flight_delays.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flight Delays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project involves analyzing flight data from a large travel agency and trying to predict whether or not a flight will be canceled based on several factors.  The data includes a variable called 'Canceled' that will act as the response variable.  The other variables provided include the month of the flight, the departure time, the unique carrier code, the scheduled length of the flight in minutes, the arrival delay, thee departure delay, and the distance in miles of the flight."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|Field|Data Type|Description|Type of Variable|Variable Category|\n",
    "|-|-|-|-|-|\n",
    "|Canceled|Binary|Canceled = 1|Target|Categorical|\n",
    "|Month|Integer|Jan = 1|Predictor|Ordinal|\n",
    "|DepartureTime|Integer|Military Time (1:00 PM  = 1300)|Predictor|Continuous|\n",
    "|UniqueCarrier|String|Airline Carrier Code|Predictor|Categorical|\n",
    "|SchedElapsedTime|Integer|Scheduled flight time in minutes|Predictor|Continuous|\n",
    "|ArrDelay|Integer|Arrival delay in minutes||Continuous|\n",
    "|DepDelay|Integer|Departure delay in minutes||Continuous|\n",
    "|Distance|Integer|Distance in miles|Predictor|Continuous|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One thing to note is that ArrDelay and DepDelay cannot be calculated until after a flight either takes off or is cancelled.  Since our objective is to predict whether a future flight will be cancelled or not, we will not have these metrics available to us and should not include them in any models.  \n",
    "\n",
    "We will also most likely need to convert the UniqueCarrier variable to one-hot encoded dummy variables in order to include this metric in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "headers = ['Canceled','Month','DepartureTime','UniqueCarrier','SchedElapsedTime','ArrDelay','DepDelay','Distance']\n",
    "\n",
    "df = pd.read_csv('Data/Data_PredictingFlightDelays.csv',names=headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Canceled</th>\n",
       "      <th>Month</th>\n",
       "      <th>DepartureTime</th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>SchedElapsedTime</th>\n",
       "      <th>ArrDelay</th>\n",
       "      <th>DepDelay</th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>814</td>\n",
       "      <td>UA</td>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>830</td>\n",
       "      <td>DL</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1835</td>\n",
       "      <td>UA</td>\n",
       "      <td>213</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1730</td>\n",
       "      <td>AA</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1442</td>\n",
       "      <td>UA</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Canceled  Month  DepartureTime UniqueCarrier  SchedElapsedTime  ArrDelay  \\\n",
       "0         1     12            814            UA               134         0   \n",
       "1         1     12            830            DL                90         0   \n",
       "2         1      1           1835            UA               213         0   \n",
       "3         1      4           1730            AA                80         0   \n",
       "4         1      7           1442            UA               103         0   \n",
       "\n",
       "   DepDelay  Distance  \n",
       "0         0       679  \n",
       "1         0       214  \n",
       "2         0      1605  \n",
       "3         0       235  \n",
       "4         0       413  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6000 entries, 0 to 5999\n",
      "Data columns (total 8 columns):\n",
      "Canceled            6000 non-null int64\n",
      "Month               6000 non-null int64\n",
      "DepartureTime       6000 non-null int64\n",
      "UniqueCarrier       6000 non-null object\n",
      "SchedElapsedTime    6000 non-null int64\n",
      "ArrDelay            6000 non-null int64\n",
      "DepDelay            6000 non-null int64\n",
      "Distance            6000 non-null int64\n",
      "dtypes: int64(7), object(1)\n",
      "memory usage: 375.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're in luck!  This appears to be a complete dataset with no missing values.  Therefore, we will not need to perform any data imputation.  There are 6000 entries.  All columns are integers, except for UniqueCarrier.  This is expected.   \n",
    "\n",
    "Next, we will begin performing exploratory data analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis  \n",
    "### Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's look at some summary statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Canceled</th>\n",
       "      <th>Month</th>\n",
       "      <th>DepartureTime</th>\n",
       "      <th>SchedElapsedTime</th>\n",
       "      <th>ArrDelay</th>\n",
       "      <th>DepDelay</th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6000.000000</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>6000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>6.220167</td>\n",
       "      <td>1330.466500</td>\n",
       "      <td>161.693500</td>\n",
       "      <td>8.113000</td>\n",
       "      <td>9.148833</td>\n",
       "      <td>987.588833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.372709</td>\n",
       "      <td>3.436325</td>\n",
       "      <td>459.118234</td>\n",
       "      <td>78.064979</td>\n",
       "      <td>35.763134</td>\n",
       "      <td>31.669299</td>\n",
       "      <td>646.666931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>-72.000000</td>\n",
       "      <td>-16.000000</td>\n",
       "      <td>68.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>930.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>547.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1325.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>837.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1715.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1249.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2359.000000</td>\n",
       "      <td>604.000000</td>\n",
       "      <td>410.000000</td>\n",
       "      <td>387.000000</td>\n",
       "      <td>4502.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Canceled        Month  DepartureTime  SchedElapsedTime     ArrDelay  \\\n",
       "count  6000.000000  6000.000000    6000.000000       6000.000000  6000.000000   \n",
       "mean      0.166667     6.220167    1330.466500        161.693500     8.113000   \n",
       "std       0.372709     3.436325     459.118234         78.064979    35.763134   \n",
       "min       0.000000     1.000000       5.000000         40.000000   -72.000000   \n",
       "25%       0.000000     3.000000     930.000000        105.000000    -8.000000   \n",
       "50%       0.000000     6.000000    1325.000000        145.000000     0.000000   \n",
       "75%       0.000000     9.000000    1715.000000        200.000000    10.000000   \n",
       "max       1.000000    12.000000    2359.000000        604.000000   410.000000   \n",
       "\n",
       "          DepDelay     Distance  \n",
       "count  6000.000000  6000.000000  \n",
       "mean      9.148833   987.588833  \n",
       "std      31.669299   646.666931  \n",
       "min     -16.000000    68.000000  \n",
       "25%      -3.000000   547.000000  \n",
       "50%       0.000000   837.000000  \n",
