Classification_Delayed_or_Not.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Module\n",
    "Within this module we will predict if the flight is going to be delayed or not and classify them into those two categories\n",
    "\n",
    "We will first work with unsampled data from the previous module [Data-Preprocessing, merging the flight data and weather data](https://github.com/GV-9wj/Flight_Delay_Prediction/blob/master/Datapreprocessing3_Merging_Data.ipynb).\n",
    "\n",
    "After we have seen the classification reports for these modules we will move onto sampled data. We will work with Undersampling and oversampling(SMOTE).\n",
    "\n",
    "#### Objectives:\n",
    "1. To classify the given flight as delayed or not using classification models.\n",
    "2. Run the classification models on unsampled data and print the report for each model.\n",
    "3. Run the classification models on undersampled data and print the report for each model.\n",
    "4. Run the classification models on oversampled data and print the report for each model.\n",
    "\n",
    "#### Classification models used:\n",
    "1. Logistic regression : [LOOKUP](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)\n",
    "2. Random Forest : [LOOKUP](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)\n",
    "3. Extra trees : [LOOKUP](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html)\n",
    "4. Decision Trees : [LOOKUP](https://scikit-learn.org/stable/modules/tree.html)\n",
    "5. Gradient Boosting Classifier : [LOOKUP](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html)\n",
    "6. XGboost Classifier : [LOOKUP](https://xgboost.readthedocs.io/en/latest/python/python_api.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First let us import all the different libraries that we need\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looking at the dataframe\n",
    "Let us look at the dataframe for a little bit of insight into the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\numpy\\lib\\arraysetops.py:569: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  mask |= (ar1 == a)\n"
     ]
    },
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index_x</th>\n",
       "      <th>ArrDel15</th>\n",
       "      <th>ArrDelayMinutes</th>\n",
       "      <th>ArrTime</th>\n",
       "      <th>CRSArrTime</th>\n",
       "      <th>CRSDepTime</th>\n",
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       "      <th>DepDelayMinutes</th>\n",
       "      <th>DepTime</th>\n",
       "      <th>...</th>\n",
       "      <th>pressure</th>\n",
       "      <th>cloudcover</th>\n",
       "      <th>DewPointF</th>\n",
       "      <th>WindGustKmph</th>\n",
       "      <th>tempF</th>\n",
       "      <th>WindChillF</th>\n",
       "      <th>humidity</th>\n",
       "      <th>time</th>\n",
       "      <th>date</th>\n",
       "      <th>airport</th>\n",
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       "      <td>2016-01-01</td>\n",
       "      <td>SEA</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>SEA</td>\n",
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       "      <td>800</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>SEA</td>\n",
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       "   index_x  ArrDel15  ArrDelayMinutes  ArrTime  CRSArrTime  CRSDepTime  \\\n",
       "0        0       0.0              8.0   1610.0        1602         745   \n",
       "1    12608       1.0             33.0   1237.0        1204         820   \n",
       "2    50768       0.0              0.0   1138.0        1143         759   \n",
       "3    51023       0.0             10.0   1401.0        1351         738   \n",
       "4    61167       0.0              0.0   1524.0        1527         745   \n",
       "\n",
       "   DayofMonth  DepDel15  DepDelayMinutes  DepTime  ... pressure  cloudcover  \\\n",
       "0           1       0.0              0.0    741.0  ...     1030           0   \n",
       "1           1       0.0              1.0    821.0  ...     1030           0   \n",
       "2           1       0.0              0.0    754.0  ...     1030           0   \n",
       "3           1       0.0              2.0    740.0  ...     1030           0   \n",
       "4           1       0.0              0.0    742.0  ...     1030           0   \n",
       "\n",
       "  DewPointF  WindGustKmph tempF  WindChillF  humidity  time        date  \\\n",
       "0        23             8    34          29        65   800  2016-01-01   \n",
       "1        23             8    34          29        65   800  2016-01-01   \n",
