.8)\n",
    "\n",
    "X_train = np.array(X[:TraingTestSize])\n",
    "X_test = np.array(X[TraingTestSize:])\n",
    "\n",
    "Y_train = np.array(Y[:TraingTestSize])\n",
    "Y_test = np.array(Y[TraingTestSize:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this step, I create neural network with one input layer, one hidden layer and one output layer. I follow MSE because this is a regression problem. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dense(4, input_shape=(6,)))\n",
    "model.add(Activation('tanh'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dense(2))\n",
    "model.add(Activation('linear'))\n",
    "model.compile(loss='mean_squared_error', optimizer='Adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 18555 samples, validate on 4639 samples\n",
      "Epoch 1/50\n",
      "18555/18555 [==============================] - 2s 114us/step - loss: 7936.4385 - val_loss: 7505.5439\n",
      "Epoch 2/50\n",
      "18555/18555 [==============================] - 2s 86us/step - loss: 6830.4515 - val_loss: 6069.6328\n",
      "Epoch 3/50\n",
      "18555/18555 [==============================] - 2s 84us/step - loss: 5242.4606 - val_loss: 4414.9308\n",
      "Epoch 4/50\n",
      "18555/18555 [==============================] - 1s 77us/step - loss: 3652.2173 - val_loss: 2934.3837\n",
      "Epoch 5/50\n",
      "18555/18555 [==============================] - 2s 86us/step - loss: 2320.3392 - val_loss: 1752.4020\n",
      "Epoch 6/50\n",
      "18555/18555 [==============================] - 1s 77us/step - loss: 1285.9346 - val_loss: 871.7949\n",
      "Epoch 7/50\n",
      "18555/18555 [==============================] - 1s 80us/step - loss: 573.6768 - val_loss: 330.7960\n",
      "Epoch 8/50\n",
      "18555/18555 [==============================] - 2s 90us/step - loss: 188.1949 - val_loss: 85.2234\n",
      "Epoch 9/50\n",
      "18555/18555 [==============================] - 2s 84us/step - loss: 40.1098 - val_loss: 12.6580\n",
      "Epoch 10/50\n",
      "18555/18555 [==============================] - 2s 84us/step - loss: 4.7369 - val_loss: 0.8890\n",
      "Epoch 11/50\n",
      "18555/18555 [==============================] - 2s 85us/step - loss: 0.2592 - val_loss: 0.0247\n",
      "Epoch 12/50\n",
      "18555/18555 [==============================] - 1s 80us/step - loss: 0.0069 - val_loss: 0.0018\n",
      "Epoch 13/50\n",
      "18555/18555 [==============================] - 2s 91us/step - loss: 0.0018 - val_loss: 0.0015\n",
      "Epoch 14/50\n",
      "18555/18555 [==============================] - 2s 101us/step - loss: 0.0017 - val_loss: 0.0014\n",
      "Epoch 15/50\n",
      "18555/18555 [==============================] - 2s 119us/step - loss: 0.0016 - val_loss: 0.0013\n",
      "Epoch 16/50\n",
      "18555/18555 [==============================] - 1s 79us/step - loss: 0.0016 - val_loss: 0.0013\n",
      "Epoch 17/50\n",
      "18555/18555 [==============================] - 2s 82us/step - loss: 0.0016 - val_loss: 0.0012\n",
      "Epoch 18/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0012\n",
      "Epoch 19/50\n",
      "18555/18555 [==============================] - 1s 72us/step - loss: 0.0016 - val_loss: 0.0014\n",
      "Epoch 20/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0013\n",
      "Epoch 21/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0013\n",
      "Epoch 22/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0014\n",
      "Epoch 23/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0012\n",
      "Epoch 24/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0013\n",
      "Epoch 25/50\n",
      "18555/18555 [==============================] - 1s 72us/step - loss: 0.0016 - val_loss: 0.0013\n",
      "Epoch 26/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0013\n",
      "Epoch 27/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0012\n",
      "Epoch 28/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0014\n",
      "Epoch 29/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0012\n",
      "Epoch 30/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0012\n",
      "Epoch 31/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0012\n",
      "Epoch 32/50\n",
      "18555/18555 [==============================] - 1s 70us/step - loss: 0.0016 - val_loss: 0.0013\n",
      "Epoch 33/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0013\n",
      "Epoch 34/50\n",
      "18555/18555 [==============================] - 1s 72us/step - loss: 0.0016 - val_loss: 0.0013\n",
      "Epoch 35/50\n",
      "18555/18555 [==============================] - 1s 72us/step - loss: 0.0016 - val_loss: 0.0012\n",
      "Epoch 36/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0012\n",
      "Epoch 37/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0012\n",
      "Epoch 38/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0014\n",
      "Epoch 39/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0014\n",
      "Epoch 40/50\n",
      "18555/18555 [==============================] - 1s 72us/step - loss: 0.0016 - val_loss: 0.0013\n",
      "Epoch 41/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0013\n",
      "Epoch 42/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0013\n",
      "Epoch 43/50\n",
      "18555/18555 [==============================] - 1s 71us/step - loss: 0.0016 - val_loss: 0.0012\n",
      "Epoch 44/50\n",
      "18555/18555 [==============================] - 2s 110us/step - loss: 0.0016 - val_loss: 0.0012\n",
      "Epoch 45/50\n",
      "18555/18555 [==============================] - 2s 116us/step - loss: 0.0016 - val_loss: 0.0012\n",
      "Epoch 46/50\n",
      "18555/18555 [==============================] - 2s 114us/step - loss: 0.0016 - val_loss: 0.0013\n",
      "Epoch 47/50\n",
      "18555/18555 [==============================] - 2s 132us/step - loss: 0.0016 - val_loss: 0.0012\n",
      "Epoch 48/50\n",
      "18555/18555 [==============================] - 2s 134us/step - loss: 0.0016 - val_loss: 0.0012\n",
      "Epoch 49/50\n",
      "18555/18555 [==============================] - 2s 119us/step - loss: 0.0016 - val_loss: 0.0018\n",
      "Epoch 50/50\n",
      "18555/18555 [==============================] - 2s 113us/step - loss: 0.0016 - val_loss: 0.0011\n",
      "MSE on training set 0.001610309757622663\n",
      "MSE on testing set 0.0011403552827822904\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, Y_train, epochs=50, validation_data=(X_test, Y_test), verbose=1)\n",
    "print(f'MSE on training set {history.history[\"loss\"][-1]}')\n",
    "print(f'MSE on testing set {history.history[\"val_loss\"][-1]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The neural network is trained for 50 epoches. Bellow, the training and testing losses are plotted. From the graph, we see that the network does not overfit. I exclude MSE of the first 15 epochs. Therefore, the MSE is already small and the difference between the train and test curve is visible. Otherwise, the loss at the first 15 epochs of the training is high and the two curves cannot be readable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots()\n",
    "ax.plot(history.history['loss'][15:])\n",
    "ax.plot(history.history['val_loss'][15:])\n",
    "plt.title('Model MSE')\n",
    "plt.ylabel('MSE')\n",
    "plt.xlabel('Epoch')\n",
    "ax.set_xticklabels(np.arange(5, 55, 5))\n",
    "plt.legend(['Train', 'Test'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2: Second Model for predicting next place (GBM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this model I try to predict Latitude and Longitude seperately. After predict the next Latitude and Longitude we can predict next coordinates. I use Gradient Boosting Modelling techniques. \n",
    "\n",
    "In order to understand the Gradient Boosting, first we have to understand what is boosting.\n",
    "\n",
    "Boosting: It is an approach where you take random samples of data, however the selection of sample is made more intelligently. We subsequently give more and more weight to hard to classify observations. \n",
    "\n",
    "So Gradient Boosting basically combines weak learners into a single strong learner in an iterative fashion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating Latitude "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "TaxiDrivers = SelectedFiles[0]\n",
    "\n",
    "DataLat = []\n",
    "with open(file=jp(path, TaxiDrivers), mode='r') as f:\n",
    "     for line in f:\n",
    "        Latitude, Longitute, Occupation, TS = line.split()\n",
    "        DataLat.append([Latitude])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "XLat = []\n",
    "YLat = []\n",
    "timeseries = 3\n",
    "for x in range(len(DataLat) - timeseries):\n",
    "    TimeSteps = DataLat[x:x + timeseries]\n",
    "    XLat.append([TS for TimeStep in TimeSteps for TS in TimeStep])\n",
    "    YLat.append(DataLat[x + timeseries])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "TraingTestSize = round(len(X) * 0.8)\n",
    "\n",
    "XLat_train = np.array(XLat[:TraingTestSize])\n",
    "XLat_test = np.array(XLat[TraingTestSize:])\n",
    "\n",
    "YLat_train = np.array(YLat[:TraingTestSize])\n",
    "YLat_test = np.array(YLat[TraingTestSize:])\n",
    "\n",
    "import numpy as np\n",
    "XLat_train=XLat_train.astype(np.float)\n",
    "XLat_test=XLat_test.astype(np.float)\n",
    "YLat_train=YLat_train.astype(np.float)\n",
    "YLat_test=YLat_test.astype(np.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mustafaozturk/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([37.79872136, 37.80018943, 37.80261972, ..., 37.71015898,\n",
       "       37.69753669, 37.67938896])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor \n",