       "75%       6.000000  1249.000000  \n",
       "max     387.000000  4502.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One point that sticks out immediately is that the 'Canceled' variable appears to be imbalanced.  If canceled and non-canceled flights were distributed evenly, the mean of this variable would be 0.5.  However, the actual mean is 0.167 and the third quartile value is 0.  This suggests that cancellations are highly imbalanced.  We will need to account for this when creating a prediction model.  \n",
    "\n",
    "Next, let's look at the distributions for the indivdual variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x209c4acd7b8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FROQcHlkh3HnnnQwbNgyAo0ePEhQUxNatWxk8eDAAQ4cOpbi4GB8fH/r3709AQAABAQGE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\nSUEQERFAQRAREZOCICIiAPw/8tkvitxcg+IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x209c11e12e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(df['Canceled'])\n",
    "ax.set_title('Canceled Distribution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we saw when running the summary statistics, the dependent variable 'Canceled' is not balanced.  There are about 5000 flights that were not canceled and only about 1000 flights that were."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x209c4d545c0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QBQsW4HK5aN68OSkpKdSvX9+0vKVLl5ZctiM3Nxe3221q3v3338/HH3+MYRjk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\nImIxKn4REYtR8YuIWMz/AQ4AZId0715JAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x209c4ac5748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(df['Month'])\n",
    "ax.set_title('Month Distribution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Flights are distributed fairly evenly among the months, but there is a slight decline from the beginning to the end of the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x209c500a3c8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4HQSd+czfpQghAoC+KBuNtYQf3HV3hxg/gFYEwunTp9mxYweFhYV00AVS21RS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\ngASCEEKIBhIIQgghAAkEIYQQDf4/KbjwkQtDiNMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x209c495d9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.violinplot(y='DepartureTime',data=df)\n",
    "ax.set_title('DepartureTime Distribution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Departure times are fairly normally distributed with a very slight tail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x209c507b320>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IiAAKBBERsVMgiIgIoEAQERE7BYKIiAAPyLOM5N52MfEPni7hvlcudrenS5C7gGYIIiIC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\njfXVV1/x3nvv8fb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x209c2e184e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(df['UniqueCarrier'])\n",
    "ax.set_title('UniqueCarrier Distribution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For UniqueCarrier, we have three values - UA, DL, and AA.  United Airlines and Delta (UA and DL respectively) both have about 1750 flights, while American Airlines (AA) has about 2500."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x209c50df2e8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3Dz30EIcOHULTNB555BFMJhNz5sxB0zT69OnD/Pnz0el0rFy5khUrVmAwGJgx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\nbgeGijz0FYcxHssFRxUA0dG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oHXsoLi6iqLiIoqJCjhbn4Ha762yjBZnwBJlxGcOO7wzC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\nDkaCXwghOhgJfiGE6GAk+IUQooP5/0F1MB+4OXPVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x209c4fcbcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.violinplot(y='SchedElapsedTime',data=df)\n",
    "ax.set_title('SchedElapsedTime Distribution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scheduled elapsed times are highly skewed with a long right tail.  We will most likely need to transform this variable before building a model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x209c5146ba8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mY5vQnTnO8OEj+clPniImJjbYZYkg6SgQ5KuCCEv/8z+/Z82a1TiTsrFm3REy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\nEuaeTUUuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x209c51172b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.violinplot(y='Distance',data=df)\n",
    "ax.set_title('Distance Distribution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distance is also highly right-tailed.  We will also need to transform this variable before modeling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bivariate analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x209c5132898>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QRk4zkDNMhBUZWd0oe9/tfmuG916HVBnprF4Ud8ms3jf+BiQBqLJUdG/tuuxz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8rWXjEfiB3Pf06eAaSBkfO5PyvD/vzJM4A8GFyPPGjtt4kd9+Yr3C7Vvt2W/a9YOdvvivUnqM1S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BVnhmHEx5ydoe2pwiAmflLGGHk2HNSTu8/FJxbPNK8eyvfVrYMnspRjSAtOrxtB18djLSJW9h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gmCIAiCIAiCIIiCg5xdgiAIgiAIgiAIouDoMYEqgiAIgsg2lX/qn9H1jTd/2001iZNpnYCe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O3v36/nype5teK8cYN1cE07KD03AHilXAmlJtLpY6mIjJX6v3y6vl/6/chplMb06EOohp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\n8f8BseIvV8YlJewAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x209c5132d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df[['Canceled','Month','DepartureTime','SchedElapsedTime','Distance']],hue='Canceled')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there is a strong correlation between scheduled elapsed time and distance.  This makes sense, as we would expect longer distances to take more time to cover and shorter distances to take less time.  We will only want to include one of these variables in our model.  \n",
    "\n",
    "There also appears to be a relationship between cancellation rate and distance/scheduled elapsed time.  Longer flights do not seem to get canceled as often as shorter flights.  Though this may just be due to there being fewer long flights.  \n",
    "\n",
    "Let's look closer at these relationships and see what the correlation between these variables is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x209c7488978>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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khSgKkjdbkTROdvr06fjTn/6E69evw8PDw1TVEhHZkhIqWUlJtkuXLli/fj0uXrwIT09P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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x209c7488240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df[['SchedElapsedTime','Distance']].corr()\n",
    "\n",
    "sns.heatmap(corr,annot=True,cmap='coolwarm',vmin=-1,vmax=1,lw=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our suspicion that SchedElapsedTime and Distance are highly correlated is confirmed.  These two variables have a correlation of 0.98.  \n",
    "\n",
    "Next, let's use t-tests to determine if the Canceled variable has an association with either the SchedElapsedTime or Distance variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will compare the mean value of the SchedElapsedTime variable between canceled and non-canceled flights.  We want to use a two-tailed t-test, because we are interested in a difference of means in either direction.  As we noticed earlier, the number of Canceled and Non-Canceled flights is not balanced.  Therefore, we will use Welch's t-test.  \n",