       "2        23             8    34          29        65   800  2016-01-01   \n",
       "3        23             8    34          29        65   800  2016-01-01   \n",
       "4        23             8    34          29        65   800  2016-01-01   \n",
       "\n",
       "   airport  \n",
       "0      SEA  \n",
       "1      SEA  \n",
       "2      SEA  \n",
       "3      SEA  \n",
       "4      SEA  \n",
       "\n",
       "[5 rows x 35 columns]"
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Readinbg the file \n",
    "df = pd.read_csv(\"Data/Merged_Flight_Weather.csv\", index_col = 0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index_x', 'ArrDel15', 'ArrDelayMinutes', 'ArrTime', 'CRSArrTime',\n",
       "       'CRSDepTime', 'DayofMonth', 'DepDel15', 'DepDelayMinutes', 'DepTime',\n",
       "       'Dest', 'DestAirportID', 'FlightDate', 'Month', 'Origin',\n",
       "       'OriginAirportID', 'Quarter', 'Year', 'DeptTime', 'index_y',\n",
       "       'windspeedKmph', 'winddirDegree', 'weatherCode', 'precipMM',\n",
       "       'visibility', 'pressure', 'cloudcover', 'DewPointF', 'WindGustKmph',\n",
       "       'tempF', 'WindChillF', 'humidity', 'time', 'date', 'airport'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We look at the colums first then the shape\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1851091, 35)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now the shape of the dataframe\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index_x            0\n",
       "ArrDel15           0\n",
       "ArrDelayMinutes    0\n",
       "ArrTime            0\n",
       "CRSArrTime         0\n",
       "CRSDepTime         0\n",
       "DayofMonth         0\n",
       "DepDel15           0\n",
       "DepDelayMinutes    0\n",
       "DepTime            0\n",
       "Dest               0\n",
       "DestAirportID      0\n",
       "FlightDate         0\n",
       "Month              0\n",
       "Origin             0\n",
       "OriginAirportID    0\n",
       "Quarter            0\n",
       "Year               0\n",
       "DeptTime           0\n",
       "index_y            0\n",
       "windspeedKmph      0\n",
       "winddirDegree      0\n",
       "weatherCode        0\n",
       "precipMM           0\n",
       "visibility         0\n",
       "pressure           0\n",
       "cloudcover         0\n",
       "DewPointF          0\n",
       "WindGustKmph       0\n",
       "tempF              0\n",
       "WindChillF         0\n",
       "humidity           0\n",
       "time               0\n",
       "date               0\n",
       "airport            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>65</td>\n",
       "      <td>SEA</td>\n",
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       "      <td>29</td>\n",
       "      <td>65</td>\n",
       "      <td>SEA</td>\n",
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      ],
      "text/plain": [
       "   ArrDel15  CRSArrTime  CRSDepTime  DayofMonth  DepDel15  DepDelayMinutes  \\\n",
       "0       0.0        1602         745           1       0.0              0.0   \n",
       "1       1.0        1204         820           1       0.0              1.0   \n",
       "2       0.0        1143         759           1       0.0              0.0   \n",
       "3       0.0        1351         738           1       0.0              2.0   \n",
       "4       0.0        1527         745           1       0.0              0.0   \n",
       "\n",
       "   DepTime Dest  DestAirportID  Month  ... precipMM  visibility  pressure  \\\n",
       "0    741.0  JFK          12478      1  ...      0.0          10      1030   \n",
       "1    821.0  PHX          14107      1  ...      0.0          10      1030   \n",
       "2    754.0  DEN          11292      1  ...      0.0          10      1030   \n",
       "3    740.0  IAH          12266      1  ...      0.0          10      1030   \n",
       "4    742.0  ATL          10397      1  ...      0.0          10      1030   \n",
       "\n",
       "   cloudcover  DewPointF  WindGustKmph  tempF  WindChillF  humidity  airport  \n",
       "0           0         23             8     34          29        65      SEA  \n",
       "1           0         23             8     34          29        65      SEA  \n",
       "2           0         23             8     34          29        65      SEA  \n",
       "3           0         23             8     34          29        65      SEA  \n",
       "4           0         23             8     34          29        65      SEA  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here we drop all the columns we don't need for our classification\n",