    "gbmregressor=GradientBoostingRegressor(n_estimators=100) \n",
    "model_gbm_Lat=gbmregressor.fit(XLat_train, YLat_train) \n",
    "pred_gbm_Lat=model_gbm_Lat.predict(XLat_test) \n",
    "pred_gbm_Lat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Latitude Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "YLat_test_2=YLat_test.reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "actual_Lat=pd.Series(YLat_test_2)\n",
    "pred_Lat=pd.Series(pred_gbm_Lat).reset_index()\n",
    "result_Lat=pd.concat([actual_Lat,pred_Lat],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>actual_Lat</th>\n",
       "      <th>pred_Lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>37.80</td>\n",
       "      <td>37.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>37.80</td>\n",
       "      <td>37.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>37.80</td>\n",
       "      <td>37.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>37.80</td>\n",
       "      <td>37.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>37.80</td>\n",
       "      <td>37.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4634</td>\n",
       "      <td>37.73</td>\n",
       "      <td>37.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4635</td>\n",
       "      <td>37.72</td>\n",
       "      <td>37.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4636</td>\n",
       "      <td>37.70</td>\n",
       "      <td>37.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4637</td>\n",
       "      <td>37.69</td>\n",
       "      <td>37.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4638</td>\n",
       "      <td>37.68</td>\n",
       "      <td>37.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4639 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      actual_Lat  pred_Lat\n",
       "0          37.80     37.80\n",
       "1          37.80     37.80\n",
       "2          37.80     37.80\n",
       "3          37.80     37.80\n",
       "4          37.80     37.80\n",
       "...          ...       ...\n",
       "4634       37.73     37.74\n",
       "4635       37.72     37.73\n",
       "4636       37.70     37.71\n",
       "4637       37.69     37.70\n",
       "4638       37.68     37.68\n",
       "\n",
       "[4639 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_Lat.set_index(\"index\",inplace=True)\n",
    "result_Lat.index.name=None\n",
    "result_Lat.columns = ['actual_Lat', 'pred_Lat']\n",
    "result_Lat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_Lat=result_Lat.actual_Lat-result_Lat.pred_Lat\n",
    "\n",
    "R2_Lat=np.corrcoef(result_Lat.actual_Lat,result_Lat.pred_Lat)\n",
    "MSE_Lat=(error_Lat**2).mean()\n",
    "RMSE_Lat=(np.sqrt(MSE_Lat)).mean()\n",
    "MAE_Lat=(np.abs(error_Lat)).mean()\n",
    "MAPE_Lat=(np.abs(error_Lat)/result_Lat.actual_Lat).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[1.        , 0.99456466],\n",
       "        [0.99456466, 1.        ]]),\n",
       " 3.226979613204282e-05,\n",
       " 0.005680651030651576,\n",
       " 0.002505415378665047,\n",
       " 6.63957904858054e-05)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2_Lat,MSE_Lat,RMSE_Lat,MAE_Lat,MAPE_Lat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating Longitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "TaxiDrivers = SelectedFiles[0]\n",
    "\n",
    "DataLon = []\n",
    "with open(file=jp(path, TaxiDrivers), mode='r') as f:\n",
    "     for line in f:\n",
    "        Latitude, Longitute, Occupation, TS = line.split()\n",
    "        DataLon.append([Longitute])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "XLon = []\n",
    "YLon = []\n",
    "timeseries = 3\n",
    "for x in range(len(DataLon) - timeseries):\n",
    "    TimeSteps = DataLon[x:x + timeseries]\n",
    "    XLon.append([TS for TimeStep in TimeSteps for TS in TimeStep])\n",
    "    YLon.append(DataLon[x + timeseries])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "TraingTestSize = round(len(X) * 0.8)\n",
    "\n",
    "XLon_train = np.array(XLon[:TraingTestSize])\n",
    "XLon_test = np.array(XLon[TraingTestSize:])\n",
    "\n",
    "YLon_train = np.array(YLon[:TraingTestSize])\n",
    "YLon_test = np.array(YLon[TraingTestSize:])\n",
    "\n",
    "import numpy as np\n",
    "XLon_train=XLon_train.astype(np.float)\n",
    "XLon_test=XLon_test.astype(np.float)\n",
    "YLon_train=YLon_train.astype(np.float)\n",
    "YLon_test=YLon_test.astype(np.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mustafaozturk/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-122.40976308, -122.41442735, -122.41703373, ..., -122.39755676,\n",
       "       -122.39394557, -122.39130202])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor \n",
    "gbmregressor=GradientBoostingRegressor(n_estimators=100) \n",
    "model_gbm_Lon=gbmregressor.fit(XLon_train, YLon_train) \n",
    "pred_gbm_Lon=model_gbm_Lon.predict(XLon_test) \n",
    "pred_gbm_Lon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Longitude Model Evaluations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "YLon_test_2=YLon_test.reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "actual_Lon=pd.Series(YLon_test_2)\n",
    "pred_Lon=pd.Series(pred_gbm_Lon).reset_index()\n",
    "result_Lon=pd.concat([actual_Lon,pred_Lon],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>actual_Lon</th>\n",
       "      <th>pred_Lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-122.41</td>\n",
       "      <td>-122.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-122.42</td>\n",
       "      <td>-122.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-122.42</td>\n",
       "      <td>-122.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-122.43</td>\n",
       "      <td>-122.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-122.43</td>\n",
       "      <td>-122.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4634</td>\n",
       "      <td>-122.40</td>\n",
       "      <td>-122.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4635</td>\n",
       "      <td>-122.40</td>\n",
       "      <td>-122.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4636</td>\n",
       "      <td>-122.39</td>\n",
       "      <td>-122.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4637</td>\n",
       "      <td>-122.39</td>\n",
       "      <td>-122.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4638</td>\n",
       "      <td>-122.39</td>\n",
       "      <td>-122.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4639 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      actual_Lon  pred_Lon\n",
       "0        -122.41   -122.41\n",
       "1        -122.42   -122.41\n",
       "2        -122.42   -122.42\n",
       "3        -122.43   -122.42\n",
       "4        -122.43   -122.43\n",
       "...          ...       ...\n",
       "4634     -122.40   -122.41\n",
       "4635     -122.40   -122.40\n",
       "4636     -122.39   -122.40\n",
       "4637     -122.39   -122.39\n",
       "4638     -122.39   -122.39\n",
       "\n",
       "[4639 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_Lon.set_index(\"index\",inplace=True)\n",
    "result_Lon.index.name=None\n",
    "result_Lon.columns = ['actual_Lon', 'pred_Lon']\n",
    "result_Lon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_Lon=result_Lon.actual_Lon-result_Lon.pred_Lon\n",
    "\n",
    "R2_Lon=np.corrcoef(result_Lon.actual_Lon,result_Lon.pred_Lon)\n",
    "MSE_Lon=(error_Lon**2).mean()\n",
    "RMSE_Lon=(np.sqrt(MSE_Lon)).mean()\n",
    "MAE_Lon=(np.abs(error_Lon)).mean()\n",
    "MAPE_Lon=(np.abs(error_Lon)/result_Lon.actual_Lon).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[1.        , 0.99248799],\n",
       "        [0.99248799, 1.        ]]),\n",
       " 2.0495762688422413e-05,\n",
       " 0.004527224612102034,\n",
       " 0.0025994435437172855,\n",
       " -2.1235399519980533e-05)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2_Lon,MSE_Lon,RMSE_Lon,MAE_Lon,MAPE_Lon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_test_Final = np.concatenate([YLat_test, YLon_test], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "Pred_Final = np.concatenate([pred_gbm_Lat.reshape(-1,1), pred_gbm_Lon.reshape(-1,1)], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([37.79872136, 37.80018943, 37.80261972, ..., 37.71015898,\n",
       "       37.69753669, 37.67938896])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_gbm_Lat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP 5 – EVALUATION (NN and GBM Models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You’ll evaluate not just the models you create but also the process that you used to create them, and their potential for practical use.\n",
    "\n",
    "The evaluation phase includes three tasks. These are;\n",
    "\n",
    "Evaluating results\n",
    "\n",
    "Reviewing the process\n",
    "\n",
    "Determining the next steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 20))\n",
    "plt.subplot(421)\n",
    "plt.scatter(pd.to_numeric(Y_test[:,0]) + lat_offset, pd.to_numeric(Y_test[:,-1]) + long_offset)\n",
    "plt.title('NN True trajectory for {}'.format(taxi_driver.split('.')[0]))\n",
    "plt.ylabel('Latitude')\n",
    "plt.xlabel('Longitude')\n",
    " \n",
    "plt.subplot(422)\n",
    "plt.scatter(pd.to_numeric(Y_pred[:,0]) + lat_offset, pd.to_numeric(Y_pred[:,-1]) + long_offset)\n",
    "plt.title('NN Predicted trajectory {}'.format(taxi_driver.split('.')[0]))\n",
    "plt.ylabel('Latitude')\n",
    "plt.xlabel('Longitude')\n",
    " \n",
    "\n",
    "plt.subplot(423)\n",
    "#plt.scatter(pd.to_numeric(pred_gbm_Lat[:,0]) + lat_offset, pd.to_numeric(pred_gbm_Lat[:,-1]) + long_offset)\n",