    "\n",
    "This test assumes normally distributed data.  As we observed earlier, the SchedElapsedTime and Distance variables are not distributed normally.  So, first, we need to transform these variables.  we will use the Box-Cox transformation to do this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x209c785cbe0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tL+rI07PtnHrHHYZBQUHIz8+HUqmEXq/H5cuXERERcccBEvJHedUa/Hi9AlF+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x209c7827630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(stats.boxcox(df['SchedElapsedTime'])[0])\n",
    "ax.set_title('Distribution of Transformed SchedElapsedTime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x209c7948160>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S0sjKymqw3Ol08uKLL5KcnNziMkI05r1t+ZRW1/G7SxL1DiVgmAwavx2ewJ5C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\nuQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x209c4d17550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(stats.boxcox(df['Distance'])[0])\n",
    "ax.set_title('Distribution of Transformed Distance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These variables are much more normally distributed after transforming them.  We should now be able to perform the t-tests using these transformed variables.  For each of these t-tests, the null hypothesis is that the means between the two groups (canceled and non-canceled flights) are the same.  The alternative hypothesis is that the mean value between the groups are not the same.  we will use an alpha of 0.05 for a confidence level of 95%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SchedElapsedTime for Canceled vs. Non-canceled Flights\n",
      "------------------------------------------------------\n",
      "t-statistic: 17.5829803162\n",
      "p-value: 1.80137246458e-63\n"
     ]
    }
   ],
   "source": [
    "t, p = stats.ttest_ind(stats.boxcox(df[df['Canceled'] == 0].SchedElapsedTime)[0], \n",
    "                       stats.boxcox(df[df['Canceled'] == 1].SchedElapsedTime)[0], \n",
    "                       equal_var=False)\n",
    "\n",
    "print('SchedElapsedTime for Canceled vs. Non-canceled Flights')\n",
    "print('------------------------------------------------------')\n",
    "print('t-statistic: %s' % t)\n",
    "print('p-value: %s' % p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When performing the t-test for SchedElapsedTime, the t-statistic is 17.58 and the p-value is 1.80e-63.  This p-value is below our alpha of 0.05 and therefore we can reject the null hypothesis.  There is statistically significant evidence that the mean SchedElapsedTime values for canceled and non-canceled flights are not the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance for Canceled vs. Non-canceled Flights\n",
      "------------------------------------------------------\n",
      "t-statistic: -8.44507531319\n",
      "p-value: 7.54177712715e-17\n"
     ]
    }
   ],
   "source": [
    "t, p = stats.ttest_ind(stats.boxcox(df[df['Canceled'] == 0].Distance)[0], \n",
    "                       stats.boxcox(df[df['Canceled'] == 1].Distance)[0], \n",
    "                       equal_var=False)\n",
    "\n",
    "print('Distance for Canceled vs. Non-canceled Flights')\n",
    "print('------------------------------------------------------')\n",
    "print('t-statistic: %s' % t)\n",
    "print('p-value: %s' % p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When performing the t-test for Distance, the t-statistic is -8.45 and the p-value is 7.54e-17. This p-value is also below our alpha of 0.05 and therefore we can reject the null hypothesis for this test as well. There is statistically significant evidence that the mean Distance values for canceled and non-canceled flights are not the same.  \n",
    "\n",
    "We stated earlier that we only want to include one of these two variables at most in our model  The magnitude of the t-statistic for the t-test involving SchedElapsedTime is higher than for the test involving Distance.  Therefore, we will consider SchedElapsedTime for inclusion in the model and not use Distance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's see if there are relationships between the carrier ID and the other variables.  \n",
    "\n",
    "We will start by performing a chi-squared test for independence between UniqueCarrier and Canceled.  The null hypothesis is that these two variables have no association.  The alternative hypothesis assumes that there is an association between the two variables.  We will select an alpha of 0.05, meaning that this test should provide results at a 95% confidence level. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Canceled vs. UniqueCarrier\n",
      "------------------------------\n",
      "Chi-squared Test Statistic: 55.6952351721\n",
      "P-value: 8.05254848157e-13\n",
      "Degrees of Freedom: 2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def print_chi2(df, col1, col2):\n",
    "    obs = pd.crosstab(df[col1], df[col2])\n",
    "\n",
    "    chi2, p, dof, expected = stats.chi2_contingency(obs)\n",
    "\n",
    "    print('%s vs. %s' % (col1, col2))\n",
    "    print('------------------------------')\n",
    "    print('Chi-squared Test Statistic: %s' % chi2)\n",
    "    print('P-value: %s' % p)\n",
    "    print('Degrees of Freedom: %s\\n' % dof)\n",
    "    \n",
    "print_chi2(df, 'Canceled', 'UniqueCarrier')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get a chi-squared test statistic of 55.7 with 2 degrees of freedom, which produces a p-value of 8.05e-13.  This is well below our alpha of 0.05, so we can safely reject the null hypothesis that there is no association between these two variables.  There is statistically significant evidence that there is a relationship between the unique carrier ID and cancellation rate.  \n",
    "\n",
    "Let's dig further into this and see which airlines are more likely to have canceled flights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x209c8a35320>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IgVK7heauXbuYPn06CQkJ7N27l5iYGFxcXHB3d2fKlClUqVKFiRMnkpqaisVi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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x209c79b0b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x='UniqueCarrier',data=df,hue='Canceled',dodge=False)\n",
    "ax.set_title('Number of Flights by Carrier and Cancellation Status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>AA</th>\n",
       "      <th>DL</th>\n",
       "      <th>UA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canceled</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1983</td>\n",
       "      <td>1499</td>\n",
       "      <td>1518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>493</td>\n",
       "      <td>189</td>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "UniqueCarrier    AA    DL    UA\n",
       "Canceled                       \n",
       "0              1983  1499  1518\n",
       "1               493   189   318"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['Canceled'],df['UniqueCarrier'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If going strictly by raw numbers of canceled flights, American Airlines leads the pack with 493, compared with 318 and 189 for United and Delta respectively.  However, American also has more flights overall.  Let's take a look at the percentages of each carrier's flights that are canceled instead of just the raw counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>AA</th>\n",
