Uber_USFares_20141112.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11.12.14 US Fare Data for UberX and UberXL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Credit: RideshareDashboard.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "usfares2014 = pd.read_csv(\"USUberFares_20141112.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### X is an Uber ride with a single passenger\n",
    "#### XL is an Uber ride with multiple passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>US_City</th>\n",
       "      <th>X_Base Fare</th>\n",
       "      <th>X_Minute</th>\n",
       "      <th>X_Mile</th>\n",
       "      <th>X_Safety</th>\n",
       "      <th>X_Min Fare</th>\n",
       "      <th>X_Cancel</th>\n",
       "      <th>XL_Base Fare</th>\n",
       "      <th>XL_Minute</th>\n",
       "      <th>XL_Mile</th>\n",
       "      <th>XL_Safety</th>\n",
       "      <th>XL_Min Fare</th>\n",
       "      <th>XL_Cancel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>‘Akron’</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>‘Albuquerque’</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>‘Amarillo’</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>‘Anchorage’</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>‘Ann Arbor’</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.18</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         US_City  X_Base Fare  X_Minute  X_Mile  X_Safety  X_Min Fare  \\\n",
       "0        ‘Akron’          2.0      0.30    1.40         1         5.0   \n",
       "1  ‘Albuquerque’          1.3      0.25    1.75         1         5.0   \n",
       "2     ‘Amarillo’          1.9      0.25    1.75         1         5.7   \n",
       "3    ‘Anchorage’          2.5      0.25    2.10         1         6.0   \n",
       "4    ‘Ann Arbor’          1.3      0.18    1.30         1         4.0   \n",
       "\n",
       "   X_Cancel  XL_Base Fare  XL_Minute  XL_Mile  XL_Safety  XL_Min Fare  \\\n",
       "0         5           0.0        0.0      0.0          0          0.0   \n",
       "1         5           0.0        0.0      0.0          0          0.0   \n",
       "2         5           0.0        0.0      0.0          0          0.0   \n",
       "3         0           0.0        0.0      0.0          0          0.0   \n",
       "4         5           3.0        0.2      2.5          1          7.0   \n",
       "\n",
       "   XL_Cancel  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          7  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usfares2014.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project: Which Uber type produced most cancellations? And if cancellations is linked to fares, which Uber type would need to reset their pricing strategy?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Some details on how Uber determines fares:\n",
    "\n",
    "### With upfront pricing, you know the exact cost of your trip before requesting. This fare includes (but is not limited to):\n",
    "### -A base rate\n",
    "### -Rates for estimated time and distance of the route\n",
    "### -The current demand for rides in the area (surge charging)\n",
    "\n",
    "### A booking fee and any applicable surcharges, fees, and tolls are also calculated and included.\n",
    "\n",
    "### When you request a ride, you agree to be charged the upfront fare when the trip ends. Your fare may increase if you travel to a different destination or make extra stops along the route, or the trip takes much longer than expected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "US_City         ‘Dallas’\n",
       "X_Base Fare            0\n",
       "X_Minute            0.17\n",
       "X_Mile               1.2\n",
       "X_Safety               1\n",
       "X_Min Fare             5\n",
       "X_Cancel               6\n",
       "XL_Base Fare        2.85\n",
       "XL_Minute           0.25\n",
       "XL_Mile             1.85\n",
       "XL_Safety              1\n",
       "XL_Min Fare            7\n",
       "XL_Cancel              6\n",
       "Name: 29, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usfares2014.loc[usfares2014['X_Base Fare'].idxmin()] #Dallas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "US_City         ‘Hamptons’\n",
       "X_Base Fare             10\n",
       "X_Minute                 1\n",
       "X_Mile                3.35\n",
       "X_Safety                 0\n",
       "X_Min Fare              25\n",
       "X_Cancel                15\n",
       "XL_Base Fare             0\n",
       "XL_Minute                0\n",
       "XL_Mile                  0\n",
       "XL_Safety                0\n",
       "XL_Min Fare              0\n",
       "XL_Cancel                0\n",
       "Name: 46, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usfares2014.loc[usfares2014['X_Base Fare'].idxmax()] #Hamptons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "US_City         ‘Austin’\n",
       "X_Base Fare          1.3\n",
       "X_Minute             0.3\n",
       "X_Mile               1.7\n",
       "X_Safety               1\n",
       "X_Min Fare             0\n",
       "X_Cancel               5\n",
       "XL_Base Fare         2.9\n",
       "XL_Minute           0.45\n",
       "XL_Mile              2.8\n",
       "XL_Safety              1\n",
       "XL_Min Fare            0\n",
       "XL_Cancel              5\n",
       "Name: 10, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usfares2014.loc[usfares2014['X_Min Fare'].idxmin()] #Austin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "US_City         ‘Hamptons’\n",
       "X_Base Fare             10\n",
       "X_Minute                 1\n",
       "X_Mile                3.35\n",
       "X_Safety                 0\n",
       "X_Min Fare              25\n",
       "X_Cancel                15\n",
       "XL_Base Fare             0\n",
       "XL_Minute                0\n",
       "XL_Mile                  0\n",
       "XL_Safety                0\n",
       "XL_Min Fare              0\n",
       "XL_Cancel                0\n",
       "Name: 46, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usfares2014.loc[usfares2014['X_Min Fare'].idxmax()] #Hamptons again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "US_City         ‘Akron’\n",
       "X_Base Fare           2\n",
       "X_Minute            0.3\n",
       "X_Mile              1.4\n",
       "X_Safety              1\n",
       "X_Min Fare            5\n",
       "X_Cancel              5\n",
       "XL_Base Fare          0\n",
       "XL_Minute             0\n",
       "XL_Mile               0\n",
       "XL_Safety             0\n",
       "XL_Min Fare           0\n",
       "XL_Cancel             0\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usfares2014.loc[usfares2014['XL_Base Fare'].idxmin()] #Akron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "US_City         ‘San Francisco’\n",
       "X_Base Fare                 2.2\n",
       "X_Minute                   0.26\n",
       "X_Mile                      1.3\n",
       "X_Safety                      1\n",
       "X_Min Fare                    5\n",
       "X_Cancel                      5\n",
       "XL_Base Fare                  5\n",
       "XL_Minute                  0.45\n",
       "XL_Mile                    2.15\n",
       "XL_Safety                     1\n",
       "XL_Min Fare                   8\n",
       "XL_Cancel                     5\n",
       "Name: 97, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usfares2014.loc[usfares2014['XL_Base Fare'].idxmax()] #San Francisco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "US_City         ‘Akron’\n",
       "X_Base Fare           2\n",
       "X_Minute            0.3\n",
       "X_Mile              1.4\n",
       "X_Safety              1\n",
       "X_Min Fare            5\n",
       "X_Cancel              5\n",
       "XL_Base Fare          0\n",
       "XL_Minute             0\n",
       "XL_Mile               0\n",