    "plt.scatter(Pred_Final[:,0] + lat_offset, Pred_Final[:,-1] + long_offset)\n",
    "plt.title('GBM True trajectory {}'.format(taxi_driver.split('.')[0]))\n",
    "plt.ylabel('Latitude')\n",
    "plt.xlabel('Longitude')\n",
    " \n",
    " \n",
    "\n",
    "plt.subplot(424)\n",
    "#plt.scatter(pd.to_numeric(pred_gbm_Lat[:,0]) + lat_offset, pd.to_numeric(pred_gbm_Lat[:,-1]) + long_offset)\n",
    "plt.scatter(Pred_Final[:,0] + lat_offset, Pred_Final[:,-1] + long_offset)\n",
    "plt.title('GBM Predicted trajectory {}'.format(taxi_driver.split('.')[0]))\n",
    "plt.ylabel('Latitude')\n",
    "plt.xlabel('Longitude')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "TaxiDrivers = SelectedFiles[0]\n",
    "\n",
    "Time = []\n",
    "with open(file=jp(path, TaxiDrivers), mode='r') as f:\n",
    "     for line in f:\n",
    "        Latitude, Longitute, Occupation, TS = line.split()\n",
    "        Time.append([TS])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "XTime = []\n",
    "YTime = []\n",
    "timeseries = 3\n",
    "for x in range(len(Time) - timeseries):\n",
    "    TimeSteps = Time[x:x + timeseries]\n",
    "    XTime.append([TS for TimeStep in TimeSteps for TS in TimeStep])\n",
    "    YTime.append(Time[x + timeseries])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "TraingTestSize = round(len(X) * 0.8)\n",
    "\n",
    "YTime_test = np.array(YTime[TraingTestSize:])\n",
    "\n",
    "import numpy as np\n",
    "YTime_test=YTime_test.astype(np.float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I also plot in the y-axis as a first dimension is Latitude, as a secondary dimension is Longitude. In the x-axis, I add time. Therefore, we can follow minutes by minutes latitude and longitude change. As a result, prediction looks good!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Longitude')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(YTime_test, YLat_test, 'g-')\n",
    "ax2.plot(YTime_test, YLon_test, 'b-')\n",
    "\n",
    "ax1.set_xlabel('Time')\n",
    "ax1.set_ylabel('Latitude', color='g')\n",
    "ax2.set_ylabel('Longitude', color='b')\n",
    "\n",
    "fig1, ax3 = plt.subplots()\n",
    "\n",
    "\n",
    "ax4 = ax3.twinx()\n",
    "ax3.plot(YTime_test, pred_gbm_Lat, 'g-')\n",
    "ax4.plot(YTime_test, pred_gbm_Lon, 'b-')\n",
    "\n",
    "ax3.set_xlabel('Time')\n",
    "ax3.set_ylabel('Latitude', color='g')\n",
    "ax4.set_ylabel('Longitude', color='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE on testing set for NN model 0.0011403552827822904\n",
      "MSE on testing set for GBM Latitude model 3.226979613204282e-05\n",
      "MSE on testing set for GBM Longitude model 2.0495762688422413e-05\n"
     ]
    }
   ],
   "source": [
    "print(f'MSE on testing set for NN model {history.history[\"val_loss\"][-1]}')\n",
    "print(f'MSE on testing set for GBM Latitude model {MSE_Lat}')\n",
    "print(f'MSE on testing set for GBM Longitude model {MSE_Lon}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First column is exact route for new_ajsnedsi taxi. Second column is predictions of models Neural Network and Gradient Boosting respectively. As you can see in previous plot GBM results are much better than Neural Network model. Also, when we compare Mean Squared Error of NN and GBM's Longitude, Latitude models GBM models’ results are much better than NN model.\n",
    "\n",
    "Note: The mean squared error tells you how close a regression line is to a set of points. It does this by taking the distances from the points to the regression line (these distances are the “errors”) and squaring them. The squaring is necessary to remove any negative signs. It also gives more weight to larger differences. It’s called the mean squared error as you’re finding the average of a set of errors.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning (did not perform)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After selecting best algorithm, I am presenting with design choices as to how to define my model architecture. In order to find an optimal model architecture should be for a given model, I have to explore a range of possibilities. In true machine learning fashion, we'll ideally ask the machine to perform this exploration and select the optimal model architecture automatically. Parameters which define the model architecture are referred to as hyperparameters and thus this process of searching for the ideal model architecture is referred to as hyperparameter tuning. GridSearchCV is one of this approach.\n",
    "\n",
    "In GridSearchCV approach, machine learning model is evaluated for a range of hyperparameter values. This approach is called GridSearchCV, because it searches for best set of hyperparameters from a grid of hyperparameters values. This entire step is not perform because of time restiction. But I wrote coding steps of hyperparamet tuning below. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Longitude hyperparameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.model_selection import ShuffleSplit\n",
    "\n",
    "#from sklearn.model_selection import GridSearchCV\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#XLon_train=XLon_train.astype(np.float)\n",
    "\n",
    "#XLon_test=XLon_test.astype(np.float)\n",
    "\n",
    "#YLon_train=YLon_train.astype(np.float)\n",
    "\n",
    "#YLon_test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#def GradientBooster(param_grid, n_jobs):\n",
    "    \n",
    " #   estimator = GradientBoostingRegressor()\n",
    "    \n",
    "  #  cv = ShuffleSplit(XLon_train.shape[0], test_size=0.2)\n",
    "    \n",
    "    #classifier = GridSearchCV(estimator=estimator,cv=cv, param_grid=param_grid, n_jobs=n_jobs)\n",
    "    \n",
    "    #classifier.fit(XLon_train, YLon_train)\n",
    "    \n",
    "    #return cv, classifier.best_estimator_\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#param_grid={'n_estimators':[100], \n",
    "\n",
    "#           'learning_rate': [0.1, 0.05, 0.02, 0.01],\n",
    "            \n",
    "#          'max_depth':[6],\n",
    "            \n",
    "#          'min_samples_leaf':[3,5,9,17],\n",
    "            \n",
    "#         'max_features':[1.0,0.3,0.1]\n",
    "            \n",
    "#          }\n",
    "\n",
    "#n_jobs=4\n",
    "\n",
    "#cv,best_est=GradientBooster(param_grid, n_jobs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print \"Best Estimator Parameters\"\n",
    "\n",
    "#print\"---------------------------\"\n",
    "\n",
    "#print \"n_estimators: %d\" %best_est.n_estimators\n",
    "\n",
    "#print \"max_depth: %d\" %best_est.max_depth\n",
    "\n",
    "#print \"Learning Rate: %.1f\" %best_est.learning_rate\n",
    "\n",
    "#print \"min_samples_leaf: %d\" %best_est.min_samples_leaf\n",
    "\n",
    "#print \"max_features: %.1f\" %best_est.max_features\n",
    "\n",
    "#print \n",
    "\n",
    "#print \"Train R-squared: %.2f\" %best_est.score(XLon_train,YLon_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import numpy as np\n",
    "\n",
    "#import matplotlib.pyplot as plt\n",
    "\n",
    "#from sklearn import cross_validation\n",
    "\n",
    "#from sklearn.naive_bayes import GaussianNB \n",
    "\n",
    "#from sklearn.datasets import load_digits \n",
    "\n",
    "#from sklearn.learning_curve import learning_curve \n",
    "\n",
    "#def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, \n",
    "\n",
    "#        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "\n",
    "#    plt.figure() \n",
    "    \n",
    "#    plt.title(title) \n",
    "    \n",
    "#    if ylim is not None: \n",
    "    \n",
    "#        plt.ylim(*ylim) \n",
    "        \n",
    "#        plt.xlabel(\"Training examples\") \n",
    "        \n",
    "#        plt.ylabel(\"Score\") \n",
    "        \n",
    "#        train_sizes, \n",
    "        \n",
    "#        train_scores, \n",
    "        \n",
    "#        test_scores = learning_curve( estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes) \n",
    "        \n",
    "#        train_scores_mean = np.mean(train_scores, axis=1) \n",
    "        \n",
    "#        train_scores_std = np.std(train_scores, axis=1) \n",
    "        \n",
    "#        test_scores_mean = np.mean(test_scores, axis=1) \n",
    "        \n",
    "#        test_scores_std = np.std(test_scores, axis=1) \n",
    "        \n",
    "#        plt.grid() \n",
    "        \n",
    "#        plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\"r\") \n",
    "          \n",
    "#        plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color=\"g\") \n",
    "        \n",
    "#        plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Training score\") \n",
    "        \n",
    "#        plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Cross-validation score\") \n",
    "        \n",
    "#        plt.legend(loc=\"best\") \n",
    "        \n",
    "#        return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#title = \"Learning Curves (Gradient Boosted Regression Trees)\" \n",
    "\n",
    "#estimator = GradientBoostingRegressor(n_estimators=best_est.n_estimators, max_depth=best_est.max_depth, \n",
    "#                                      learning_rate=best_est.learning_rate, \n",
    "                                      \n",
    "#min_samples_leaf=best_est.min_samples_leaf, \n",
    "#                                      max_features=best_est.max_features)\n",
    "\n",
    "#plot_learning_curve(estimator, title, XLon_train, YLon_train, cv=cv, n_jobs=n_jobs)\n",
    "\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Switching back to the best model from gridsearch \n",