       "      <th>DL</th>\n",
       "      <th>UA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canceled</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.800889</td>\n",
       "      <td>0.888033</td>\n",
       "      <td>0.826797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.199111</td>\n",
       "      <td>0.111967</td>\n",
       "      <td>0.173203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "UniqueCarrier        AA        DL        UA\n",
       "Canceled                                   \n",
       "0              0.800889  0.888033  0.826797\n",
       "1              0.199111  0.111967  0.173203"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['Canceled'],df['UniqueCarrier']).apply(lambda r: r/r.sum(), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When looking at percentages, the airlines finish in the same order as the raw counts.  American cancels the most of their flights with about 20%, where United and Delta cancel about 17% and 11% of their flights respectively.  So, according to the data that we have, a flight is more likely to be canceled if it is operated by American Airlines than either of the other two airlines.  \n",
    "\n",
    "On the other hand, Delta has a cancellation rate that is only slightly over half of that of either American or United.  Only about 11% of Delta's flights get canceled, compared with 20% and 17% for American and United respectively.  In this sense, Delta is the most reliable of these three (at least in the sample of flights that is accounted for by this data)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's compare Month and UniqueCarrier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x209c8ab6be0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wNm7cSGBgoKv7EhERFynUPv7u3btTvnx51q9fz5IlS+jZs+cVl9myZQsxMTEA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\nlAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x209c798b2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x='Month',data=df,hue='UniqueCarrier')\n",
    "ax.set_title('Number of Flights by Carrier and Month')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's a bit difficult to tell if there is a relationship between Month and UniqueCarrier from just looking at the countplot.  American Airlines appears to be on a bit of a different monthly schedule than the other two airlines, but this could just be noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Month vs. UniqueCarrier\n",
      "------------------------------\n",
      "Chi-squared Test Statistic: 96.3835626734\n",
      "P-value: 2.74633914661e-11\n",
      "Degrees of Freedom: 22\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_chi2(df, 'Month', 'UniqueCarrier')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Since Month is an ordinal variable and UniqueCarrier is a categorical variable, we can use a chi-squared test for independence with these variables.  When we do, we get a test statistic of 96.38 with 22 degrees of freedom.  This produces a p-value of 2.75e-11, which is below our alpha of 0.05.  We can reject the null hypothesis that these two variables are not related.  There is statistically significant evidence that Month and UniqueCarrier are not independent of each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We will need to use ANOVA to compare UniqueCarrier with DepartureTime and SchedElapsedTime, as we will have more than 2 groups to compare (since there are 3 distinct carrier codes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x209c8bb32e8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+jBo1ik8//RRPT09uv/12du/eXWg/LVq0YO3atbRr146yZctSrlw5xo0bR9WqVRk9ejTP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\nD1bVXnb3nkP+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x209c7a395f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x='UniqueCarrier',y='DepartureTime',data=df)\n",
    "ax.set_title('Comparison of Mean DepartureTime by UniqueCarrier')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the boxplot, it does not appear that there is a relationship between DepartureTime and UniqueCarrier.  Let's run an ANOVA to verify that though."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DepartureTime for AA vs. DL vs. UA Flights\n",
      "------------------------------------------------------\n",
      "F-statistic: -8.44507531319\n",
      "p-value: 0.612742978025\n"
     ]
    }
   ],
   "source": [
    "f, p = stats.f_oneway(df[df['UniqueCarrier'] == 'AA'].DepartureTime,\n",
    "                      df[df['UniqueCarrier'] == 'DL'].DepartureTime,\n",
    "                      df[df['UniqueCarrier'] == 'UA'].DepartureTime)\n",
    "\n",
    "print('DepartureTime for AA vs. DL vs. UA Flights')\n",
    "print('------------------------------------------------------')\n",
    "print('F-statistic: %s' % t)\n",
    "print('p-value: %s' % p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When comparing flights for the mean DepartureTime for the three different airlines, we get an F-statistic of -8.45 and a p-value of 0.61.  This p-value is greater than our alpha of 0.05, so we cannot reject the null hypothesis that the means among these groups are the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x209c8c4f278>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o0IHExMQSrzkxMZHs7GzMZjPx8fEEBgZy9epVevTogYeHB6NHjyYiIoLjx4/j7u5OmzZt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x209c6c51080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x='UniqueCarrier',y='SchedElapsedTime',data=df)\n",
    "ax.set_title('Comparison of Mean SchedElapsedTime by UniqueCarrier')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the boxplot for SchedElapsedTime vs. UniqueCarrier, it again does not look like there is a relationship.  However, there are lots of outliers that could cause issues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SchedElapsedTime for AA vs. DL vs. UA Flights\n",
      "------------------------------------------------------\n",
      "F-statistic: -8.44507531319\n",
      "p-value: 0.0\n"
     ]
    }
   ],
   "source": [
    "f, p = stats.f_oneway(stats.boxcox(df[df['UniqueCarrier'] == 'AA'].SchedElapsedTime)[0],\n",
    "                      stats.boxcox(df[df['UniqueCarrier'] == 'DL'].SchedElapsedTime)[0],\n",
    "                      stats.boxcox(df[df['UniqueCarrier'] == 'UA'].SchedElapsedTime)[0])\n",
    "\n",
    "print('SchedElapsedTime for AA vs. DL vs. UA Flights')\n",
    "print('------------------------------------------------------')\n",
    "print('F-statistic: %s' % t)\n",
    "print('p-value: %s' % p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "When performing an ANOVA for SchedElapsedTime (after transforming it) and UniqueCarrier, we get an F-statistic of -8.45 and a p-value of 0.  This is obviously less than our alpha of 0.05, meaning we can reject the null hypothesis that the means of these groups are the same.  However, this result may have been influenced by the outliers that we noticed earlier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to compare DepartureTime with cancellation rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x209c8ceacf8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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e/dQllpSq4FIgHDp0iEOHDvHaa685582YMQOLxcLLL79cbcVVJ30K+0Wm41PY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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x209c7914668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x='Canceled',y='DepartureTime',data=df)\n",