       "XL_Safety             0\n",
       "XL_Min Fare           0\n",
       "XL_Cancel             0\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usfares2014.loc[usfares2014['XL_Min Fare'].idxmin()] #Akron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "US_City         ‘New York City’\n",
       "X_Base Fare                   3\n",
       "X_Minute                    0.4\n",
       "X_Mile                     2.15\n",
       "X_Safety                      0\n",
       "X_Min Fare                    8\n",
       "X_Cancel                     10\n",
       "XL_Base Fare                4.5\n",
       "XL_Minute                   0.6\n",
       "XL_Mile                    3.25\n",
       "XL_Safety                     0\n",
       "XL_Min Fare                  12\n",
       "XL_Cancel                    10\n",
       "Name: 75, dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usfares2014.loc[usfares2014['XL_Min Fare'].idxmax()] #New York City - interesting since it is a metropolitan city like SF (max XL_Base Fare)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X_Base Fare</th>\n",
       "      <th>X_Minute</th>\n",
       "      <th>X_Mile</th>\n",
       "      <th>X_Safety</th>\n",
       "      <th>X_Min Fare</th>\n",
       "      <th>X_Cancel</th>\n",
       "      <th>XL_Base Fare</th>\n",
       "      <th>XL_Minute</th>\n",
       "      <th>XL_Mile</th>\n",
       "      <th>XL_Safety</th>\n",
       "      <th>XL_Min Fare</th>\n",
       "      <th>XL_Cancel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.909916</td>\n",
       "      <td>0.237647</td>\n",
       "      <td>1.497563</td>\n",
       "      <td>0.957983</td>\n",
       "      <td>4.918067</td>\n",
       "      <td>5.344538</td>\n",
       "      <td>1.268067</td>\n",
       "      <td>0.121681</td>\n",
       "      <td>0.857983</td>\n",
       "      <td>0.361345</td>\n",
       "      <td>2.478151</td>\n",
       "      <td>2.159664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.047439</td>\n",
       "      <td>0.098029</td>\n",
       "      <td>0.407597</td>\n",
       "      <td>0.201476</td>\n",
       "      <td>2.353241</td>\n",
       "      <td>1.829314</td>\n",
       "      <td>1.700831</td>\n",
       "      <td>0.165408</td>\n",
       "      <td>1.148506</td>\n",
       "      <td>0.482421</td>\n",
       "      <td>3.413586</td>\n",
       "      <td>2.997126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.150000</td>\n",
       "      <td>0.255000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>2.150000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.350000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       X_Base Fare    X_Minute      X_Mile    X_Safety  X_Min Fare  \\\n",
       "count   119.000000  119.000000  119.000000  119.000000  119.000000   \n",
       "mean      1.909916    0.237647    1.497563    0.957983    4.918067   \n",
       "std       1.047439    0.098029    0.407597    0.201476    2.353241   \n",
       "min       0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%       1.500000    0.200000    1.250000    1.000000    4.000000   \n",
       "50%       1.900000    0.210000    1.500000    1.000000    5.000000   \n",
       "75%       2.150000    0.255000    1.750000    1.000000    5.000000   \n",
       "max      10.000000    1.000000    3.350000    1.000000   25.000000   \n",
       "\n",
       "         X_Cancel  XL_Base Fare   XL_Minute     XL_Mile   XL_Safety  \\\n",
       "count  119.000000    119.000000  119.000000  119.000000  119.000000   \n",
       "mean     5.344538      1.268067    0.121681    0.857983    0.361345   \n",
       "std      1.829314      1.700831    0.165408    1.148506    0.482421   \n",
       "min      0.000000      0.000000    0.000000    0.000000    0.000000   \n",
       "25%      5.000000      0.000000    0.000000    0.000000    0.000000   \n",
       "50%      5.000000      0.000000    0.000000    0.000000    0.000000   \n",
       "75%      5.000000      3.000000    0.300000    2.150000    1.000000   \n",
       "max     15.000000      5.000000    0.600000    3.250000    1.000000   \n",
       "\n",
       "       XL_Min Fare   XL_Cancel  \n",
       "count   119.000000  119.000000  \n",
       "mean      2.478151    2.159664  \n",
       "std       3.413586    2.997126  \n",
       "min       0.000000    0.000000  \n",
       "25%       0.000000    0.000000  \n",
       "50%       0.000000    0.000000  \n",
       "75%       7.000000    5.000000  \n",
       "max      12.000000   10.000000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usfares2014.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Framework from here:\n",
    "## 1. Transform data into what will work for classification function\n",
    "## 2. Include all fares as features\n",
    "## 3. Make Training and Test Sets\n",
    "## 4. Train Model (Logistic Regression)\n",
    "## 5. Examine Model Performance\n",
    "## 6. Run Test set through Model\n",
    "## 7. Compare predictions with real data                                        \n",
    "## 8. Propose how this model can be updated with current data to make up-to-date predictions on pricing\n",
    "## 9. Analysis can be done on pricing to propose new pricing strategy, see if there is ROI "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Transforming y-value (classification value) into binary set of values.\n",
    "#### For values 0-->0, for values >1 -->1 representing no cancellation, and yes cancellation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "usfares2014['X_Cancel_Binary'] = np.sign(usfares2014['X_Cancel'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "usfares2014['XL_Cancel_Binary'] = np.sign(usfares2014['XL_Cancel'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.head of                    US_City  X_Base Fare  X_Minute  X_Mile  X_Safety  \\\n",
       "0                  ‘Akron’         2.00      0.30    1.40         1   \n",
       "1            ‘Albuquerque’         1.30      0.25    1.75         1   \n",
       "2               ‘Amarillo’         1.90      0.25    1.75         1   \n",
       "3              ‘Anchorage’         2.50      0.25    2.10         1   \n",
       "4              ‘Ann Arbor’         1.30      0.18    1.30         1   \n",
       "5              ‘Annapolis’         2.76      0.19    1.52         1   \n",
       "6          ‘Asheville, NC’         2.20      0.20    1.85         1   \n",
       "7                 ‘Athens’         1.30      0.16    1.22         1   \n",
       "8                ‘Atlanta’         1.30      0.16    1.22         1   \n",
       "9             ‘Auburn, AL’         1.50      0.20    1.60         1   \n",
       "10                ‘Austin’         1.30      0.30    1.70         1   \n",
       "11           ‘Bakersfield’         1.90      0.25    1.25         1   \n",
       "12             ‘Baltimore’         1.75      0.15    1.30         1   \n",
       "13           ‘Baton Rouge’         2.50      0.35    1.60         1   \n",
       "14                 ‘Boise’         2.50      0.25    1.75         1   \n",
       "15                ‘Boston’         2.00      0.21    1.20         1   \n",
       "16            ‘Burlington’         2.15      0.30    2.00         1   \n",
       "17        ‘Charleston, SC’         1.95      0.20    1.75         1   \n",
       "18             ‘Charlotte’         2.00      0.20    1.25         1   \n",
       "19   ‘Charlottesville, VA’         2.75      0.25    1.50         1   \n",
       "20               ‘Chicago’         1.70      0.20    0.90         1   \n",
       "21            ‘Cincinnati’         2.00      0.25    1.25         1   \n",
       "22             ‘Cleveland’         2.00      0.25    1.25         1   \n",
       "23       ‘College Station’         1.90      0.25    1.75         1   \n",
       "24          ‘Columbia, MO’         1.75      0.50    1.80         1   \n",