    "#estimator = best_est \n",
    "##Re-fitting to the train set \n",
    "#estimator.fit(XLon_train, YLon_train) \n",
    "##Calculating train/test scores - R-squared value \n",
    "#print \"Train R-squared: %.2f\" %estimator.score(XLon_train, YLon_train) \n",
    "#print \"Test R-squared: %.2f\" %estimator.score(XLon_test, YLon_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#def GradientBoosterLat(param_grid, n_jobs):\n",
    "    \n",
    "#    estimator = GradientBoostingRegressor()\n",
    "    \n",
    "#    cv = ShuffleSplit(XLat_train.shape[0], test_size=0.2)\n",
    "    \n",
    "#    classifier = GridSearchCV(estimator=estimator,cv=cv, param_grid=param_grid, n_jobs=n_jobs)\n",
    "    \n",
    "#    classifier.fit(XLat_train, YLat_train)\n",
    "    \n",
    "#    return cv, classifier.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#param_grid={'n_estimators':[100], \n",
    "#            'learning_rate': [0.1, 0.05, 0.02, 0.01],\n",
    "#            'max_depth':[6],\n",
    "#            'min_samples_leaf':[3,5,9,17],\n",
    "#            'max_features':[1.0,0.3,0.1]\n",
    "#            }\n",
    "#n_jobs=4\n",
    "#cv,best_est=GradientBoosterLat(param_grid, n_jobs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print \"Best Estimator Parameters\"\n",
    "\n",
    "#print\"---------------------------\"\n",
    "#print \"n_estimators: %d\" %best_est.n_estimators\n",
    "#print \"max_depth: %d\" %best_est.max_depth\n",
    "#print \"Learning Rate: %.1f\" %best_est.learning_rate\n",
    "#print \"min_samples_leaf: %d\" %best_est.min_samples_leaf\n",
    "#print \"max_features: %.1f\" %best_est.max_features\n",
    "#print \n",
    "#print \"Train R-squared: %.2f\" %best_est.score(XLon_train,YLon_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP 4 – MODELLING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling Occupancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "def DateExtraction(row: Series):\n",
    "    dt = datetime.datetime.fromtimestamp(row['Timestamp'])\n",
    "    row['day'] = dt.weekday()\n",
    "    row['hour'] = dt.hour\n",
    "    row['minute'] = dt.minute\n",
    "    row['monthday'] = dt.day\n",
    "    return row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame()\n",
    "for f in SelectedFiles:\n",
    "    df = ConverttoDF(file_name=f)\n",
    "    df = PreviousCoordinates(df=df)\n",
    "    df = DistanceCalculation(df=df)\n",
    "    data = pd.concat([data, df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Occupancy</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>monthday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>new_ajsnedsi_1</td>\n",
       "      <td>37.75</td>\n",
       "      <td>-122.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1211022396.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>new_ajsnedsi_2</td>\n",
       "      <td>37.75</td>\n",
       "      <td>-122.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1211022456.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>new_ajsnedsi_3</td>\n",
       "      <td>37.75</td>\n",
       "      <td>-122.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1211022516.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>new_ajsnedsi_4</td>\n",
       "      <td>37.75</td>\n",
       "      <td>-122.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1211022577.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>new_ajsnedsi_5</td>\n",
       "      <td>37.77</td>\n",
       "      <td>-122.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1211022637.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Latitude  Longitude  Occupancy     Timestamp  day  hour  \\\n",
       "new_ajsnedsi_1     37.75    -122.40       0.00 1211022396.00 5.00 14.00   \n",
       "new_ajsnedsi_2     37.75    -122.40       0.00 1211022456.00 5.00 14.00   \n",
       "new_ajsnedsi_3     37.75    -122.40       0.00 1211022516.00 5.00 14.00   \n",
       "new_ajsnedsi_4     37.75    -122.39       0.00 1211022577.00 5.00 14.00   \n",
       "new_ajsnedsi_5     37.77    -122.39       0.00 1211022637.00 5.00 14.00   \n",
       "\n",
       "                minute  monthday  \n",
       "new_ajsnedsi_1    6.00     17.00  \n",
       "new_ajsnedsi_2    7.00     17.00  \n",
       "new_ajsnedsi_3    8.00     17.00  \n",
       "new_ajsnedsi_4    9.00     17.00  \n",
       "new_ajsnedsi_5   10.00     17.00  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[['Latitude', 'Longitude', 'Occupancy', 'Timestamp']]\n",
    "data['day'] = 0\n",
    "data['hour'] = 0\n",
    "data['minute'] = 0\n",
    "data['monthday'] = 0\n",
    "data = data.apply(lambda row: DateExtraction(row), axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(labels=['Occupancy', 'Timestamp'], axis=1)\n",
    "X['Latitude'] = X['Latitude'] - lat_offset\n",
    "X['Longitude'] = X['Longitude'] - long_offset\n",
    "\n",
    "Y = data['Occupancy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, stratify=Y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Baseline Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I select a naive model that predicts always 0 for the next location. \n",
    "\n",
    "Naive model For naïve forecasts, we simply set all forecasts to be the value of the last observation.This method works remarkably well for many economic and financial time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The baseline accuracy of a naive model is 0.549\n"
     ]
    }
   ],
   "source": [
    "baseline_acc = round(Y_test[Y_test == 0].shape[0] / Y_test.shape[0], 3)\n",
    "print(f'The baseline accuracy of a naive model is {baseline_acc}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest Model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I also build Random Forests in order to predict if a datapoint is a pick-up location or not. RF outperforms the baseline approach significantly.\n",
    "\n",
    "Random forest, like its name implies, consists of a large number of individual decision trees that operate as an ensemble. Each individual tree in the random forest spits out a class prediction and the class with the most votes becomes our model’s prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy is 0.713\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=200, max_depth=20, random_state=0)\n",
    "clf.fit(X_train, Y_train)\n",
    "Y_pred = clf.predict(X_test)\n",
    "acc = round(accuracy_score(Y_test, Y_pred), 3)\n",
    "print(f'The accuracy is {acc}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Feature Importance "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also inspect and interpret the trained Random Forest classifier by analyzing the importance of each feature. Coordindates are the most important features to classify a pick-up point whereas the day feature does not help the classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = clf.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in clf.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Plot the feature importances of the forest\n",
    "plt.figure()\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(X.shape[1]), importances[indices],\n",
    "       color=\"r\", yerr=std[indices], align=\"center\")\n",
    "plt.xticks(range(X_train.shape[1]), [X_train.columns.tolist()[idx]  for idx in indices])\n",
    "plt.xlim([-1, X_train.shape[1]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding new variable - Holiday Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I think also adding holiday and weather data will be very useful to our model. So I added holiday data as a flag and retrain Random Forest Classifier model. There is only one holiday in the given period of time which is Memorial Day(26th of May) .Result of both models are same but precision of predicting hailing a cab is increase 1%. Therefore we can use holiday flag for our model and it gives other data such as weather will be valuable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[datetime.datetime(2008, 1, 1, 0, 0) datetime.datetime(2008, 1, 21, 0, 0)\n",
      " datetime.datetime(2008, 2, 18, 0, 0) datetime.datetime(2008, 5, 26, 0, 0)\n",
      " datetime.datetime(2008, 7, 4, 0, 0) datetime.datetime(2008, 9, 1, 0, 0)\n",
      " datetime.datetime(2008, 10, 13, 0, 0)\n",
      " datetime.datetime(2008, 11, 11, 0, 0)\n",
      " datetime.datetime(2008, 11, 27, 0, 0)\n",
      " datetime.datetime(2008, 12, 25, 0, 0)]\n"
     ]
    }
   ],
   "source": [
    "from pandas.tseries.holiday import USFederalHolidayCalendar\n",
    "cal = USFederalHolidayCalendar()\n",
    "holidays = cal.holidays(start='2008-01-01', end='2008-12-31').to_pydatetime()\n",
    "print(holidays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_new = data\n",
    "data_new['Holiday_Flag'] = [(lambda x: (x in holidays) * 1)(x) for x in data_new['Timestamp']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Latitude  Longitude  Occupancy     Timestamp  day  hour  \\\n",
      "new_ajsnedsi_1       37.75    -122.40       0.00 1211022396.00 5.00 14.00   \n",
      "new_ajsnedsi_2       37.75    -122.40       0.00 1211022456.00 5.00 14.00   \n",
      "new_ajsnedsi_3       37.75    -122.40       0.00 1211022516.00 5.00 14.00   \n",
      "new_ajsnedsi_4       37.75    -122.39       0.00 1211022577.00 5.00 14.00   \n",
      "new_ajsnedsi_5       37.77    -122.39       0.00 1211022637.00 5.00 14.00   \n",
      "...                    ...        ...        ...           ...  ...   ...   \n",
      "new_idjojwa_4991     37.75    -122.39       0.00 1213088788.00 1.00 12.00   \n",
      "new_idjojwa_4992     37.75    -122.39       0.00 1213088817.00 1.00 12.00   \n",