    "ax.set_title('Comparison of Mean DepartureTime by Canceled')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean departure time appears to be similar for canceled and non-canceled flights, although the standard deviation is larger for non-canceled flights.  Let's run a t-test to see if that is actually the case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Departure Time for Canceled vs. Non-canceled Flights\n",
      "------------------------------------------------------\n",
      "t-statistic: 19.7855672471\n",
      "p-value: 1.14937551898e-78\n"
     ]
    }
   ],
   "source": [
    "t, p = stats.ttest_ind(stats.boxcox(df[df['Canceled'] == 0].DepartureTime)[0], \n",
    "                       stats.boxcox(df[df['Canceled'] == 1].DepartureTime)[0], \n",
    "                       equal_var=False)\n",
    "\n",
    "print('Departure Time for Canceled vs. Non-canceled Flights')\n",
    "print('------------------------------------------------------')\n",
    "print('t-statistic: %s' % t)\n",
    "print('p-value: %s' % p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When running a t-test, we get a t-statistic of 19.79 and a p-value of 1.15e-78.  This is less than our alpha of 0.05, so we can reject the null hypothesis that the mean departure time is the same between canceled and non-canceled flights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Feature Engineering  \n",
    "### Variable Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have explored our data, it is time to modify it.  We will be encapsulating all of these steps in a function later. For now, we can create a copy of the original dataframe in order to test while having the original data to fall back on in case we make any mistakes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to perform the transformation on SchedElapsedTime that was discussed earlier.  The transformed values will be stored in a new column called SchedElapsedTime_transform.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Canceled</th>\n",
       "      <th>Month</th>\n",
       "      <th>DepartureTime</th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>SchedElapsedTime</th>\n",
       "      <th>ArrDelay</th>\n",
       "      <th>DepDelay</th>\n",
       "      <th>Distance</th>\n",
       "      <th>SchedElapsedTime_transform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>814</td>\n",
       "      <td>UA</td>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>679</td>\n",
       "      <td>3.979778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>830</td>\n",
       "      <td>DL</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>214</td>\n",
       "      <td>3.716421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1835</td>\n",
       "      <td>UA</td>\n",
       "      <td>213</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1605</td>\n",
       "      <td>4.275034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1730</td>\n",
       "      <td>AA</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>235</td>\n",
       "      <td>3.636706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1442</td>\n",
       "      <td>UA</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>413</td>\n",
       "      <td>3.806725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Canceled  Month  DepartureTime UniqueCarrier  SchedElapsedTime  ArrDelay  \\\n",
       "0         1     12            814            UA               134         0   \n",
       "1         1     12            830            DL                90         0   \n",
       "2         1      1           1835            UA               213         0   \n",
       "3         1      4           1730            AA                80         0   \n",
       "4         1      7           1442            UA               103         0   \n",
       "\n",
       "   DepDelay  Distance  SchedElapsedTime_transform  \n",
       "0         0       679                    3.979778  \n",
       "1         0       214                    3.716421  \n",
       "2         0      1605                    4.275034  \n",
       "3         0       235                    3.636706  \n",
       "4         0       413                    3.806725  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['SchedElapsedTime_transform'] = stats.boxcox(df2['SchedElapsedTime'])[0]\n",
    "\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also remove the columns that were noted earlier as well as the original SchedElapsedTime column to simplify things."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2.drop(['ArrDelay','DepDelay','Distance','SchedElapsedTime'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Canceled</th>\n",
       "      <th>Month</th>\n",
       "      <th>DepartureTime</th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>SchedElapsedTime_transform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>814</td>\n",
       "      <td>UA</td>\n",
       "      <td>3.979778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>830</td>\n",
       "      <td>DL</td>\n",
       "      <td>3.716421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1835</td>\n",
       "      <td>UA</td>\n",
       "      <td>4.275034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1730</td>\n",
       "      <td>AA</td>\n",
       "      <td>3.636706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1442</td>\n",
       "      <td>UA</td>\n",
       "      <td>3.806725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Canceled  Month  DepartureTime UniqueCarrier  SchedElapsedTime_transform\n",
       "0         1     12            814            UA                    3.979778\n",
       "1         1     12            830            DL                    3.716421\n",
       "2         1      1           1835            UA                    4.275034\n",
       "3         1      4           1730            AA                    3.636706\n",
       "4         1      7           1442            UA                    3.806725"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should also replace the UniqueCarrier field with dummy variables, as was noted earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = pd.get_dummies(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Canceled</th>\n",
       "      <th>Month</th>\n",
       "      <th>DepartureTime</th>\n",
       "      <th>SchedElapsedTime_transform</th>\n",
       "      <th>UniqueCarrier_AA</th>\n",
       "      <th>UniqueCarrier_DL</th>\n",
       "      <th>UniqueCarrier_UA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>814</td>\n",
       "      <td>3.979778</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>830</td>\n",
       "      <td>3.716421</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1835</td>\n",
       "      <td>4.275034</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1730</td>\n",
       "      <td>3.636706</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1442</td>\n",
       "      <td>3.806725</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Canceled  Month  DepartureTime  SchedElapsedTime_transform  \\\n",
       "0         1     12            814                    3.979778   \n",