    "df = df.drop(columns=['index_x', 'time', 'date', 'FlightDate', 'index_y', 'DeptTime','ArrDelayMinutes', \n",
    "                      'ArrTime'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ArrDel15', 'CRSArrTime', 'CRSDepTime', 'DayofMonth', 'DepDel15',\n",
       "       'DepDelayMinutes', 'DepTime', 'Dest', 'DestAirportID', 'Month',\n",
       "       'Origin', 'OriginAirportID', 'Quarter', 'Year', 'windspeedKmph',\n",
       "       'winddirDegree', 'weatherCode', 'precipMM', 'visibility', 'pressure',\n",
       "       'cloudcover', 'DewPointF', 'WindGustKmph', 'tempF', 'WindChillF',\n",
       "       'humidity', 'airport'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at the new features\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1851091, 27)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1850998, 27)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label Encoding using cat.codes\n",
    "Label Encoding refers to converting the labels into numeric form so as to convert it into the machine-readable form. Machine learning algorithms can then decide in a better way on how those labels must be operated. It is an important pre-processing step for the structured dataset in supervised learning.\n",
    "Here we are label encoding the `Origin` and the `airport` features, by first converting them to category type and then using `cat.codes`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ArrDel15            float64\n",
       "CRSArrTime            int64\n",
       "CRSDepTime            int64\n",
       "DayofMonth            int64\n",
       "DepDel15            float64\n",
       "DepDelayMinutes     float64\n",
       "DepTime             float64\n",
       "Dest               category\n",
       "DestAirportID         int64\n",
       "Month                 int64\n",
       "Origin             category\n",
       "OriginAirportID       int64\n",
       "Quarter               int64\n",
       "Year                  int64\n",
       "windspeedKmph         int64\n",
       "winddirDegree         int64\n",
       "weatherCode           int64\n",
       "precipMM            float64\n",
       "visibility            int64\n",
       "pressure              int64\n",
       "cloudcover            int64\n",
       "DewPointF             int64\n",
       "WindGustKmph          int64\n",
       "tempF                 int64\n",
       "WindChillF            int64\n",
       "humidity              int64\n",
       "airport            category\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Origin\"] = df[\"Origin\"].astype('category')\n",
    "df[\"airport\"] = df[\"airport\"].astype('category')\n",
    "df[\"Dest\"] = df[\"Dest\"].astype('category')\n",
    "# To see if our features have convered to category\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see those both Origin and airport have been converted into category type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ArrDel15</th>\n",
       "      <th>CRSArrTime</th>\n",
       "      <th>CRSDepTime</th>\n",
       "      <th>DayofMonth</th>\n",
       "      <th>DepDel15</th>\n",
       "      <th>DepDelayMinutes</th>\n",
       "      <th>DepTime</th>\n",
       "      <th>Dest</th>\n",
       "      <th>DestAirportID</th>\n",
       "      <th>Month</th>\n",
       "      <th>...</th>\n",
       "      <th>precipMM</th>\n",
       "      <th>visibility</th>\n",
       "      <th>pressure</th>\n",
       "      <th>cloudcover</th>\n",
       "      <th>DewPointF</th>\n",
       "      <th>WindGustKmph</th>\n",
       "      <th>tempF</th>\n",
       "      <th>WindChillF</th>\n",
       "      <th>humidity</th>\n",
       "      <th>airport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1602</td>\n",
       "      <td>745</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>6</td>\n",
       "      <td>12478</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1030</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>65</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1204</td>\n",
       "      <td>820</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>821.0</td>\n",
       "      <td>12</td>\n",
       "      <td>14107</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1030</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>65</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1143</td>\n",
       "      <td>759</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>754.0</td>\n",
       "      <td>2</td>\n",