      "new_idjojwa_4993     37.75    -122.39       0.00 1213088877.00 1.00 12.00   \n",
      "new_idjojwa_4994     37.75    -122.39       0.00 1213088937.00 1.00 12.00   \n",
      "new_idjojwa_4995     37.75    -122.39       0.00 1213088997.00 1.00 12.00   \n",
      "\n",
      "                  minute  monthday  Holiday_Flag  \n",
      "new_ajsnedsi_1      6.00     17.00             0  \n",
      "new_ajsnedsi_2      7.00     17.00             0  \n",
      "new_ajsnedsi_3      8.00     17.00             0  \n",
      "new_ajsnedsi_4      9.00     17.00             0  \n",
      "new_ajsnedsi_5     10.00     17.00             0  \n",
      "...                  ...       ...           ...  \n",
      "new_idjojwa_4991    6.00     10.00             0  \n",
      "new_idjojwa_4992    6.00     10.00             0  \n",
      "new_idjojwa_4993    7.00     10.00             0  \n",
      "new_idjojwa_4994    8.00     10.00             0  \n",
      "new_idjojwa_4995    9.00     10.00             0  \n",
      "\n",
      "[1079188 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = data.drop(labels=['Occupancy', 'Timestamp'], axis=1)\n",
    "X_new['Latitude'] = X_new['Latitude'] - lat_offset\n",
    "X_new['Longitude'] = X_new['Longitude'] - long_offset\n",
    "\n",
    "Y_new = data_new['Occupancy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new_train, X_new_test, Y_new_train, Y_new_test = train_test_split(X_new, Y_new, stratify=Y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The baseline accuracy of a naive model is 0.549\n"
     ]
    }
   ],
   "source": [
    "baseline_acc_new = round(Y_new_test[Y_new_test == 0].shape[0] / Y_new_test.shape[0], 3)\n",
    "print(f'The baseline accuracy of a naive model is {baseline_acc_new}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy is 0.715\n"
     ]
    }
   ],
   "source": [
    "clf_new = RandomForestClassifier(n_estimators=200, max_depth=20, random_state=0)\n",
    "clf_new.fit(X_new_train, Y_new_train)\n",
    "Y_pred_new = clf_new.predict(X_new_test)\n",
    "acc_new = round(accuracy_score(Y_new_test, Y_pred_new), 3)\n",
    "print(f'The accuracy is {acc_new}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o9OgjIlouQR8R0XIJ+oiIlkvQR0S0XII+IqLlEvQRES2XoI+IaLkEfUREy+UHUxENzZ3sAiLWUnr0EREtl6CPiGi5BH1ERMsl6CMiWi5BHxHRcgn6iIiWS9BHRLRcgj4iouUS9BERLZegj4houUZBL+kQSTdJWiTp5C7LD5D0a0mrJB3RsexoSTeX29H9KjwiIprpGfSSpgCnA4cCM4GjJM3saHY7cAxwXse62wKnAPsB+wKnSNpm7GVHRERTTXr0+wKLbC+2/SBwPnB4vYHtJbavBR7uWPdg4FLbd9peCVwKHNKHuiMioqEmQb8jsLQ2vazMa6LRupKOlzQkaWjFihUNNx0REU00CXp1meeG22+0ru0zbQ/YHpg6dWrDTUdERBNNgn4ZsHNteidgecPtj2XdiIjogyZBPx+YIWlXSZsAs4A5Dbd/CXCQpG3KSdiDyryIiJggPYPe9irgRKqA/g3wbdsLJc2WdBiApH0kLQPeAJwhaWFZ907gI1QHi/nA7DIvIiImiOymw+0TY2BgwENDQ+O/I3U7fTDBej3360KNkDr7bR37m4t2kLTA9kC3ZfllbEREyyXoIyJaLkEfEdFyCfqIiJZL0EdEtFyCPiKi5RL0EREtl6CPiGi5BH1ERMsl6CMiWi5BHxHRcgn6iIiWS9BHRLRcgj4iouUS9BERLZegj2iZwcFBBgcHJ7uMWIck6CMiWi5BHxHRcgn6iIiWS9BHRLRcgj4iouUS9BERLbdRk0aSDgE+B0wBzrJ9WsfyTYGvAXsDfwKOtL1E0nTgN8BNpek82yf0p/SIlpImfzt2f2qIdULPoJc0BTgdeAWwDJgvaY7tG2rNjgNW2n6GpFnAx4Ejy7JbbO/Z57ojIqKhJkM3+wKLbC+2/SBwPnB4R5vDgXPL/YuAl0v96pZERMRYNAn6HYGltellZV7XNrZXAXcD25Vlu0q6StLPJL14jPVGRMQoNRmj79Yz7xzAG6nN74Bptv8kaW/gu5Kea/vPj1lZOh44HmDatGkNSoqIiKaa9OiXATvXpncClo/URtJGwFbAnbYfsP0nANsLgFuAZ3buwPaZtgdsD0ydOnX0jyIiIkbUJOjnAzMk7SppE2AWMKejzRzg6HL/COCnti1pajmZi6SnAzOAxf0pPSIimug5dGN7laQTgUuovl55tu2FkmYDQ7bnAF8Bvi5pEXAn1cEA4ABgtqRVwGrgBNt3jscDiYjK3MkuINY58jr2fdmBgQEPDQ2N/47WhS8F9Xru14UaIXX22/pQ5wTkwvCllOfOnTvu+9oQSFpge6DbsvwyNiKi5RL0EREtl6CPiGi5BH1ERMs1uqhZRMTj5OJr64306CMiWi5BHxHRcgn6iIiWS9BHRLRcgj4iouXyrZuImBRzJ7uADUh69BERLZegj4houQR9RETLJegjIlouQR8R0XIJ+oiIlkvQR0S0XII+IqLlEvQRES2XoI+IaLkEfUREyyXoIyJarlHQSzpE0k2SFkk6ucvyTSVdUJb/UtL02rL3l/k3STq4f6VHREQTPYNe0hTgdOBQYCZwlKSZHc2OA1bafgbwGeDjZd2ZwCzgucAhwBfL9iIiYoI06dHvCyyyvdj2g8D5wOEdbQ4Hzi33LwJeLkll/vm2H7B9K7CobC8iIiZIk+vR7wgsrU0vA/YbqY3tVZLuBrYr8+d1rLtj5w4kHQ8cXybvlXRTo+on1/bAHWPagtSfStYsdfbXhlHnxNQI60edY3/NJ8YuIy1oEvTdnkk3bNNkXWyfCZzZoJZ1hqQh2wOTXUcvqbO/Umd/rQ91rg819tJk6GYZsHNteidg+UhtJG0EbAXc2XDdiIgYR02Cfj4wQ9KukjahOrk6p6PNHODocv8I4Ke2XebPKt/K2RWYAfyqP6VHREQTPYduypj7icAlwBTgbNsLJc0GhmzPAb4CfF3SIqqe/Kyy7kJJ3wZuAFYB77C9epwey0RbX4aaUmd/pc7+Wh/qXB9qXCNVHe+IiGir/DI2IqLlEvQRES23QQW9pHtH0XZQ0n+uTZ8g6a3l/jGSdliL/S+RtP3a1rQ2JJ01/EtmSR9Yi/WnS7q+D3Uc1u3yGaNY/yRJTxprHSNsuy+PcTJJ2lrSP9SmByV9v8F6jdr1g6RTJb17IvYVj7VBBf0oDQKPBL3tL9n+Wpk8Bhh10E8G239n+4YyOeqg72Mdc2yfNoZNnASMS9CPh/I144m0NfAPPVutxzo7RaXD9YUe6zxycJE0W9KBXdr09WBX6loh6epy+1qZf46kI/q1n9HY4INe0mvKhdiukvRjSU8pF2U7Afgf5YV68fAbprxQA8A3y7In1nvqkgYkzS33t5P0o7LtM6j9gEzSmyX9CniipDPq1wCStIukn0i6tvw7rcw/R9LnJV0pafHwm0bSEyR9UdJCSd+XdHFt2dxS02llX1dL+mZnL7Y8tlPL/b0lXSPpF8A7am2mSPqkpPmltr8v86dLurF8eri+bP9ASVdIulnSvvU/yjU8jsf8wUn6QlnvnVQH1sskXVaWHSTpF5J+LelCSVuM8a0wRdKXy3P4o/K67ilpXnms/yppm/pzWu5vL2lJuX9MqeV7wI9G2lHD52tbSd8t+54naY+y7qmSzi41LC7PDcBpwG7l9f1kmbeFpIvKvr4pVT8jVXWRwhslXQ68vlbXvuU1uar8+6wy/+eS9qy1u2K4nl4kfVDVBQ1/DAxv723lPXSNpO9IepKkLSXdKmnj0uY/qfq72rjJfpqw/WHbP+7X9nq4wPae5fbWCdrniDb4oAcuB15gey+q6/i81/YS4EvAZ8oL9fPhxrYvAoaAN5Vl969h26cAl5dtzwGGA/s5wJHA/sD9wGrgTbX1vgB8zfYewDeBz9eWPQ14EfBqqj9uqP5YpwPPA/4OeGFnIbZPBu4vNb+pc3mHrwLvtN25neOAu23vA+wDvE3V7yMAngF8DtgDeDbwxlLnu+n+SaLb4+jK9uepfmj3UtsvVXVQ/RBwoO3nU70e7+rxmHqZAZxu+7nAXcDfAl8D3ldeh+uoXs9eXggcbftlPdr1er7+Cbiq7PsDpZZhzwYOprpu1CklDE8Gbimv73tKu72oPgnNBJ4O7C9pM+DLwGuAFwNPrW33RuCA8n79MPC/yvyzqD7FIumZwKa2r+31REjam+qr1ntRvUf3KYv+r+19bP8N8BvgONv3AHOBV5U2s4Dv2H6o137Kvrp2jjraPNKjnsiD3Qj1frgc7K6XdGbtILxPeQy/UNWp6suQYoK++rXuJZKuA95DdaXNfjkA+AaA7R8AK8v8lwN7U/0Y7Yll+um19V4InFfuf50qAIZ91/bDZTjmKWXei4ALy/zfA5etbcGStgK2tv2z2v6HHQS8VdLVwC+prmc0oyy71fZ1th8GFgI/KT+au47qINSp2+No6gVU4XVFqeVo1nCdj4ZutX11ub8A2I3HPg/nUr2evVxq+86G+1vT8/UiynNv+6fAduW1AfhBuVDgHcAfGfn5+5XtZWUfV5ftPrvs++ayv2/U2m8FXFjC5TM8+rdwIfDqckD5r8A5DR4fVAeSf7V9n+0/8+gPLXcvwXkdVQdneD9nAceW+8dSdTjqhj+RXl1e99m1ZWvqHD3GBB3sjqzVemyX5V8oB7vdqTLg1WX+V4ETSierb785muhxxHXRPwOftj1H0iBw6lpsYxWPHjQ361jW7YcKAs61/X5J99p+Vo/t17fxQMd26v+ORr1meLRu0b3m4WX/zfYlj5lZDXXV63q4Nv0w3d9n3R7HSDV1q+NS20eNsHxt1OtZTTXmPZI1vd5/WYv9dXu+VnVZZ/h16ax1pL/jkdqN9Pp+BLjM9uvKazoXwPZ9ki6luhrtf6Eaumyq277OAV5r+xpJx1CdD8P2FWVY6yXAFNudvdn7bdd71cfUankhj/bMvw58Yg01PXKwK9v5Bo9eVHEr4FxJM0rtw0NHFwL/KOk9NDvYXWD7xDUsf6mk91Kdd9oWWCjp58CWtq8sbc7j0QPAmKRHX72wvy33j67NvwfYcoR1OpctoeqhQ/WRf9j/pwzJSDoU2KbM/wlwhKQnl2XbSqr3SK+k/Lq4rH95j8dwOfC3qsbqn0L5w+niodqY5x+AJ6s6j7Ap5Q1l+y7gbknDnyLqwzyXAG+vjaM+U9LmPWobjduAmaoumbEV1SedYfXnfB7VMMQzSh1PKr2sfrobWCnpxWX6LcBw734Jj77e43Vyrf7eGQTuKL3ikazp/Vp3I7CrpN3KdP1gWf9bOKZjvbOoesnzG35igeoxvE7V+Y4tqXrQlDp/V95HncOIXwO+xeN786PV65egvQ52u1PVuxlUBzugfrA7b4T1eyqfKL4IHGH7eVSfLjZj7TpsjWxoQf8kSctqt3dR9eAvLEfT+qVIv0f1Jr269sc+7BzgS2XZE6nGUz9XtlH/uPVPwAGSfk017HE7QBmu+BDVCbvNgd8D82s1vRM4VtK1VAHz33s8ru9QXUDueuAMqmGVu7u0OxO4VtI3y9jn7NL2+1QBMOxY4HRVJ2Pr5yDOorqcxa/Lx/sz6OOnQttLgW8D11J9/L6qo/YfSrrM9gqqIPpWeY7mUfXS+u1o4JNlH3vy6FDBp6gOeFdSXcJ2PJwKDJR9n8ZjOyGPY/tPVENZ1+vRk7Hd2v2Vqvf6gzI+fVtt8SeA/y3pCqrLndTXWwD8mVEEsO1fAxdQDRt9Bxg+1/WPVO+7S3ns+w6q130bqrAfjdF0jibyYNfN8KfAO1R9ieAIANsrgXskvaAsn9Vt5bWRSyC0hKQtbN8raTuqC8ftX8brI8ZM1e9G5gLPLmP+47WfI4DDbb+ly7J7bW9Rmz4GGLB9YhlqOpvqwLsCONb27aq+SXav7U9JOgf4vu2LJB0CfJaqc3c5sLvtV0t6IdX5mBXAT4G32J5e2+eNwEm2/98aHsMjdXXMr+//o1RBvoTq//K4zfapkvaj6uH/her5PsD2/g2eujVK0LeEqq90bg1sAnzC9jmTWlC0hqofCn4MeJftC8dxP/9M9V+WvtL2v4/XftbWRBzshjts5f7JwNNs9/pE33u7CfqIiDWbwIPdkcD7qYZEbwOOKUOVY9tugj4iYvTK1yY7e9tX2H5Ht/aTKUEfEdFyG9q3biIiNjgJ+oiIlkvQR0S0XII+IqLl/gNqO8p9fgysagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = clf_new.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in clf_new.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Plot the feature importances of the forest\n",
    "plt.figure()\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(X_new.shape[1]), importances[indices],\n",
    "       color=\"r\", yerr=std[indices], align=\"center\")\n",
    "plt.xticks(range(X_new_train.shape[1]), [X_new_train.columns.tolist()[idx]  for idx in indices])\n",
    "plt.xlim([-1, X_new_train.shape[1]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Comparisons "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I use accuracy as a metric, since the problem seems to be balanced (55% of the labels are 0 and 45% of the labels are 1). In a scenario with higher imbalance I could have used F1 score. So formulas and meanings for accuracy, F1 score, etc. below\n",
    "\n",
    "\n",
    "Accuracy: The proportion of the total number of predictions that were correct.\n",
    "\n",
    "Positive Predictive Value (Precision): The proportion of positive cases correctly identified.\n",
    "\n",
    "Negative Predictive Value: The proportion of negative cases correctly identified.\n",
    "\n",
    "Sensitivity (Recall): The proportion of actual positive cases correctly identified.\n",
    "\n",
    "Specificity: The proportion of actual negative cases correctly identified.\n",
    "\n",
    "F1 Score= (2 * Precision * Recall) / (Precision + Recall)\n",
    "\n",
    "Kappa = (Observed Accuracy -Expected Accuracy) / (1 -Expected Accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix :\n",
      "[[84677 33834]\n",
      " [28152 69175]]\n",
      "Accuracy Score : 0.7128123870680788\n",
      "Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.75      0.71      0.73    118511\n",
      "         1.0       0.67      0.71      0.69     97327\n",
      "\n",
      "    accuracy                           0.71    215838\n",
      "   macro avg       0.71      0.71      0.71    215838\n",
      "weighted avg       0.71      0.71      0.71    215838\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix \n",
    "from sklearn.metrics import accuracy_score \n",
    "from sklearn.metrics import classification_report \n",
    " \n",
    "results = confusion_matrix(Y_test, Y_pred)\n",