       "1         1     12            830                    3.716421   \n",
       "2         1      1           1835                    4.275034   \n",
       "3         1      4           1730                    3.636706   \n",
       "4         1      7           1442                    3.806725   \n",
       "\n",
       "   UniqueCarrier_AA  UniqueCarrier_DL  UniqueCarrier_UA  \n",
       "0                 0                 0                 1  \n",
       "1                 0                 1                 0  \n",
       "2                 0                 0                 1  \n",
       "3                 1                 0                 0  \n",
       "4                 0                 0                 1  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's encapsulate all of the previous data modification steps into a function that can be re-used later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def clean_data(df):\n",
    "    # Create a copy of the original dataframe so the original is not modified\n",
    "    df2 = df.copy()\n",
    "    \n",
    "    # Transform SchedElapsedTime\n",
    "    df2['SchedElapsedTime_transform'] = stats.boxcox(df2['SchedElapsedTime'])[0]\n",
    "\n",
    "    # Drop unnecessary columns\n",
    "    df2.drop(['ArrDelay','DepDelay','Distance','SchedElapsedTime'],axis=1,inplace=True)\n",
    "    \n",
    "    # Create dummy variables\n",
    "    df2 = pd.get_dummies(df2)\n",
    "    \n",
    "    # Return cleaned dataframe\n",
    "    return df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can test the data cleaning function by creating another copy of the original dataframe and comparing it with df2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = clean_data(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Canceled</th>\n",
       "      <th>Month</th>\n",
       "      <th>DepartureTime</th>\n",
       "      <th>SchedElapsedTime_transform</th>\n",
       "      <th>UniqueCarrier_AA</th>\n",
       "      <th>UniqueCarrier_DL</th>\n",
       "      <th>UniqueCarrier_UA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Canceled Month DepartureTime SchedElapsedTime_transform UniqueCarrier_AA  \\\n",
       "0     True  True          True                       True             True   \n",
       "\n",
       "  UniqueCarrier_DL UniqueCarrier_UA  \n",
       "0             True             True  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = (df1 == df2)\n",
    "test.drop_duplicates(inplace=True)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new dataset df1 is an exact duplicate of df2.  This means that the clean_data() function works properly.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the data has been explored and cleaned, we can begin the process of building a model.  First we need to create our train and test datasets.  We will hold out 20% of the data as a test set and use the rest to train the model.  We will also split the dependent variable ('Canceled') out from the independent variables.  This will result in 4 new variables: X_train, X_test, y_train, and y_test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = df1['Canceled']\n",
    "X = df1.drop('Canceled',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=888)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to avoid overfitting the model, we will use stratified k-fold cross validation on the training dataset.  We will start by using 10 folds and adjust accordingly if model computation time is too lengthy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.linear_model import LogisticRegressionCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to create the cross validation object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "kfold = StratifiedKFold(n_splits=10, random_state=888)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create the model.  We will try using logistic regression first.  Since the dependent variable is not distributed evenly, we will used balanced weighting.  \n",
    "\n",
    "In order to constrain the scope of this project, we will simply build the model using all of the remaining independent variables.  However, in an ideal situation more time would be spent on feature selection using algorithms such as LASSO or stepwise selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=10, class_weight='balanced',\n",
       "           cv=StratifiedKFold(n_splits=10, random_state=888, shuffle=False),\n",
       "           dual=False, fit_intercept=True, intercept_scaling=1.0,\n",
       "           max_iter=100, multi_class='ovr', n_jobs=1, penalty='l2',\n",
       "           random_state=888, refit=True, scoring=None, solver='lbfgs',\n",
       "           tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model = LogisticRegressionCV(cv=kfold,class_weight='balanced',random_state=888)\n",
    "lr_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many metrics that can be used to evaluate a model.  Given that the dependent variable in this case is unbalanced, accuracy is probably not the best metric to use in model evaluation.  Therefore, we will also look at other metrics such as logarithmic loss and area under the ROC curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import make_scorer, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Accuracy: 0.590831891993\n",
      "Mean Log Loss: -0.66432446031\n",
      "Mean Area Under ROC Curve: 0.635162975792\n"
     ]
    }
   ],
   "source": [
    "def print_eval_scores(model, X_train, y_train, cv):\n",
    "    accuracy = cross_val_score(model, X_train, y_train, cv=cv,scoring='accuracy')\n",
    "    log_loss = cross_val_score(model, X_train, y_train, cv=cv, scoring='neg_log_loss')\n",
    "    roc_auc = cross_val_score(model, X_train, y_train, cv=cv, scoring='roc_auc')\n",
    "\n",
    "    print('Mean Accuracy: %s' % accuracy.mean())\n",
    "    print('Mean Log Loss: %s' % log_loss.mean())\n",
    "    print('Mean Area Under ROC Curve: %s' % roc_auc.mean())\n",
    "    \n",
    "print_eval_scores(lr_model, X_train, y_train, kfold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean accuracy of this model across the ten folds used in cross validation is about 58.7%.  This is somewhat mediocre, but as was noted earlier, accuracy is not necessarily the most relevant model evaluation metric in this circumstance.  \n",
    "\n",
    "The mean log loss is also somewhat mediocre at 0.66.  \n",
    "\n",
    "The area under the ROC curve of 0.64 is better than random (0.5 is as good as random), but perhaps we can do better with another modeling algorithm.  \n",
    "\n",
    "In an ideal scenario, we would now try to improve this model by tuning the hyperparameters.  In order to constrain the scope of this project, we will forego this step.  For now, let's look at how the model does with the test dataset and evaluate a couple other modeling algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.58999999999999997"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy score for the test set is very close to the mean accuracy produced by cross validation on the train set.  That's a good sign, as it indicates that the model generalizes well to new data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_lr = lr_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.88      0.58      0.70       984\n",
      "          1       0.25      0.63      0.36       216\n",
      "\n",