       "      <td>11292</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1030</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>65</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1351</td>\n",
       "      <td>738</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>5</td>\n",
       "      <td>12266</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1030</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>65</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1527</td>\n",
       "      <td>745</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>742.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10397</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1030</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>65</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ArrDel15  CRSArrTime  CRSDepTime  DayofMonth  DepDel15  DepDelayMinutes  \\\n",
       "0       0.0        1602         745           1       0.0              0.0   \n",
       "1       1.0        1204         820           1       0.0              1.0   \n",
       "2       0.0        1143         759           1       0.0              0.0   \n",
       "3       0.0        1351         738           1       0.0              2.0   \n",
       "4       0.0        1527         745           1       0.0              0.0   \n",
       "\n",
       "   DepTime  Dest  DestAirportID  Month  ...  precipMM  visibility  pressure  \\\n",
       "0    741.0     6          12478      1  ...       0.0          10      1030   \n",
       "1    821.0    12          14107      1  ...       0.0          10      1030   \n",
       "2    754.0     2          11292      1  ...       0.0          10      1030   \n",
       "3    740.0     5          12266      1  ...       0.0          10      1030   \n",
       "4    742.0     0          10397      1  ...       0.0          10      1030   \n",
       "\n",
       "   cloudcover  DewPointF  WindGustKmph  tempF  WindChillF  humidity  airport  \n",
       "0           0         23             8     34          29        65       13  \n",
       "1           0         23             8     34          29        65       13  \n",
       "2           0         23             8     34          29        65       13  \n",
       "3           0         23             8     34          29        65       13  \n",
       "4           0         23             8     34          29        65       13  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Origin\"] = df[\"Origin\"].cat.codes\n",
    "df[\"airport\"] = df[\"airport\"].cat.codes\n",
    "df[\"Dest\"] = df[\"Dest\"].cat.codes\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ArrDel15           float64\n",
       "CRSArrTime           int64\n",
       "CRSDepTime           int64\n",
       "DayofMonth           int64\n",
       "DepDel15           float64\n",
       "DepDelayMinutes    float64\n",
       "DepTime            float64\n",
       "Dest                  int8\n",
       "DestAirportID        int64\n",
       "Month                int64\n",
       "Origin                int8\n",
       "OriginAirportID      int64\n",
       "Quarter              int64\n",
       "Year                 int64\n",
       "windspeedKmph        int64\n",
       "winddirDegree        int64\n",
       "weatherCode          int64\n",
       "precipMM           float64\n",
       "visibility           int64\n",
       "pressure             int64\n",
       "cloudcover           int64\n",
       "DewPointF            int64\n",
       "WindGustKmph         int64\n",
       "tempF                int64\n",
       "WindChillF           int64\n",
       "humidity             int64\n",
       "airport               int8\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     209299\n",
       "14    166915\n",
       "11    153436\n",
       "0     146719\n",
       "2     144147\n",
       "7     132148\n",
       "3     125900\n",
       "13    115701\n",
       "12    114007\n",
       "4     100167\n",
       "9      99559\n",
       "6      93423\n",
       "5      89551\n",
       "1      83486\n",
       "10     76540\n",
       "Name: Origin, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Origin.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     209299\n",
       "14    166915\n",
       "11    153436\n",
       "0     146719\n",
       "2     144147\n",
       "7     132148\n",
       "3     125900\n",
       "13    115701\n",
       "12    114007\n",
       "4     100167\n",
       "9      99559\n",
       "6      93423\n",
       "5      89551\n",
       "1      83486\n",
       "10     76540\n",
       "Name: airport, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.airport.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ArrDel15', 'CRSArrTime', 'CRSDepTime', 'DayofMonth', 'DepDel15',\n",