    "print ('Confusion Matrix :')\n",
    "print(results) \n",
    "print ('Accuracy Score :',accuracy_score(Y_test, Y_pred) )\n",
    "print ('Report : ')\n",
    "print (classification_report(Y_test, Y_pred) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix :\n",
      "[[85320 33191]\n",
      " [28347 68980]]\n",
      "Accuracy Score : 0.7148880178652508\n",
      "Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.75      0.72      0.73    118511\n",
      "         1.0       0.68      0.71      0.69     97327\n",
      "\n",
      "    accuracy                           0.71    215838\n",
      "   macro avg       0.71      0.71      0.71    215838\n",
      "weighted avg       0.72      0.71      0.72    215838\n",
      "\n"
     ]
    }
   ],
   "source": [
    "results_new = confusion_matrix(Y_new_test, Y_pred_new) \n",
    "print ('Confusion Matrix :')\n",
    "print(results_new) \n",
    "print ('Accuracy Score :',accuracy_score(Y_new_test, Y_pred_new) )\n",
    "print ('Report : ')\n",
    "print (classification_report(Y_new_test, Y_pred_new) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Result of Holiday Flag added models accuracy more accurate than previous model.  Also precision of predicting hailing a cab is increase 1%. Therefore we can use holiday flag for our model and it gives other data such as weather will be valuable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP 6 – DEPLOYMENT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deployment is where data mining pays off. In this final phase of the Cross-Industry Standard Process for Data Mining (CRISP-DM) process, it doesn’t matter how brilliant your discoveries may be, or how perfectly your models fit the data, if you don’t actually use those things to improve the way that you do business.\n",
    "\n",
    "The deployment phase includes four tasks. These are;\n",
    "\n",
    "Planning deployment (your methods for integrating data-mining discoveries into use)\n",
    "\n",
    "Planning monitoring and maintenance\n",
    "\n",
    "Reporting final results\n",
    "\n",
    "Reviewing final results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bonus Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I apply two different method to identify cluster of taxi cabs.\n",
    "\n",
    "First is using DBSCAN (Density-Based Spatial Clustering of Applications with Noise) that is a popular unsupervised learning method utilized in model building and machine learning algorithms. I subset 10% of the total data and divided into two in terms of occupancy flag. I looked other cabs where they are driving in the past and where they will go after it. By doing so, I clustered its cabs together based on similarity of behavior.\n",
    "\n",
    "My second method is implementing RFM methodology in to this question. I subset 10% of the total data and I found total miles per cab, total occupied miles per cab and average active minutes per day. Using mindset of RFM, I split the data into 4 in terms of quantile. Using this quantiles, I create 7 sample of segments. I gave the definition of segments in the coding phase."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DBSCAN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_dbscan(db: DBSCAN, X: DataFrame):\n",
    "\n",
    "    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)\n",
    "    core_samples_mask[db.core_sample_indices_] = True\n",
    "\n",
    "    labels = db.labels_\n",
    "    \n",
    "    # Number of clusters in labels, ignoring noise if present.\n",
    "    n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "    n_noise_ = list(labels).count(-1)\n",
    "\n",
    "    print('Estimated number of clusters: %d' % n_clusters_)\n",
    "\n",
    "    # Black removed and is used for noise instead.\n",
    "    unique_labels = set(labels)\n",
    "    colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))]\n",
    "    for k, col in zip(unique_labels, colors):\n",
    "        if k == -1:\n",
    "            # Black used for noise.\n",
    "            col = [0, 0, 0, 1]\n",
    "            continue\n",
    "\n",
    "        class_member_mask = (labels == k)\n",
    "\n",
    "        xy = X[class_member_mask & core_samples_mask].values\n",
    "        plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col), markeredgecolor='k', markersize=14)\n",
    "\n",
    "        xy = X[class_member_mask & ~core_samples_mask].values\n",
    "        plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col), markeredgecolor='k', markersize=6)\n",
    "\n",
    "    plt.title('Estimated number of clusters: %d' % n_clusters_)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated number of clusters: 207\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sampled_data = data[data['Occupancy'] == 1][['Latitude', 'Longitude']].sample(frac=0.3, random_state=0)\n",
    "\n",
    "db = DBSCAN(eps=0.0005, min_samples=50)\n",
    "db.fit(sampled_data)\n",
    "visualize_dbscan(db=db, X=sampled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated number of clusters: 234\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sampled_data = data[data['Occupancy'] == 0][['Latitude', 'Longitude']].sample(frac=0.3, random_state=0)\n",
    "\n",
    "db = DBSCAN(eps=0.0005, min_samples=50)\n",
    "db.fit(sampled_data)\n",
    "visualize_dbscan(db=db, X=sampled_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Businesswise Segmentation (RFM Segmentation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When I think the data that I have, total miles per cab, total occupied miles per cab and average active minutes per day variable are more important than others. I extract those data below and create new dataset for RFM mindset segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "def DateExtraction(row: Series):\n",
    "    dt = datetime.datetime.fromtimestamp(row['Timestamp'])\n",
    "    row['day'] = dt.weekday()\n",
    "    row['hour'] = dt.hour\n",
    "    row['minute'] = dt.minute\n",
    "    row['monthday'] = dt.day\n",
    "    return row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Occupancy</th>\n",
       "      <th>Taxi</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>monthday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>new_ajsnedsi_1</td>\n",
       "      <td>37.75</td>\n",
       "      <td>-122.40</td>\n",
       "      <td>0</td>\n",
       "      <td>new_ajsnedsi</td>\n",
       "      <td>1211022396</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>new_ajsnedsi_2</td>\n",
       "      <td>37.75</td>\n",
       "      <td>-122.40</td>\n",
       "      <td>0</td>\n",
       "      <td>new_ajsnedsi</td>\n",
       "      <td>1211022456</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>new_ajsnedsi_3</td>\n",
       "      <td>37.75</td>\n",
       "      <td>-122.40</td>\n",
       "      <td>0</td>\n",
       "      <td>new_ajsnedsi</td>\n",
       "      <td>1211022516</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>new_ajsnedsi_4</td>\n",
       "      <td>37.75</td>\n",
       "      <td>-122.39</td>\n",
       "      <td>0</td>\n",
       "      <td>new_ajsnedsi</td>\n",
       "      <td>1211022577</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>new_ajsnedsi_5</td>\n",
       "      <td>37.77</td>\n",
       "      <td>-122.39</td>\n",
       "      <td>0</td>\n",
       "      <td>new_ajsnedsi</td>\n",
       "      <td>1211022637</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Latitude  Longitude  Occupancy          Taxi   Timestamp  day  \\\n",
       "new_ajsnedsi_1     37.75    -122.40          0  new_ajsnedsi  1211022396    5   \n",
       "new_ajsnedsi_2     37.75    -122.40          0  new_ajsnedsi  1211022456    5   \n",
       "new_ajsnedsi_3     37.75    -122.40          0  new_ajsnedsi  1211022516    5   \n",
       "new_ajsnedsi_4     37.75    -122.39          0  new_ajsnedsi  1211022577    5   \n",
       "new_ajsnedsi_5     37.77    -122.39          0  new_ajsnedsi  1211022637    5   \n",
       "\n",
       "                hour  minute  monthday  \n",
       "new_ajsnedsi_1    14       6        17  \n",
       "new_ajsnedsi_2    14       7        17  \n",
       "new_ajsnedsi_3    14       8        17  \n",
       "new_ajsnedsi_4    14       9        17  \n",
       "new_ajsnedsi_5    14      10        17  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame()\n",
    "for f in SelectedFiles:\n",
    "    df = ConverttoDF(file_name=f)\n",
    "    df = PreviousCoordinates(df=df)\n",
    "    df = DistanceCalculation(df=df)\n",
    "    data = pd.concat([data, df])\n",
    "data = data[['Latitude', 'Longitude', 'Occupancy', 'Taxi','Timestamp']]\n",
    "data['day'] = 0\n",
    "data['hour'] = 0\n",
    "data['minute'] = 0\n",
    "data['monthday'] = 0\n",
    "data = data.apply(lambda row: DateExtraction(row), axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "Segment = pd.DataFrame()\n",
    "for f in SelectedFiles:\n",
    "    df = ConverttoDF(file_name=f)\n",
    "    df = PreviousCoordinates(df=df)\n",
    "    df = DistanceCalculation(df=df)\n",
    "    Segment = pd.concat([Segment, df])\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Taxi\n",
      "new_abmuyawm     4945.10\n",
      "new_acvebr       6668.34\n",
      "new_afweka       5431.69\n",
      "new_ajsnedsi     6603.38\n",
      "new_amknywr      5606.30\n",
      "new_ancedvab     1822.48\n",
      "new_andyol       5451.43\n",
      "new_aniajcr      5326.07\n",
      "new_anupwr       4006.42\n",
      "new_artgagvu     5339.73\n",
      "new_asigno       6106.54\n",
      "new_atidfi       6433.05\n",
      "new_easnsvu      5812.89\n",