       "25          ‘Columbia, SC’         1.95      0.20    1.75         1   \n",
       "26              ‘Columbus’         0.50      0.22    1.30         1   \n",
       "27           ‘Connecticut’         3.00      0.20    1.90         1   \n",
       "28        ‘Corpus Christi’         1.75      0.25    1.45         1   \n",
       "29                ‘Dallas’         0.00      0.17    1.20         1   \n",
       "..                     ...          ...       ...     ...       ...   \n",
       "89                  ‘Reno’         2.40      0.30    1.85         1   \n",
       "90          ‘Richmond, VA’         1.50      0.20    1.60         1   \n",
       "91    ‘Roanoke-Blacksburg’         2.75      0.25    1.50         1   \n",
       "92            ‘Sacramento’         1.70      0.20    1.30         1   \n",
       "93                 ‘Salem’         3.00      0.30    1.75         1   \n",
       "94        ‘Salt Lake City’         1.15      0.18    1.40         1   \n",
       "95           ‘San Antonio’         1.60      0.22    1.50         1   \n",
       "96             ‘San Diego’         2.10      0.20    1.25         1   \n",
       "97         ‘San Francisco’         2.20      0.26    1.30         1   \n",
       "98       ‘San Luis Obispo’         3.00      0.15    2.45         1   \n",
       "99         ‘Santa Barbara’         3.00      0.25    1.80         1   \n",
       "100              ‘Seattle’         1.35      0.24    1.35         1   \n",
       "101           ‘South Bend’         1.90      0.25    1.25         1   \n",
       "102              ‘Spokane’         1.50      0.35    1.65         1   \n",
       "103             ‘St Louis’         0.00      0.00    0.00         0   \n",
       "104               ‘Tacoma’         1.35      0.24    1.35         1   \n",
       "105          ‘Tallahassee’         2.00      0.15    1.50         1   \n",
       "106                ‘Tampa’         1.25      0.13    1.20         1   \n",
       "107               ‘Toledo’         1.00      0.30    1.50         1   \n",
       "108              ‘Toronto’         2.75      0.30    0.90         1   \n",
       "109               ‘Tucson’         1.75      0.18    1.70         1   \n",
       "110                ‘Tulsa’         1.75      0.18    1.45         1   \n",
       "111           ‘Tuscaloosa’         1.50      0.20    1.60         1   \n",
       "112        ‘Vancouver, Wa’         1.50      0.30    1.55         1   \n",
       "113              ‘Ventura’         1.61      0.29    1.25         1   \n",
       "114                 ‘Waco’         1.90      0.25    1.75         1   \n",
       "115      ‘Washington D.C.’         2.00      0.25    1.25         1   \n",
       "116              ‘Wichita’         2.00      0.20    1.65         1   \n",
       "117       ‘Wilmington, NC’         1.95      0.20    1.50         1   \n",
       "118            ‘Worcester’         2.00      0.21    1.20         1   \n",
       "\n",
       "     X_Min Fare  X_Cancel  XL_Base Fare  XL_Minute  XL_Mile  XL_Safety  \\\n",
       "0          5.00         5          0.00       0.00     0.00          0   \n",
       "1          5.00         5          0.00       0.00     0.00          0   \n",
       "2          5.70         5          0.00       0.00     0.00          0   \n",
       "3          6.00         0          0.00       0.00     0.00          0   \n",
       "4          4.00         5          3.00       0.20     2.50          1   \n",
       "5          5.60         5          0.00       0.00     0.00          0   \n",
       "6          5.00         5          0.00       0.00     0.00          0   \n",
       "7          6.00         6          0.00       0.00     0.00          0   \n",
       "8          6.00         6          3.10       0.30     2.20          1   \n",
       "9          5.00         5          0.00       0.00     0.00          0   \n",
       "10         0.00         5          2.90       0.45     2.80          1   \n",
       "11         4.00         5          0.00       0.00     0.00          0   \n",
       "12         4.00         5          3.00       0.25     2.25          1   \n",
       "13         5.00         5          4.00       0.40     2.80          1   \n",
       "14         5.00         5          0.00       0.00     0.00          0   \n",
       "15         5.00        10          3.50       0.35     2.45          1   \n",
       "16         5.00         5          0.00       0.00     0.00          0   \n",
       "17         4.00         5          0.00       0.00     0.00          0   \n",
       "18         4.00         5          4.00       0.30     2.55          1   \n",
       "19         5.00         5          0.00       0.00     0.00          0   \n",
       "20         2.70         5          3.00       0.35     1.75          1   \n",
       "21         5.00         5          3.85       0.40     2.15          1   \n",
       "22         5.00         5          3.85       0.40     2.15          1   \n",
       "23         5.70         5          0.00       0.00     0.00          0   \n",
       "24         5.00         5          0.00       0.00     0.00          0   \n",
       "25         4.00         5          0.00       0.00     0.00          0   \n",
       "26         5.00         5          3.30       0.23     2.15          1   \n",
       "27         7.00        10          0.00       0.00     0.00          0   \n",
       "28         4.70         5          0.00       0.00     0.00          0   \n",
       "29         5.00         6          2.85       0.25     1.85          1   \n",
       "..          ...       ...           ...        ...      ...        ...   \n",
       "89         5.00         5          0.00       0.00     0.00          0   \n",
       "90         5.00         5          0.00       0.00     0.00          0   \n",
       "91         5.00         5          0.00       0.00     0.00          0   \n",
       "92         5.00         5          4.00       0.35     2.80          1   \n",
       "93         6.00         5          0.00       0.00     0.00          0   \n",
       "94         4.00         5          0.00       0.00     0.00          0   \n",
       "95         5.00         6          3.00       0.35     2.75          1   \n",
       "96         4.00         5          2.85       0.25     2.65          1   \n",
       "97         5.00         5          5.00       0.45     2.15          1   \n",
       "98         5.00         5          0.00       0.00     0.00          0   \n",
       "99         6.00         5          0.00       0.00     0.00          0   \n",
       "100        4.00         5          3.00       0.30     2.75          1   \n",
       "101        0.00         5          0.00       0.00     0.00          0   \n",
       "102        5.00         5          0.00       0.00     0.00          0   \n",
       "103        0.00         0          0.00       0.00     0.00          0   \n",
       "104        4.00         5          0.00       0.00     0.00          0   \n",
       "105        4.00         5          0.00       0.00     0.00          0   \n",
       "106        4.00         5          3.00       0.30     2.15          1   \n",
       "107        4.00         5          0.00       0.00     0.00          0   \n",
       "108        2.75         5          5.00       0.35     1.55          1   \n",
       "109        4.00        10          3.50       0.30     2.75          1   \n",
       "110        5.00         5          3.30       0.35     2.50          1   \n",
       "111        5.00         5          0.00       0.00     0.00          0   \n",
       "112        5.00         5          0.00       0.00     0.00          0   \n",
       "113        4.00         5          0.00       0.00     0.00          0   \n",
       "114        5.70         5          0.00       0.00     0.00          0   \n",
       "115        5.00         5          4.00       0.35     2.50          1   \n",