       "       'DepDelayMinutes', 'DepTime', 'Dest', 'DestAirportID', 'Month',\n",
       "       'Origin', 'OriginAirportID', 'Quarter', 'Year', 'windspeedKmph',\n",
       "       'winddirDegree', 'weatherCode', 'precipMM', 'visibility', 'pressure',\n",
       "       'cloudcover', 'DewPointF', 'WindGustKmph', 'tempF', 'WindChillF',\n",
       "       'humidity', 'airport'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way for label encoding is to use `sklearn.preprocessing.LabelEncoder` : [LOOKUP](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Feature exploration\n",
    "Since there are no missing values or categorical variables we can continue further and look at the corellation between various features of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,25))\n",
    "cor = df.corr()\n",
    "sns.heatmap(cor, annot=True, cmap='Blues')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ArrDel15</th>\n",
       "      <th>CRSArrTime</th>\n",
       "      <th>CRSDepTime</th>\n",
       "      <th>DayofMonth</th>\n",
       "      <th>DepDel15</th>\n",
       "      <th>DepDelayMinutes</th>\n",
       "      <th>DepTime</th>\n",
       "      <th>Dest</th>\n",
       "      <th>DestAirportID</th>\n",
       "      <th>Month</th>\n",
       "      <th>...</th>\n",
       "      <th>precipMM</th>\n",
       "      <th>visibility</th>\n",
       "      <th>pressure</th>\n",
       "      <th>cloudcover</th>\n",
       "      <th>DewPointF</th>\n",
       "      <th>WindGustKmph</th>\n",
       "      <th>tempF</th>\n",
       "      <th>WindChillF</th>\n",
       "      <th>humidity</th>\n",
       "      <th>airport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ArrDel15</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.110601</td>\n",
       "      <td>0.127240</td>\n",
       "      <td>-0.007595</td>\n",
       "      <td>0.710538</td>\n",
       "      <td>0.546142</td>\n",
       "      <td>0.154322</td>\n",
       "      <td>0.051256</td>\n",
       "      <td>0.048184</td>\n",
       "      <td>-0.023368</td>\n",
       "      <td>...</td>\n",
       "      <td>0.088449</td>\n",
       "      <td>-0.021842</td>\n",
       "      <td>-0.073886</td>\n",
       "      <td>0.037856</td>\n",
       "      <td>0.038140</td>\n",
       "      <td>0.080309</td>\n",
       "      <td>0.035390</td>\n",
       "      <td>0.031992</td>\n",
       "      <td>-0.000720</td>\n",
       "      <td>0.020761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRSArrTime</th>\n",
       "      <td>0.110601</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.487270</td>\n",
       "      <td>-0.001902</td>\n",
       "      <td>0.118568</td>\n",
       "      <td>0.086941</td>\n",
       "      <td>0.513784</td>\n",
       "      <td>0.009244</td>\n",
       "      <td>0.010097</td>\n",
       "      <td>-0.004749</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030528</td>\n",
       "      <td>0.091271</td>\n",
       "      <td>-0.060437</td>\n",
       "      <td>-0.032332</td>\n",
       "      <td>0.006619</td>\n",
       "      <td>0.090938</td>\n",
       "      <td>0.141935</td>\n",
       "      <td>0.132120</td>\n",
       "      <td>-0.204943</td>\n",
       "      <td>-0.024607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRSDepTime</th>\n",
       "      <td>0.127240</td>\n",
       "      <td>0.487270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000354</td>\n",
       "      <td>0.154985</td>\n",
       "      <td>0.104760</td>\n",
       "      <td>0.934008</td>\n",
       "      <td>0.061127</td>\n",
       "      <td>0.062914</td>\n",
       "      <td>-0.001498</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032850</td>\n",
       "      <td>0.105889</td>\n",
       "      <td>-0.077368</td>\n",
       "      <td>-0.057288</td>\n",
       "      <td>0.030099</td>\n",
       "      <td>0.115164</td>\n",
       "      <td>0.146884</td>\n",
       "      <td>0.147577</td>\n",
       "      <td>-0.191055</td>\n",
       "      <td>-0.044655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DayofMonth</th>\n",
       "      <td>-0.007595</td>\n",
       "      <td>-0.001902</td>\n",
       "      <td>0.000354</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007067</td>\n",
       "      <td>-0.006684</td>\n",
       "      <td>-0.000778</td>\n",
       "      <td>0.000443</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.010127</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001259</td>\n",
       "      <td>0.025225</td>\n",