      "new_ecdiwovu     6169.27\n",
      "new_eglohabs     5371.02\n",
      "new_eicmynts     3423.72\n",
      "new_ejcrur       4703.90\n",
      "new_elswcky      5734.05\n",
      "new_eoffrij      5696.08\n",
      "new_eybyay       6651.21\n",
      "new_icedpa       3027.34\n",
      "new_ictlypt      4708.31\n",
      "new_idjoat        771.99\n",
      "new_idjojwa      1122.90\n",
      "new_idodly       6323.11\n",
      "new_igglitby     6704.11\n",
      "new_ikujfurk     4579.91\n",
      "new_imhacy       1147.65\n",
      "new_indtwrat     6153.95\n",
      "new_iofplik      5985.70\n",
      "new_iovhar       5933.90\n",
      "new_ipdraw        744.10\n",
      "new_mfeuer         25.98\n",
      "new_oasthul      4449.06\n",
      "new_obsimjio     5222.93\n",
      "new_ocwyit       4164.55\n",
      "new_ogikligu     5011.57\n",
      "new_ojvighta     6126.91\n",
      "new_oksbosfl     5938.80\n",
      "new_omdrid       1828.81\n",
      "new_omluaj       6294.80\n",
      "new_ononma       5405.28\n",
      "new_ophugbe      6936.31\n",
      "new_osckro       6325.60\n",
      "new_osoccef      6062.73\n",
      "new_otvasdar     5737.96\n",
      "new_oydtesa      5476.33\n",
      "new_ucvepnuv     5816.33\n",
      "new_ugdifbl      4144.39\n",
      "new_ujtosh       3482.12\n",
      "new_ujtrud       5425.04\n",
      "new_upthin     103063.49\n",
      "new_uvreoipy     6289.59\n",
      "Name: Miles, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "MileCover=pd.DataFrame()\n",
    "\n",
    "MileCover = Segment.groupby(by=['Taxi'])['Miles'].sum()\n",
    "\n",
    "print(MileCover)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "WPassangerDistance = Segment[Segment['Occupancy'] == 1]\n",
    "#Distance Without Passanger\n",
    "DistanceWPassanger=pd.DataFrame()\n",
    "DistanceWPassanger = WPassangerDistance.groupby(by=['Taxi'])['Miles'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Miles_x  Miles_y\n",
      "Taxi                           \n",
      "new_abmuyawm   4945.10  2837.17\n",
      "new_acvebr     6668.34  2803.25\n",
      "new_afweka     5431.69  3068.61\n",
      "new_ajsnedsi   6603.38  3537.55\n",
      "new_amknywr    5606.30  3546.35\n",
      "new_ancedvab   1822.48  1042.58\n",
      "new_andyol     5451.43  3127.38\n",
      "new_aniajcr    5326.07  3026.74\n",
      "new_anupwr     4006.42  2198.14\n",
      "new_artgagvu   5339.73  3075.60\n",
      "new_asigno     6106.54  3305.22\n",
      "new_atidfi     6433.05  3736.04\n",
      "new_easnsvu    5812.89  3088.56\n",
      "new_ecdiwovu   6169.27  3529.79\n",
      "new_eglohabs   5371.02  3043.68\n",
      "new_eicmynts   3423.72  1882.14\n",
      "new_ejcrur     4703.90  2573.98\n",
      "new_elswcky    5734.05  3360.13\n",
      "new_eoffrij    5696.08  3288.99\n",
      "new_eybyay     6651.21  3583.84\n",
      "new_icedpa     3027.34  1697.96\n",
      "new_ictlypt    4708.31  2765.17\n",
      "new_idjoat      771.99   421.79\n",
      "new_idjojwa    1122.90   690.45\n",
      "new_idodly     6323.11  3869.85\n",
      "new_igglitby   6704.11  3562.57\n",
      "new_ikujfurk   4579.91  2550.30\n",
      "new_imhacy     1147.65   658.93\n",
      "new_indtwrat   6153.95  3401.41\n",
      "new_iofplik    5985.70  3229.65\n",
      "new_iovhar     5933.90  3343.29\n",
      "new_ipdraw      744.10   337.56\n",
      "new_mfeuer       25.98     9.09\n",
      "new_oasthul    4449.06  2593.81\n",
      "new_obsimjio   5222.93  3125.83\n",
      "new_ocwyit     4164.55  2400.60\n",
      "new_ogikligu   5011.57  2800.97\n",
      "new_ojvighta   6126.91  3357.41\n",
      "new_oksbosfl   5938.80  3650.77\n",
      "new_omdrid     1828.81  1125.71\n",
      "new_omluaj     6294.80  3388.41\n",
      "new_ononma     5405.28  3002.27\n",
      "new_ophugbe    6936.31  4072.16\n",
      "new_osckro     6325.60  3427.16\n",
      "new_osoccef    6062.73  3250.77\n",
      "new_otvasdar   5737.96  3146.22\n",
      "new_oydtesa    5476.33  3209.19\n",
      "new_ucvepnuv   5816.33  3453.47\n",
      "new_ugdifbl    4144.39  2391.73\n",
      "new_ujtosh     3482.12  1932.94\n",
      "new_ujtrud     5425.04  3071.71\n",
      "new_upthin   103063.49 50799.87\n",
      "new_uvreoipy   6289.59  3407.39\n"
     ]
    }
   ],
   "source": [
    "RFMData = pd.merge(MileCover,DistanceWPassanger, on='Taxi', how='left')\n",
    "print(RFMData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "output  = pd.DataFrame()\n",
    "output = data[['Taxi','monthday']]\n",
    "output = output.drop_duplicates()\n",
    "ActDay = pd.DataFrame()\n",
    "ActDay = output.groupby(by=['Taxi'])['monthday'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Active Minutes\n",
    "ActMin = pd.DataFrame()\n",
    "ActMin = data.groupby(by=['Taxi'])['Timestamp'].count()\n",
    "#Active Days\n",
    "output  = pd.DataFrame()\n",
    "output = data[['Taxi','monthday']]\n",
    "output = output.drop_duplicates()\n",
    "ActDay = pd.DataFrame()\n",
    "ActDay = output.groupby(by=['Taxi'])['monthday'].count()\n",
    "\n",
    "Active = pd.DataFrame()\n",
    "Active = pd.merge(ActMin, ActDay, on='Taxi', how='left')\n",
    "#Finding Active Minutes per Day\n",
    "Active['ActMinPerDay'] = Active['Timestamp'] / Active['monthday']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              MileCoverage  MileCoverageWPassanger  ActMinPerDay\n",
      "Taxi                                                            \n",
      "new_abmuyawm       4945.10                 2837.17        949.83\n",
      "new_acvebr         6668.34                 2803.25        985.68\n",
      "new_afweka         5431.69                 3068.61        964.40\n",
      "new_ajsnedsi       6603.38                 3537.55        927.84\n",
      "new_amknywr        5606.30                 3546.35        912.12\n",
      "new_ancedvab       1822.48                 1042.58        990.11\n",
      "new_andyol         5451.43                 3127.38       1029.25\n",
      "new_aniajcr        5326.07                 3026.74       1032.92\n",
      "new_anupwr         4006.42                 2198.14        619.29\n",
      "new_artgagvu       5339.73                 3075.60       1117.19\n",
      "new_asigno         6106.54                 3305.22       1135.58\n",
      "new_atidfi         6433.05                 3736.04       1022.62\n",
      "new_easnsvu        5812.89                 3088.56        738.67\n",
      "new_ecdiwovu       6169.27                 3529.79       1013.96\n",
      "new_eglohabs       5371.02                 3043.68        839.92\n",
      "new_eicmynts       3423.72                 1882.14        440.57\n",
      "new_ejcrur         4703.90                 2573.98        868.71\n",
      "new_elswcky        5734.05                 3360.13        955.79\n",
      "new_eoffrij        5696.08                 3288.99       1033.00\n",
      "new_eybyay         6651.21                 3583.84       1103.44\n",
      "new_icedpa         3027.34                 1697.96        862.83\n",
      "new_ictlypt        4708.31                 2765.17        615.92\n",
      "new_idjoat          771.99                  421.79        958.43\n",
      "new_idjojwa        1122.90                  690.45        555.00\n",
      "new_idodly         6323.11                 3869.85        973.46\n",
      "new_igglitby       6704.11                 3562.57        908.08\n",
      "new_ikujfurk       4579.91                 2550.30        864.29\n",
      "new_imhacy         1147.65                  658.93        702.73\n",
      "new_indtwrat       6153.95                 3401.41        755.54\n",
      "new_iofplik        5985.70                 3229.65        756.24\n",
      "new_iovhar         5933.90                 3343.29       1065.29\n",
      "new_ipdraw          744.10                  337.56       1006.75\n",
      "new_mfeuer           25.98                    9.09        102.00\n",
      "new_oasthul        4449.06                 2593.81        609.50\n",
      "new_obsimjio       5222.93                 3125.83       1073.25\n",
      "new_ocwyit         4164.55                 2400.60        700.04\n",
      "new_ogikligu       5011.57                 2800.97       1071.29\n",
      "new_ojvighta       6126.91                 3357.41        992.72\n",
      "new_oksbosfl       5938.80                 3650.77        809.00\n",
      "new_omdrid         1828.81                 1125.71       1006.38\n",
      "new_omluaj         6294.80                 3388.41       1066.92\n",
      "new_ononma         5405.28                 3002.27       1041.25\n",
      "new_ophugbe        6936.31                 4072.16       1083.67\n",
      "new_osckro         6325.60                 3427.16       1101.46\n",
      "new_osoccef        6062.73                 3250.77       1162.67\n",
      "new_otvasdar       5737.96                 3146.22       1022.17\n",
      "new_oydtesa        5476.33                 3209.19       1115.71\n",
      "new_ucvepnuv       5816.33                 3453.47        827.08\n",
      "new_ugdifbl        4144.39                 2391.73        718.38\n",
      "new_ujtosh         3482.12                 1932.94        911.44\n",
      "new_ujtrud         5425.04                 3071.71       1006.46\n",
      "new_upthin       103063.49                50799.87       2056.92\n",
      "new_uvreoipy       6289.59                 3407.39        927.83\n"
     ]
    }
   ],
   "source": [
    "RFMData = pd.merge(RFMData,Active, on='Taxi', how='left')\n",
    "RFMDataF = RFMData[['Miles_x', 'Miles_y', 'ActMinPerDay']]\n",