       "116        5.00         5          0.00       0.00     0.00          0   \n",
       "117        4.00         4          0.00       0.00     0.00          0   \n",
       "118        5.00        10          0.00       0.00     0.00          0   \n",
       "\n",
       "     XL_Min Fare  XL_Cancel  X_Cancel_Binary  XL_Cancel_Binary  \n",
       "0            0.0          0                1                 0  \n",
       "1            0.0          0                1                 0  \n",
       "2            0.0          0                1                 0  \n",
       "3            0.0          0                0                 0  \n",
       "4            7.0          7                1                 1  \n",
       "5            0.0          0                1                 0  \n",
       "6            0.0          0                1                 0  \n",
       "7            0.0          0                1                 0  \n",
       "8            8.0          6                1                 1  \n",
       "9            0.0          0                1                 0  \n",
       "10           0.0          5                1                 1  \n",
       "11           0.0          0                1                 0  \n",
       "12           7.0          5                1                 1  \n",
       "13           7.0          5                1                 1  \n",
       "14           0.0          0                1                 0  \n",
       "15           7.0         10                1                 1  \n",
       "16           0.0          0                1                 0  \n",
       "17           0.0          0                1                 0  \n",
       "18           7.0          5                1                 1  \n",
       "19           0.0          0                1                 0  \n",
       "20           6.0          5                1                 1  \n",
       "21           7.0          5                1                 1  \n",
       "22           7.0          5                1                 1  \n",
       "23           0.0          0                1                 0  \n",
       "24           0.0          0                1                 0  \n",
       "25           0.0          0                1                 0  \n",
       "26           7.0          5                1                 1  \n",
       "27           0.0          0                1                 0  \n",
       "28           0.0          0                1                 0  \n",
       "29           7.0          6                1                 1  \n",
       "..           ...        ...              ...               ...  \n",
       "89           0.0          0                1                 0  \n",
       "90           0.0          0                1                 0  \n",
       "91           0.0          0                1                 0  \n",
       "92           7.0          5                1                 1  \n",
       "93           0.0          0                1                 0  \n",
       "94           0.0          0                1                 0  \n",
       "95           7.0          6                1                 1  \n",
       "96           7.0          5                1                 1  \n",
       "97           8.0          5                1                 1  \n",
       "98           0.0          0                1                 0  \n",
       "99           0.0          0                1                 0  \n",
       "100          6.0          5                1                 1  \n",
       "101          0.0          0                1                 0  \n",
       "102          0.0          0                1                 0  \n",
       "103          0.0          0                0                 0  \n",
       "104          0.0          0                1                 0  \n",
       "105          0.0          0                1                 0  \n",
       "106          7.0          5                1                 1  \n",
       "107          0.0          0                1                 0  \n",
       "108          5.0          5                1                 1  \n",
       "109          6.0         10                1                 1  \n",
       "110          7.0          5                1                 1  \n",
       "111          0.0          0                1                 0  \n",
       "112          0.0          0                1                 0  \n",
       "113          0.0          0                1                 0  \n",
       "114          0.0          0                1                 0  \n",
       "115          7.0          5                1                 1  \n",
       "116          0.0          0                1                 0  \n",
       "117          0.0          0                1                 0  \n",
       "118          0.0          0                1                 0  \n",
       "\n",
       "[119 rows x 15 columns]>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usfares2014.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>US_City</th>\n",
       "      <th>X_Base Fare</th>\n",
       "      <th>X_Minute</th>\n",
       "      <th>X_Mile</th>\n",
       "      <th>X_Safety</th>\n",
       "      <th>X_Min Fare</th>\n",
       "      <th>XL_Base Fare</th>\n",
       "      <th>XL_Minute</th>\n",
       "      <th>XL_Mile</th>\n",
       "      <th>XL_Safety</th>\n",
       "      <th>XL_Min Fare</th>\n",
       "      <th>XL_Cancel</th>\n",
       "      <th>X_Cancel_Binary</th>\n",
       "      <th>XL_Cancel_Binary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>‘Akron’</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>‘Albuquerque’</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>‘Amarillo’</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1</td>\n",
       "      <td>5.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>‘Anchorage’</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>‘Ann Arbor’</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.18</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>‘Annapolis’</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1</td>\n",
       "      <td>5.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>‘Asheville, NC’</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>‘Athens’</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>‘Atlanta’</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1</td>\n",
       "      <td>6.00</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>‘Auburn, AL’</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>‘Austin’</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>‘Bakersfield’</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>‘Baltimore’</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>‘Baton Rouge’</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.40</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>‘Boise’</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>‘Boston’</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0.35</td>\n",
       "      <td>2.45</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>‘Burlington’</td>\n",
       "      <td>2.15</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>‘Charleston, SC’</td>\n",
       "      <td>1.95</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>‘Charlotte’</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.55</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>‘Charlottesville, VA’</td>\n",
       "      <td>2.75</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>‘Chicago’</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "      <td>2.70</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>‘Cincinnati’</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.85</td>\n",
       "      <td>0.40</td>\n",