      "avg / total       0.77      0.59      0.64      1200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average precision for this model is 0.76, which is decent.  But when looking closer, we can see that the precision for values of 0 is really good (0.87) and for values of 1 it is pretty bad (0.24).  \n",
    "\n",
    "The recall of 0.58 is somewhat middle of the road.  \n",
    "\n",
    "Let's take a look at the confusion matrix to more easily visualize what's going on here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x209c90dd0f0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAK5cuQKtVgt/f39kZWUBADIzM9G1a1d4eXnhxIkT0Ol0KCoqQm5uLjw8PCqNi5UxETUq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\nJmMiIhlgMiYikgEmYyIiGfh//LC5qtVSitAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x209c90c0630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_cm = confusion_matrix(y_test,y_pred_lr)\n",
    "\n",
    "ax = sns.heatmap(lr_cm,annot=True,cmap='coolwarm',fmt='.0f')\n",
    "ax.set_title('Logistic Regression Confusion Matrix')\n",
    "ax.set_ylabel('True Label')\n",
    "ax.set_xlabel('Predicted Label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "When looking at the confusion matrix, we can see that this model produced lots of true negatives (568), but almost as many false positives (416).  When comparing those numbers with the number of false negatives (82) and true positives (134), it appears that this model may be predicting too many flight cancellations.  If we were not constraining the scope of this project, a next step might be to make adjustments to the weights of the model.  For now though, let's persist this model using python's pickle library in case we want to include it in our final data pipeline function and move on to a different classification algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "lr_model_pickled = pickle.dumps(lr_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will build a model using a random forest classifier.  Once again, we will refrain from additional feature selection and hyperparameter tuning in order to constrain the scope of this project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight='balanced',\n",
       "            criterion='gini', max_depth=None, max_features='auto',\n",
       "            max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "            min_impurity_split=None, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=100, n_jobs=1, oob_score=False, random_state=888,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_model = RandomForestClassifier(n_estimators=100, random_state=888,class_weight='balanced')\n",
    "rfc_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Accuracy: 0.801458072011\n",
      "Mean Log Loss: -0.697041450276\n",
      "Mean Area Under ROC Curve: 0.623389627037\n"
     ]
    }
   ],
   "source": [
    "print_eval_scores(rfc_model, X_train, y_train, kfold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The random forest produced a much higher accuracy than the logistic regression model, but again accuracy is not necessarily the best evaluation metric when the response variable is as unbalanced as ours.  This is clear when looking at the mean log loss and area under the ROC curve, as these metrics are both slightly worse for the random forest than for logisitic regression.  \n",
    "\n",
    "Let's take a look at how this model generalizes to the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80083333333333329"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The RFC model produces an accuracy score of about 80.1% for both the train and test sets, so it does seem to generalize well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_rfc = rfc_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.83      0.95      0.89       984\n",
      "          1       0.35      0.12      0.18       216\n",
      "\n",
      "avg / total       0.75      0.80      0.76      1200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_rfc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When first looking at the overall precision and recall for this model it appears to be performing really well.  But when looking closer, we can see that the precision and recall are excellent for predictions of 0 and poor for predictions of 1.  Lt's take a look at the confusion matrix to see it a little more clearly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x209c927a470>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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byZiITEpdTuB99913KC8vR1JSEjIyMrBixQpotVpERETAx8cHMTExSEtLg4eHBxITE7Fj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\nOI/1CxoaIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x209ca2617f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rfc_cm = confusion_matrix(y_test,y_pred_rfc)\n",
    "\n",
    "ax = sns.heatmap(rfc_cm,annot=True,cmap='coolwarm',fmt='.0f')\n",
    "ax.set_title('Random Forest Classifier Confusion Matrix')\n",
    "ax.set_ylabel('True Label')\n",
    "ax.set_xlabel('Predicted Label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "When looking at the confusion matrix, it is clear that this model is just predicting that nearly all (94% to be precise) of the flights will not be canceled.  So this model has the opposite problem of the logistic regression model.  Canceled flights were being over-predicted in that case and they are being under-predicted in this case.  \n",
    "\n",
    "In order to determine which of these models is more useful, the context of the problem needs to be taken into account.  In this case, is it better to think that a flight will be canceled and have it take off as scheduled or to think that a flight will occur and have it be canceled.  I would prefer the first option (as I think most people would).  This means that predicting too many cancellations is preferable to predicting too many non-canceled flights.  Therefore, the logistic regression model would probably be a beter option than the random forest classifier model.  \n",
    "\n",
    "Again, we would normally tune the hyperparameters for the RFC model (e.g. minimum samples per split, minimum samples per leaf, maximum leaf nodes, etc.) at this time in order to try and get a more useful model, but we will refrain from doing that in order to constrain the scope of the project.  Let's see if we can outperform the logistic regression model using k-nearest neighbors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normally, we would loop through multiple different numbers of neighbors to use when creating a k-NN classifier and determine which value is optimal.  To constrain the scope of this project, we will just use a value of 5 for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_model = KNeighborsClassifier(n_neighbors=5)\n",
    "knn_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Accuracy: 0.814587897879\n",
      "Mean Log Loss: -2.2647147457\n",
      "Mean Area Under ROC Curve: 0.571234260682\n"
     ]
    }
   ],
   "source": [
    "print_eval_scores(knn_model, X_train, y_train, kfold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "When running our k-NN model on the test data set using 10 fold stratified cross validation, we get a mean accuracy score of 81.5%.  This is the highest accuracy we have seen so far.  However, highlighting the earlier warning about relying solely on the accuracy score, we can see that the log loss is terrible and the area under the ROC curve is the worst of the three models as well.  This is probably not the optimal model out of the three, but let's look at some additional metrics around its performance on the test set to see if we can find out more about what this model is doing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80249999999999999"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy is again similar between the train and test sets, so the model does appear to generalize well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_knn = knn_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.83      0.95      0.89       984\n",