       "      <td>-0.032988</td>\n",
       "      <td>-0.015192</td>\n",
       "      <td>0.018075</td>\n",
       "      <td>0.006418</td>\n",
       "      <td>0.016331</td>\n",
       "      <td>0.015826</td>\n",
       "      <td>-0.001185</td>\n",
       "      <td>-0.000008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepDel15</th>\n",
       "      <td>0.710538</td>\n",
       "      <td>0.118568</td>\n",
       "      <td>0.154985</td>\n",
       "      <td>-0.007067</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.608080</td>\n",
       "      <td>0.183905</td>\n",
       "      <td>0.033684</td>\n",
       "      <td>0.031411</td>\n",
       "      <td>-0.021137</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080523</td>\n",
       "      <td>-0.012637</td>\n",
       "      <td>-0.060215</td>\n",
       "      <td>0.029351</td>\n",
       "      <td>0.036910</td>\n",
       "      <td>0.072551</td>\n",
       "      <td>0.036078</td>\n",
       "      <td>0.033097</td>\n",
       "      <td>-0.005634</td>\n",
       "      <td>0.014527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepDelayMinutes</th>\n",
       "      <td>0.546142</td>\n",
       "      <td>0.086941</td>\n",
       "      <td>0.104760</td>\n",
       "      <td>-0.006684</td>\n",
       "      <td>0.608080</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.127716</td>\n",
       "      <td>0.019076</td>\n",
       "      <td>0.016701</td>\n",
       "      <td>-0.017678</td>\n",
       "      <td>...</td>\n",
       "      <td>0.081938</td>\n",
       "      <td>-0.019312</td>\n",
       "      <td>-0.052188</td>\n",
       "      <td>0.031330</td>\n",
       "      <td>0.038023</td>\n",
       "      <td>0.071245</td>\n",
       "      <td>0.029618</td>\n",
       "      <td>0.027397</td>\n",
       "      <td>0.004276</td>\n",
       "      <td>0.008607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepTime</th>\n",
       "      <td>0.154322</td>\n",
       "      <td>0.513784</td>\n",
       "      <td>0.934008</td>\n",
       "      <td>-0.000778</td>\n",
       "      <td>0.183905</td>\n",
       "      <td>0.127716</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.061829</td>\n",
       "      <td>0.063417</td>\n",
       "      <td>-0.003646</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036953</td>\n",
       "      <td>0.102774</td>\n",
       "      <td>-0.075025</td>\n",
       "      <td>-0.053260</td>\n",
       "      <td>0.031685</td>\n",
       "      <td>0.118263</td>\n",
       "      <td>0.150887</td>\n",
       "      <td>0.149695</td>\n",
       "      <td>-0.193531</td>\n",
       "      <td>-0.045355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dest</th>\n",
       "      <td>0.051256</td>\n",
       "      <td>0.009244</td>\n",
       "      <td>0.061127</td>\n",
       "      <td>0.000443</td>\n",
       "      <td>0.033684</td>\n",
       "      <td>0.019076</td>\n",
       "      <td>0.061829</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993752</td>\n",
       "      <td>0.002829</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008627</td>\n",
       "      <td>0.011786</td>\n",
       "      <td>-0.025933</td>\n",
       "      <td>-0.011573</td>\n",
       "      <td>-0.056142</td>\n",
       "      <td>0.004310</td>\n",
       "      <td>-0.032813</td>\n",
       "      <td>-0.031766</td>\n",
       "      <td>-0.021616</td>\n",
       "      <td>-0.040610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DestAirportID</th>\n",
       "      <td>0.048184</td>\n",
       "      <td>0.010097</td>\n",
       "      <td>0.062914</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.031411</td>\n",
       "      <td>0.016701</td>\n",
       "      <td>0.063417</td>\n",
       "      <td>0.993752</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003311</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010310</td>\n",
       "      <td>0.013525</td>\n",
       "      <td>-0.028700</td>\n",
       "      <td>-0.013609</td>\n",
       "      <td>-0.055773</td>\n",
       "      <td>0.001647</td>\n",
       "      <td>-0.029950</td>\n",
       "      <td>-0.028669</td>\n",
       "      <td>-0.023853</td>\n",
       "      <td>-0.033152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <td>-0.023368</td>\n",
       "      <td>-0.004749</td>\n",
       "      <td>-0.001498</td>\n",
       "      <td>0.010127</td>\n",
       "      <td>-0.021137</td>\n",
       "      <td>-0.017678</td>\n",
       "      <td>-0.003646</td>\n",
       "      <td>0.002829</td>\n",
       "      <td>0.003311</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.040026</td>\n",