       "      <td>2.15</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>‘Cleveland’</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.85</td>\n",
       "      <td>0.40</td>\n",
       "      <td>2.15</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>‘College Station’</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1</td>\n",
       "      <td>5.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>‘Columbia, MO’</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>‘Columbia, SC’</td>\n",
       "      <td>1.95</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>‘Columbus’</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.23</td>\n",
       "      <td>2.15</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>‘Connecticut’</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>‘Corpus Christi’</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1</td>\n",
       "      <td>4.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>‘Dallas’</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.85</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>‘Reno’</td>\n",
       "      <td>2.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>‘Richmond, VA’</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>‘Roanoke-Blacksburg’</td>\n",
       "      <td>2.75</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>‘Sacramento’</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.35</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>‘Salem’</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>‘Salt Lake City’</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>‘San Antonio’</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.35</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>‘San Diego’</td>\n",
       "      <td>2.10</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.85</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.65</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>‘San Francisco’</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2.15</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>‘San Luis Obispo’</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2.45</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>‘Santa Barbara’</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>‘Seattle’</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>‘South Bend’</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>‘Spokane’</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>‘St Louis’</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>‘Tacoma’</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>‘Tallahassee’</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>‘Tampa’</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.15</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>‘Toledo’</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>‘Toronto’</td>\n",
       "      <td>2.75</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "      <td>2.75</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.55</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>‘Tucson’</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.18</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>‘Tulsa’</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.18</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.35</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>‘Tuscaloosa’</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>‘Vancouver, Wa’</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.55</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>‘Ventura’</td>\n",
       "      <td>1.61</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>‘Waco’</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1</td>\n",
       "      <td>5.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>‘Washington D.C.’</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.35</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>‘Wichita’</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>‘Wilmington, NC’</td>\n",
       "      <td>1.95</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>‘Worcester’</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   US_City  X_Base Fare  X_Minute  X_Mile  X_Safety  \\\n",
       "0                  ‘Akron’         2.00      0.30    1.40         1   \n",
       "1            ‘Albuquerque’         1.30      0.25    1.75         1   \n",
       "2               ‘Amarillo’         1.90      0.25    1.75         1   \n",
       "3              ‘Anchorage’         2.50      0.25    2.10         1   \n",
       "4              ‘Ann Arbor’         1.30      0.18    1.30         1   \n",
       "5              ‘Annapolis’         2.76      0.19    1.52         1   \n",
       "6          ‘Asheville, NC’         2.20      0.20    1.85         1   \n",
       "7                 ‘Athens’         1.30      0.16    1.22         1   \n",
       "8                ‘Atlanta’         1.30      0.16    1.22         1   \n",
       "9             ‘Auburn, AL’         1.50      0.20    1.60         1   \n",
       "10                ‘Austin’         1.30      0.30    1.70         1   \n",
       "11           ‘Bakersfield’         1.90      0.25    1.25         1   \n",
       "12             ‘Baltimore’         1.75      0.15    1.30         1   \n",
       "13           ‘Baton Rouge’         2.50      0.35    1.60         1   \n",
       "14                 ‘Boise’         2.50      0.25    1.75         1   \n",
       "15                ‘Boston’         2.00      0.21    1.20         1   \n",
       "16            ‘Burlington’         2.15      0.30    2.00         1   \n",
       "17        ‘Charleston, SC’         1.95      0.20    1.75         1   \n",
       "18             ‘Charlotte’         2.00      0.20    1.25         1   \n",
       "19   ‘Charlottesville, VA’         2.75      0.25    1.50         1   \n",
       "20               ‘Chicago’         1.70      0.20    0.90         1   \n",
       "21            ‘Cincinnati’         2.00      0.25    1.25         1   \n",
       "22             ‘Cleveland’         2.00      0.25    1.25         1   \n",
       "23       ‘College Station’         1.90      0.25    1.75         1   \n",
       "24          ‘Columbia, MO’         1.75      0.50    1.80         1   \n",
       "25          ‘Columbia, SC’         1.95      0.20    1.75         1   \n",
       "26              ‘Columbus’         0.50      0.22    1.30         1   \n",
       "27           ‘Connecticut’         3.00      0.20    1.90         1   \n",
       "28        ‘Corpus Christi’         1.75      0.25    1.45         1   \n",
       "29                ‘Dallas’         0.00      0.17    1.20         1   \n",
       "..                     ...          ...       ...     ...       ...   \n",
       "89                  ‘Reno’         2.40      0.30    1.85         1   \n",
       "90          ‘Richmond, VA’         1.50      0.20    1.60         1   \n",
       "91    ‘Roanoke-Blacksburg’         2.75      0.25    1.50         1   \n",
       "92            ‘Sacramento’         1.70      0.20    1.30         1   \n",
       "93                 ‘Salem’         3.00      0.30    1.75         1   \n",
       "94        ‘Salt Lake City’         1.15      0.18    1.40         1   \n",
       "95           ‘San Antonio’         1.60      0.22    1.50         1   \n",
       "96             ‘San Diego’         2.10      0.20    1.25         1   \n",
       "97         ‘San Francisco’         2.20      0.26    1.30         1   \n",
       "98       ‘San Luis Obispo’         3.00      0.15    2.45         1   \n",
       "99         ‘Santa Barbara’         3.00      0.25    1.80         1   \n",
       "100              ‘Seattle’         1.35      0.24    1.35         1   \n",
       "101           ‘South Bend’         1.90      0.25    1.25         1   \n",
       "102              ‘Spokane’         1.50      0.35    1.65         1   \n",
       "103             ‘St Louis’         0.00      0.00    0.00         0   \n",