    "RFMDataF.rename(columns={'Miles_x': 'MileCoverage', 'Miles_y': 'MileCoverageWPassanger'}, inplace=True)\n",
    "print(RFMDataF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After merging data, I found the quantiles value. This will help me to divide my data into four in terms of every variable such as Mile Coverages with Passanger."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'MileCoverage': {0.25: 4449.064540179275, 0.5: 5451.434417563113, 0.75: 6126.910746619501}, 'MileCoverageWPassanger': {0.25: 2550.2962244979594, 0.5: 3088.5633236431745, 0.75: 3401.4137584852115}, 'ActMinPerDay': {0.25: 827.08, 0.5: 964.4, 0.75: 1033.0}}\n"
     ]
    }
   ],
   "source": [
    "quantiles = RFMDataF.quantile(q=[0.25,0.5,0.75])\n",
    "quantiles = quantiles.to_dict()\n",
    "print(quantiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RScore(x,p,d):\n",
    "    if x <= d[p][0.25]:\n",
    "        return 1\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 2\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 3\n",
    "    else:\n",
    "        return 4\n",
    "    \n",
    "def FMScore(x,p,d):\n",
    "    if x <= d[p][0.25]:\n",
    "        return 4\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 3\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 2\n",
    "    else:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>MileCoverage</th>\n",
       "      <th>MileCoverageWPassanger</th>\n",
       "      <th>ActMinPerDay</th>\n",
       "      <th>r_quartile</th>\n",
       "      <th>f_quartile</th>\n",
       "      <th>m_quartile</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taxi</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>new_abmuyawm</td>\n",
       "      <td>4945.10</td>\n",
       "      <td>2837.17</td>\n",
       "      <td>949.83</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>new_acvebr</td>\n",
       "      <td>6668.34</td>\n",
       "      <td>2803.25</td>\n",
       "      <td>985.68</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>new_afweka</td>\n",
       "      <td>5431.69</td>\n",
       "      <td>3068.61</td>\n",
       "      <td>964.40</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>new_ajsnedsi</td>\n",
       "      <td>6603.38</td>\n",
       "      <td>3537.55</td>\n",
       "      <td>927.84</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>new_amknywr</td>\n",
       "      <td>5606.30</td>\n",
       "      <td>3546.35</td>\n",
       "      <td>912.12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              MileCoverage  MileCoverageWPassanger  ActMinPerDay  r_quartile  \\\n",
       "Taxi                                                                           \n",
       "new_abmuyawm       4945.10                 2837.17        949.83           2   \n",
       "new_acvebr         6668.34                 2803.25        985.68           4   \n",
       "new_afweka         5431.69                 3068.61        964.40           2   \n",
       "new_ajsnedsi       6603.38                 3537.55        927.84           4   \n",
       "new_amknywr        5606.30                 3546.35        912.12           3   \n",
       "\n",
       "              f_quartile  m_quartile  \n",
       "Taxi                                  \n",
       "new_abmuyawm           3           3  \n",
       "new_acvebr             3           2  \n",
       "new_afweka             3           3  \n",
       "new_ajsnedsi           1           3  \n",
       "new_amknywr            1           3  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFMDataF['r_quartile'] = RFMDataF['MileCoverage'].apply(RScore, args=('MileCoverage',quantiles,))\n",
    "RFMDataF['f_quartile'] = RFMDataF['MileCoverageWPassanger'].apply(FMScore, args=('MileCoverageWPassanger',quantiles,))\n",
    "RFMDataF['m_quartile'] = RFMDataF['ActMinPerDay'].apply(FMScore, args=('ActMinPerDay',quantiles,))\n",
    "RFMDataF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>MileCoverage</th>\n",
       "      <th>MileCoverageWPassanger</th>\n",
       "      <th>ActMinPerDay</th>\n",
       "      <th>r_quartile</th>\n",
       "      <th>f_quartile</th>\n",
       "      <th>m_quartile</th>\n",
       "      <th>RFMScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taxi</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>new_abmuyawm</td>\n",
       "      <td>4945.10</td>\n",
       "      <td>2837.17</td>\n",
       "      <td>949.83</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>new_acvebr</td>\n",
       "      <td>6668.34</td>\n",
       "      <td>2803.25</td>\n",
       "      <td>985.68</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>new_afweka</td>\n",
       "      <td>5431.69</td>\n",
       "      <td>3068.61</td>\n",
       "      <td>964.40</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>new_ajsnedsi</td>\n",
       "      <td>6603.38</td>\n",
       "      <td>3537.55</td>\n",
       "      <td>927.84</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>new_amknywr</td>\n",
       "      <td>5606.30</td>\n",
       "      <td>3546.35</td>\n",
       "      <td>912.12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              MileCoverage  MileCoverageWPassanger  ActMinPerDay  r_quartile  \\\n",
       "Taxi                                                                           \n",
       "new_abmuyawm       4945.10                 2837.17        949.83           2   \n",
       "new_acvebr         6668.34                 2803.25        985.68           4   \n",
       "new_afweka         5431.69                 3068.61        964.40           2   \n",
       "new_ajsnedsi       6603.38                 3537.55        927.84           4   \n",
       "new_amknywr        5606.30                 3546.35        912.12           3   \n",
       "\n",
       "              f_quartile  m_quartile RFMScore  \n",
       "Taxi                                           \n",
       "new_abmuyawm           3           3      233  \n",
       "new_acvebr             3           2      432  \n",
       "new_afweka             3           3      233  \n",
       "new_ajsnedsi           1           3      413  \n",
       "new_amknywr            1           3      313  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFMDataF['RFMScore'] = RFMDataF.r_quartile.map(str) + RFMDataF.f_quartile.map(str) + RFMDataF.m_quartile.map(str)\n",
    "RFMDataF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RFMScore\n",
       "134    1\n",
       "142    3\n",
       "143    3\n",
       "144    7\n",
       "221    1\n",
       "222    1\n",
       "231    3\n",
       "232    2\n",
       "233    4\n",
       "234    1\n",
       "243    1\n",
       "313    1\n",
       "314    2\n",
       "321    4\n",
       "322    3\n",
       "323    1\n",
       "324    1\n",
       "334    1\n",
       "411    4\n",
       "412    3\n",
       "413    3\n",
       "421    1\n",
       "424    1\n",
       "432    1\n",
       "Name: RFMScore, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFMDataF.groupby(by=['RFMScore'])['RFMScore'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can give clusters a name accordingly. Here is some example of it. \n",
    "---\n",
    "Best Taxi Drivers: Actively driving cab and working much more than other per day\n",
    "\n",
    "     (if mile coverage, mile coverage with passanger and active minutes per day are in the top %25)\n",
    "---               \n",
    "Globetrotter : Actively driving cab\n",
    "\n",
    "     (if mile coverage is in the top %25)\n",
    "---               \n",
    "Most Occupied Taxi : Actively driving cab with passanger\n",
    "\n",
    "     (if mile coverage with passanger is in the top %25)\n",
    "---               \n",
    "Most Active Taxi: Working much more than other per day\n",
    "\n",
    "     (if active minutes per day is in the top %25)\n",
    "---               \n",
    "Fast and Vacant: Actively driving more miles but less minutes per day. That means he/she drives fast with more vacant \n",
    "\n",
    "     (if mile coverage is in the top %25 and mile coverage with passanger and active minutes per day are in the bottom %25)\n",
    "---               \n",
    "Lucky Stand-by: Driver is not driving without passanger but he/she drive more than other in terms of mile coverage with passanger and active minutes per day.\n",
    "\n",
    "     (if mile coverage is in the bottom %25 and mile coverage with passanger and active minutes per day are in the top %25)\n",
    "---                \n",
    "Not Active Taxi Drivers: Not actively driving cab and working much more than other per day\n",
    "\n",
    "     (if mile coverage, mile coverage with passanger and active minutes per day are in the bottom %25)\n",
    "--- "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Taxi Drivers:  0\n",
      "Globetrotter :  13\n",
      "Most Occupied Taxi :  14\n",
      "Most Active Taxi:  14\n",
      "Fast and Vacant:  4\n",
      "Lucky Stand-by:  7\n",
      "Not Active Taxi Drivers:  0\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Taxi Drivers: \",len(RFMDataF[RFMDataF['RFMScore']=='444']))\n",
    "print('Globetrotter : ',len(RFMDataF[RFMDataF['r_quartile']==4]))\n",
    "print('Most Occupied Taxi : ',len(RFMDataF[RFMDataF['f_quartile']==4]))\n",
    "print(\"Most Active Taxi: \",len(RFMDataF[RFMDataF['m_quartile']==4]))\n",
    "print('Fast and Vacant: ', len(RFMDataF[RFMDataF['RFMScore']=='411']))\n",
    "print('Lucky Stand-by: ',len(RFMDataF[RFMDataF['RFMScore']=='144']))\n",
    "print('Not Active Taxi Drivers: ',len(RFMDataF[RFMDataF['RFMScore']=='111']))"
   ]
  }
 ],
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