      "          1       0.35      0.12      0.18       216\n",
      "\n",
      "avg / total       0.75      0.80      0.76      1200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_rfc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x209cb283160>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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h8PPzg0qlKrdtJmMisijVqYybNm0Kg8EAo9EIrVYLKysrHDlyBB07dgQA+Pv7IzU1FQqF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\nGRMRyQCTMRGRDDAZExHJwP8DJv4TcFyxlGYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x209cb2748d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_cm = confusion_matrix(y_test,y_pred_knn)\n",
    "\n",
    "ax = sns.heatmap(knn_cm,annot=True,cmap='coolwarm',fmt='.0f')\n",
    "ax.set_title('K Nearest Neighbors Confusion Matrix')\n",
    "ax.set_ylabel('True Label')\n",
    "ax.set_xlabel('Predicted Label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "When looking at the precision and recall as well as the confusion matrix, the results look similar to the random forest classifier.  The k-NN model appears to be classifying even more of the flights as non-canceled though, which explains why the performance metrics look similar, but slightly worse.  \n",
    "\n",
    "Again, we would normally attempt to optimize this model by tuning the hyperparameters, but will refrain from doing so for the purposes of this project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Model Selection and Encapsulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Given that false positives are more palatable than false negatives for this problem, we will select the logistic regression model.  Despite having a lower accuracy score than the other two models that were trained, this model seems to suit our needs the best.  \n",
    "\n",
    "Next, we will encapsulate the entire pipeline from data cleaning to modeling predictions in a function.  This will make it easy to evaluate new flights and predict whether or not thye will be canceled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict_cancellations(df):\n",
    "    # Clean data\n",
    "    df1 = clean_data(df)\n",
    "    \n",
    "    # Drop 'Canceled' column if it exists\n",
    "    if 'Canceled' in df1.columns:\n",
    "        df1.drop('Canceled',axis=1,inplace=True)\n",
    "        \n",
    "    # Load pickled logistic regression model\n",
    "    clf = pickle.loads(lr_model_pickled)\n",
    "    \n",
    "    # Generate and return predictions\n",
    "    y_pred = clf.predict(df1)\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can test this function by passing the original dataset and checking to see that a vector of predictions for the Canceled variable is returned.  We will also append these predictions to the original dataframe as a new column and export it to a csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_func = predict_cancellations(df)\n",
    "\n",
    "y_pred_func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Canceled</th>\n",
       "      <th>Month</th>\n",
       "      <th>DepartureTime</th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>SchedElapsedTime</th>\n",
       "      <th>ArrDelay</th>\n",
       "      <th>DepDelay</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>814</td>\n",
       "      <td>UA</td>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>830</td>\n",
       "      <td>DL</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1835</td>\n",
       "      <td>UA</td>\n",
       "      <td>213</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1605</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1730</td>\n",
       "      <td>AA</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>235</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1442</td>\n",
       "      <td>UA</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>413</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Canceled  Month  DepartureTime UniqueCarrier  SchedElapsedTime  ArrDelay  \\\n",
       "0         1     12            814            UA               134         0   \n",
       "1         1     12            830            DL                90         0   \n",
       "2         1      1           1835            UA               213         0   \n",
       "3         1      4           1730            AA                80         0   \n",
       "4         1      7           1442            UA               103         0   \n",
       "\n",
       "   DepDelay  Distance  Predictions  \n",
       "0         0       679            0  \n",
       "1         0       214            0  \n",
       "2         0      1605            1  \n",
       "3         0       235            1  \n",
       "4         0       413            1  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Predictions'] = y_pred_func\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('flight_delay_predictions.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Future Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When analyzing the flight delays dataset, we traversed through the full data science pipeline of understanding the data, preparing the data, bulding models, evaluating and selecting models, and finally preparing the model for deployment.  With most projects, there is usually room for further work when they are \"done.\"  This project is no exception.  With the given time constraints, decisions were made to constrain the scope at certain points in the process.  This leaves lots of room for future improvements to this work.  \n",
    "\n",
    "Expanded feature engineering is one area of possible future work.  For example, the DepartureTime variable could be modified or turned into new features.  Perhaps binning it into hours or other chunks of the day would produce a better model than leaving it in its current form.  \n",
    "\n",
    "Feature selection is another area where further work could be done.  We decided to exclude certain variables while performing exploratory data analysis, but we may have produced more useful models had we not included all of the remaining features.  Algorithms such as Grid Search, LASSO, or Stepwise selection could yield a more optimized feature set.  \n",
    "\n",
    "Model tuning is probably the lowest hanging fruit in terms of additional work that could be done with this dataset.  We chose to use almost the default parameter values for each of our three models just due to the time constraint of completing this project in a couple days after work.  It is highly unlikely that these are the optimized configurations for these models though.  There are so many things that could be tuned.  Different weights could be evaluated.  The depth and breadth of the random forest could be played with.  Additional distance metrics and values of k could be examined for the k-nearest neighbors model.  A lot of time could be spent on this area."
   ]
  }
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