       "104               ‘Tacoma’         1.35      0.24    1.35         1   \n",
       "105          ‘Tallahassee’         2.00      0.15    1.50         1   \n",
       "106                ‘Tampa’         1.25      0.13    1.20         1   \n",
       "107               ‘Toledo’         1.00      0.30    1.50         1   \n",
       "108              ‘Toronto’         2.75      0.30    0.90         1   \n",
       "109               ‘Tucson’         1.75      0.18    1.70         1   \n",
       "110                ‘Tulsa’         1.75      0.18    1.45         1   \n",
       "111           ‘Tuscaloosa’         1.50      0.20    1.60         1   \n",
       "112        ‘Vancouver, Wa’         1.50      0.30    1.55         1   \n",
       "113              ‘Ventura’         1.61      0.29    1.25         1   \n",
       "114                 ‘Waco’         1.90      0.25    1.75         1   \n",
       "115      ‘Washington D.C.’         2.00      0.25    1.25         1   \n",
       "116              ‘Wichita’         2.00      0.20    1.65         1   \n",
       "117       ‘Wilmington, NC’         1.95      0.20    1.50         1   \n",
       "118            ‘Worcester’         2.00      0.21    1.20         1   \n",
       "\n",
       "     X_Min Fare  XL_Base Fare  XL_Minute  XL_Mile  XL_Safety  XL_Min Fare  \\\n",
       "0          5.00          0.00       0.00     0.00          0          0.0   \n",
       "1          5.00          0.00       0.00     0.00          0          0.0   \n",
       "2          5.70          0.00       0.00     0.00          0          0.0   \n",
       "3          6.00          0.00       0.00     0.00          0          0.0   \n",
       "4          4.00          3.00       0.20     2.50          1          7.0   \n",
       "5          5.60          0.00       0.00     0.00          0          0.0   \n",
       "6          5.00          0.00       0.00     0.00          0          0.0   \n",
       "7          6.00          0.00       0.00     0.00          0          0.0   \n",
       "8          6.00          3.10       0.30     2.20          1          8.0   \n",
       "9          5.00          0.00       0.00     0.00          0          0.0   \n",
       "10         0.00          2.90       0.45     2.80          1          0.0   \n",
       "11         4.00          0.00       0.00     0.00          0          0.0   \n",
       "12         4.00          3.00       0.25     2.25          1          7.0   \n",
       "13         5.00          4.00       0.40     2.80          1          7.0   \n",
       "14         5.00          0.00       0.00     0.00          0          0.0   \n",
       "15         5.00          3.50       0.35     2.45          1          7.0   \n",
       "16         5.00          0.00       0.00     0.00          0          0.0   \n",
       "17         4.00          0.00       0.00     0.00          0          0.0   \n",
       "18         4.00          4.00       0.30     2.55          1          7.0   \n",
       "19         5.00          0.00       0.00     0.00          0          0.0   \n",
       "20         2.70          3.00       0.35     1.75          1          6.0   \n",
       "21         5.00          3.85       0.40     2.15          1          7.0   \n",
       "22         5.00          3.85       0.40     2.15          1          7.0   \n",
       "23         5.70          0.00       0.00     0.00          0          0.0   \n",
       "24         5.00          0.00       0.00     0.00          0          0.0   \n",
       "25         4.00          0.00       0.00     0.00          0          0.0   \n",
       "26         5.00          3.30       0.23     2.15          1          7.0   \n",
       "27         7.00          0.00       0.00     0.00          0          0.0   \n",
       "28         4.70          0.00       0.00     0.00          0          0.0   \n",
       "29         5.00          2.85       0.25     1.85          1          7.0   \n",
       "..          ...           ...        ...      ...        ...          ...   \n",
       "89         5.00          0.00       0.00     0.00          0          0.0   \n",
       "90         5.00          0.00       0.00     0.00          0          0.0   \n",
       "91         5.00          0.00       0.00     0.00          0          0.0   \n",
       "92         5.00          4.00       0.35     2.80          1          7.0   \n",
       "93         6.00          0.00       0.00     0.00          0          0.0   \n",
       "94         4.00          0.00       0.00     0.00          0          0.0   \n",
       "95         5.00          3.00       0.35     2.75          1          7.0   \n",
       "96         4.00          2.85       0.25     2.65          1          7.0   \n",
       "97         5.00          5.00       0.45     2.15          1          8.0   \n",
       "98         5.00          0.00       0.00     0.00          0          0.0   \n",
       "99         6.00          0.00       0.00     0.00          0          0.0   \n",
       "100        4.00          3.00       0.30     2.75          1          6.0   \n",
       "101        0.00          0.00       0.00     0.00          0          0.0   \n",
       "102        5.00          0.00       0.00     0.00          0          0.0   \n",
       "103        0.00          0.00       0.00     0.00          0          0.0   \n",
       "104        4.00          0.00       0.00     0.00          0          0.0   \n",
       "105        4.00          0.00       0.00     0.00          0          0.0   \n",
       "106        4.00          3.00       0.30     2.15          1          7.0   \n",
       "107        4.00          0.00       0.00     0.00          0          0.0   \n",
       "108        2.75          5.00       0.35     1.55          1          5.0   \n",
       "109        4.00          3.50       0.30     2.75          1          6.0   \n",
       "110        5.00          3.30       0.35     2.50          1          7.0   \n",
       "111        5.00          0.00       0.00     0.00          0          0.0   \n",
       "112        5.00          0.00       0.00     0.00          0          0.0   \n",
       "113        4.00          0.00       0.00     0.00          0          0.0   \n",
       "114        5.70          0.00       0.00     0.00          0          0.0   \n",
       "115        5.00          4.00       0.35     2.50          1          7.0   \n",
       "116        5.00          0.00       0.00     0.00          0          0.0   \n",
       "117        4.00          0.00       0.00     0.00          0          0.0   \n",
       "118        5.00          0.00       0.00     0.00          0          0.0   \n",
       "\n",
       "     XL_Cancel  X_Cancel_Binary  XL_Cancel_Binary  \n",
       "0            0                1                 0  \n",
       "1            0                1                 0  \n",
       "2            0                1                 0  \n",
       "3            0                0                 0  \n",
       "4            7                1                 1  \n",
       "5            0                1                 0  \n",
       "6            0                1                 0  \n",
       "7            0                1                 0  \n",
       "8            6                1                 1  \n",
       "9            0                1                 0  \n",
       "10           5                1                 1  \n",
       "11           0                1                 0  \n",
       "12           5                1                 1  \n",
       "13           5                1                 1  \n",
       "14           0                1                 0  \n",
       "15          10                1                 1  \n",
       "16           0                1                 0  \n",
       "17           0                1                 0  \n",
       "18           5                1                 1  \n",
       "19           0                1                 0  \n",
       "20           5                1                 1  \n",
       "21           5                1                 1  \n",
       "22           5                1                 1  \n",
       "23           0                1                 0  \n",
       "24           0                1                 0  \n",
       "25           0                1                 0  \n",
       "26           5                1                 1  \n",
       "27           0                1                 0  \n",
       "28           0                1                 0  \n",
       "29           6                1                 1  \n",
       "..         ...              ...               ...  \n",
       "89           0                1                 0  \n",
       "90           0                1                 0  \n",
       "91           0                1                 0  \n",
       "92           5                1                 1  \n",
       "93           0                1                 0  \n",
       "94           0                1                 0  \n",
       "95           6                1                 1  \n",
       "96           5                1                 1  \n",
       "97           5                1                 1  \n",
       "98           0                1                 0  \n",
       "99           0                1                 0  \n",
       "100          5                1                 1  \n",
       "101          0                1                 0  \n",
       "102          0                1                 0  \n",
       "103          0                0                 0  \n",
       "104          0                1                 0  \n",
       "105          0                1                 0  \n",
       "106          5                1                 1  \n",
       "107          0                1                 0  \n",
       "108          5                1                 1  \n",
       "109         10                1                 1  \n",
       "110          5                1                 1  \n",
       "111          0                1                 0  \n",
       "112          0                1                 0  \n",
       "113          0                1                 0  \n",
       "114          0                1                 0  \n",
       "115          5                1                 1  \n",
       "116          0                1                 0  \n",
       "117          0                1                 0  \n",
       "118          0                1                 0  \n",
       "\n",
       "[119 rows x 14 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Deleting X_Cancel\n",
    "usfares2014_=usfares2014.drop('X_Cancel', axis=1)\n",
    "usfares2014_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>US_City</th>\n",
       "      <th>X_Base Fare</th>\n",
       "      <th>X_Minute</th>\n",
       "      <th>X_Mile</th>\n",
       "      <th>X_Safety</th>\n",
       "      <th>X_Min Fare</th>\n",
       "      <th>XL_Base Fare</th>\n",
       "      <th>XL_Minute</th>\n",
       "      <th>XL_Mile</th>\n",
       "      <th>XL_Safety</th>\n",
       "      <th>XL_Min Fare</th>\n",
       "      <th>X_Cancel_Binary</th>\n",
       "      <th>XL_Cancel_Binary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>‘Akron’</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>‘Albuquerque’</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>‘Amarillo’</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>‘Anchorage’</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>‘Ann Arbor’</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.18</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         US_City  X_Base Fare  X_Minute  X_Mile  X_Safety  X_Min Fare  \\\n",
       "0        ‘Akron’          2.0      0.30    1.40         1         5.0   \n",
       "1  ‘Albuquerque’          1.3      0.25    1.75         1         5.0   \n",
       "2     ‘Amarillo’          1.9      0.25    1.75         1         5.7   \n",
       "3    ‘Anchorage’          2.5      0.25    2.10         1         6.0   \n",
       "4    ‘Ann Arbor’          1.3      0.18    1.30         1         4.0   \n",
       "\n",
       "   XL_Base Fare  XL_Minute  XL_Mile  XL_Safety  XL_Min Fare  X_Cancel_Binary  \\\n",
       "0           0.0        0.0      0.0          0          0.0                1   \n",
       "1           0.0        0.0      0.0          0          0.0                1   \n",
       "2           0.0        0.0      0.0          0          0.0                1   \n",
       "3           0.0        0.0      0.0          0          0.0                0   \n",
       "4           3.0        0.2      2.5          1          7.0                1   \n",
       "\n",
       "   XL_Cancel_Binary  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 1  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Deleting XL_Cancel\n",
    "usfares2014__=usfares2014_.drop('XL_Cancel', axis=1)\n",
    "usfares2014__.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Specifying Training and Test data further to feed into M.L. Logisitc Regressor. \n",
    "\n",
    "#### Training models with 80% of data points.\n",
    "#### Testing models with 20% data points.\n",
    "\n",
    "#### Training and Testing data further split up into x and y components.\n",
    "\n",
    "#### x is all data without cancel column (X/XL)\n",
    "#### y is all data with cancel column (X/XL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "#### One model will be made for X_Uber and another one will be made for XL_Uber. \n",
    "##### Also, for feature selection what contributes most to target variable is not Safety, but set fares.\n",
    "##### So Safety will be dropped from final dataset along with Min Fare. This feature (Min Fare) is a calculation based on other fares we are using as predictors.\n",
    "##### Thus, it does not contribute to our target value as well.\n",
    "##### Lastly, city names need to be taken out of the dataset since these are non-numerical. Fare contributors will be traced back to cities via indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X_Base Fare</th>\n",
       "      <th>X_Minute</th>\n",
       "      <th>X_Mile</th>\n",
       "      <th>X_Cancel_Binary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.9</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.18</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X_Base Fare  X_Minute  X_Mile  X_Cancel_Binary\n",
       "0          2.0      0.30    1.40                1\n",
       "1          1.3      0.25    1.75                1\n",
       "2          1.9      0.25    1.75                1\n",
       "3          2.5      0.25    2.10                0\n",
       "4          1.3      0.18    1.30                1"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_usfares2014 = usfares2014__[['X_Base Fare', 'X_Minute', 'X_Mile', 'X_Cancel_Binary']]\n",
    "X_usfares2014.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>XL_Base Fare</th>\n",
       "      <th>XL_Minute</th>\n",
       "      <th>XL_Mile</th>\n",
       "      <th>XL_Cancel_Binary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   XL_Base Fare  XL_Minute  XL_Mile  XL_Cancel_Binary\n",
       "0           0.0        0.0      0.0                 0\n",
       "1           0.0        0.0      0.0                 0\n",
       "2           0.0        0.0      0.0                 0\n",
       "3           0.0        0.0      0.0                 0\n",
       "4           3.0        0.2      2.5                 1"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XL_usfares2014 = usfares2014__[['XL_Base Fare', 'XL_Minute', 'XL_Mile', 'XL_Cancel_Binary']]\n",
    "XL_usfares2014.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data is now ready to be split into Training and Test datasets for Training data to create Logistic Regression model and performance checked with Test set data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_x = X_usfares2014.drop('X_Cancel_Binary', axis = 1)\n",
    "X_y = X_usfares2014['X_Cancel_Binary']\n",
    "\n",
    "XL_x = XL_usfares2014.drop('XL_Cancel_Binary', axis = 1)\n",
    "XL_y = XL_usfares2014['XL_Cancel_Binary']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X and XL data will not be split into Training and Test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "X_train_X, X_test_X, y_train_X, y_test_X = train_test_split(X_x, X_y, test_size=0.2)\n",
    "X_train_XL, X_test_XL, y_train_XL, y_test_XL = train_test_split(XL_x, XL_y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(95, 3) (95L,)\n",
      "(24, 3) (24L,)\n",
      "(95, 3) (95L,)\n",
      "(24, 3) (24L,)\n"
     ]
    }
   ],
   "source": [
    "#Check to see if data was split correctly.\n",
    "print X_train_X.shape, y_train_X.shape\n",
    "print X_test_X.shape, y_test_X.shape\n",
    "\n",
    "print X_train_XL.shape, y_train_XL.shape\n",
    "print X_test_XL.shape, y_test_XL.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now that data is split correctly, can feed data into Logistic Regression object.\n",
    "\n",
    "logreg_X = LogisticRegression()\n",
    "logreg_X.fit(X_train_X, y_train_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg_XL = LogisticRegression()\n",
    "logreg_XL.fit(X_train_XL, y_train_XL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on Uber_X test set: 0.96\n"
     ]
    }
   ],
   "source": [
    "y_pred_X = logreg_X.predict(X_test_X)\n",
    "print('Accuracy of logistic regression classifier on Uber_X test set: {:.2f}'.format(logreg_X.score(X_test_X, y_test_X)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on Uber_XL test set: 1.00\n"
     ]
    }
   ],
   "source": [
    "y_pred_XL = logreg_XL.predict(X_test_XL)\n",
    "print('Accuracy of logistic regression classifier on Uber_XL test set: {:.2f}'.format(logreg_XL.score(X_test_XL, y_test_XL)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Accuracy for Uber_XL higher compared to Uber_X due to balanced data set (similar amount of non-cancellations as there were cancellations). \n",
    "Given, there was not much variability/chance for there to be misclassification since data had an even amount of each classification value to train the model well. There was not much swing between cancellations and no cancellations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More Model Performance Metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1]\n",
      " [ 0 23]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix_X = confusion_matrix(y_test_X, y_pred_X)\n",
    "print(confusion_matrix_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::The result is telling us for Uber_X model, we have 23 correct predictions and 1 incorrect prediction. Not bad."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[13  0]\n",
      " [ 0 11]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix_XL = confusion_matrix(y_test_XL, y_pred_XL)\n",
    "print(confusion_matrix_XL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::The result is telling us for Uber_XL model, we have 24 correct predictions and 0 incorrect prediction. Not bad at all."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "Computing Precision, Recall, F-Score and Support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.00      0.00      0.00         1\n",
      "          1       0.96      1.00      0.98        23\n",
      "\n",
      "avg / total       0.92      0.96      0.94        24\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mindy\\Anaconda2\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test_X, y_pred_X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00        13\n",
      "          1       1.00      1.00      1.00        11\n",
      "\n",
      "avg / total       1.00      1.00      1.00        24\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test_XL, y_pred_XL))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Borrowing from scikit learn:\n",
    "\n",
    "The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier to not label a sample as positive if it is negative.\n",
    "\n",
    "The recall is the ratio tp / (tp + fn) where tp is the number of true positives and fn the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples.\n",
    "\n",
    "The F-beta score can be interpreted as a weighted harmonic mean of the precision and recall, where an F-beta score reaches its best value at 1 and worst score at 0.\n",
    "\n",
    "The F-beta score weights the recall more than the precision by a factor of beta. beta = 1.0 means recall and precision are equally important.\n",
    "\n",
    "The support is the number of occurrences of each class in y_test."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::Some comments about the Classification Reports for Uber_X and Uber_XL data:\n",
    "\n",
    "There is data imbalance for Uber_X since most of the fares resulted in non-cancellations 23/24 = 95.8%.\n",
    "For Uber_XL, the data was more balanced - almost 50% for cancellations and 50% for pick-up's. Therefore Uber_XL model performed better than Uber_X since the data quality was better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "Now, and finally, the ROC Curve:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf749a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "logit_roc_auc = roc_auc_score(y_test_X, logreg_X.predict(X_test_X))\n",
    "fpr, tpr, thresholds = roc_curve(y_test_X, logreg_X.predict_proba(X_test_X)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic for Uber_X')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf925668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logit_roc_auc = roc_auc_score(y_test_XL, logreg_XL.predict(X_test_XL))\n",
    "fpr, tpr, thresholds = roc_curve(y_test_XL, logreg_XL.predict_proba(X_test_XL)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic for Uber_XL')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "::Some comments on ROC Curve for Uber_X and Uber_XL:\n",
    "            \n",
    "The ROC Curve is another common tool used to assess how well a binary classifier did. \n",
    "The dotted red line represents a purely random classifier.\n",
    "The ideal situation is for the Logisitc Regression curve to be in the top left corner.\n",
    "\n",
    "As expected Uber_XL ROC Curve outperforms that of Uber_X since Uber_XL area under the curve = 1.00, which represents the perfect classifier. What determines a good classifier is what the threshold is set at to classify as 0 or 1. Due to Uber_XL having even data to start with in order to train its logistic regression model, its ability to correctly classify unseen data would be better than Uber_X's dataset that has a huge skewing towards non-cancellations. Therefore, Uber_X's ROC Curve area is 0.5, which is the baseline."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In conclusion, if you want a strong model to predict whether or not fares set are going to lead to cancellations or not, the Uber_XL Logistic Regression model would be the best one to use. This model could be used in real-time to see what pricing strategy response would be. If price adjustments need to be made to increase completed trips then changes can be made more swiftly in order to not lose out on ROI.\n",
    "\n",
    "If you would like pricing data that leads to less cancellations, Uber_X would be the best data set to use as reference to where to set pricing since the data itself has a high skewing towards non-cancellations.\n",
    "\n",
    "Therefore, even though Uber_XL logisic regression model outperformed Uber_X's, Uber_X had the better pricing strategy. So Uber_XL will need to reset its pricing to capitalize more on interested Uber riders."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}