       "      <td>0.121191</td>\n",
       "      <td>0.042888</td>\n",
       "      <td>-0.084233</td>\n",
       "      <td>0.122952</td>\n",
       "      <td>-0.090128</td>\n",
       "      <td>0.142461</td>\n",
       "      <td>0.138033</td>\n",
       "      <td>-0.053684</td>\n",
       "      <td>0.002300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Origin</th>\n",
       "      <td>0.020761</td>\n",
       "      <td>-0.024607</td>\n",
       "      <td>-0.044655</td>\n",
       "      <td>-0.000008</td>\n",
       "      <td>0.014527</td>\n",
       "      <td>0.008607</td>\n",
       "      <td>-0.045355</td>\n",
       "      <td>-0.040610</td>\n",
       "      <td>-0.033152</td>\n",
       "      <td>0.002300</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.027725</td>\n",
       "      <td>-0.017840</td>\n",
       "      <td>-0.052336</td>\n",
       "      <td>0.076511</td>\n",
       "      <td>0.018882</td>\n",
       "      <td>-0.039873</td>\n",
       "      <td>-0.031699</td>\n",
       "      <td>-0.028340</td>\n",
       "      <td>0.117704</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OriginAirportID</th>\n",
       "      <td>0.019357</td>\n",
       "      <td>-0.025218</td>\n",
       "      <td>-0.045688</td>\n",
       "      <td>-0.000147</td>\n",
       "      <td>0.012233</td>\n",
       "      <td>0.006823</td>\n",
       "      <td>-0.046768</td>\n",
       "      <td>-0.033287</td>\n",
       "      <td>-0.026588</td>\n",
       "      <td>0.002796</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.028767</td>\n",
       "      <td>-0.019516</td>\n",
       "      <td>-0.057426</td>\n",
       "      <td>0.074003</td>\n",
       "      <td>-0.016751</td>\n",
       "      <td>-0.046741</td>\n",
       "      <td>-0.050462</td>\n",
       "      <td>-0.046612</td>\n",
       "      <td>0.100136</td>\n",
       "      <td>0.993755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quarter</th>\n",
       "      <td>-0.022739</td>\n",
       "      <td>-0.004011</td>\n",
       "      <td>-0.001978</td>\n",
       "      <td>0.010357</td>\n",
       "      <td>-0.020276</td>\n",
       "      <td>-0.015989</td>\n",
       "      <td>-0.003982</td>\n",
       "      <td>0.002776</td>\n",
       "      <td>0.003257</td>\n",
       "      <td>0.970557</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.037419</td>\n",
       "      <td>0.120011</td>\n",
       "      <td>0.037625</td>\n",
       "      <td>-0.086879</td>\n",
       "      <td>0.133693</td>\n",
       "      <td>-0.092858</td>\n",
       "      <td>0.152983</td>\n",
       "      <td>0.148997</td>\n",
       "      <td>-0.054536</td>\n",
       "      <td>0.002292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>0.010234</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.001672</td>\n",
       "      <td>0.000285</td>\n",
       "      <td>0.005314</td>\n",
       "      <td>0.003542</td>\n",
       "      <td>0.003710</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>0.000474</td>\n",
       "      <td>-0.002256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008247</td>\n",
       "      <td>0.058635</td>\n",
       "      <td>-0.043998</td>\n",
       "      <td>-0.091035</td>\n",
       "      <td>-0.017624</td>\n",
       "      <td>0.040659</td>\n",
       "      <td>-0.010239</td>\n",
       "      <td>0.004545</td>\n",
       "      <td>-0.044559</td>\n",
       "      <td>0.000278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windspeedKmph</th>\n",
       "      <td>0.085093</td>\n",
       "      <td>0.160427</td>\n",
       "      <td>0.154845</td>\n",
       "      <td>0.009468</td>\n",
       "      <td>0.080514</td>\n",
       "      <td>0.071739</td>\n",
       "      <td>0.164111</td>\n",
       "      <td>0.008086</td>\n",
       "      <td>0.006310</td>\n",
       "      <td>-0.094982</td>\n",
       "      <td>...</td>\n",
       "      <td>0.130669</td>\n",
       "      <td>0.033562</td>\n",
       "      <td>-0.203910</td>\n",
       "      <td>0.090122</td>\n",
       "      <td>-0.055783</td>\n",
       "      <td>0.910852</td>\n",
       "      <td>-0.097257</td>\n",
       "      <td>-0.127656</td>\n",
       "      <td>0.014278</td>\n",
       "      <td>-0.030547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>winddirDegree</th>\n",
       "      <td>0.019526</td>\n",
       "      <td>0.016975</td>\n",
       "      <td>0.050818</td>\n",
       "      <td>0.001262</td>\n",
       "      <td>0.022374</td>\n",
       "      <td>0.012503</td>\n",
       "      <td>0.047758</td>\n",
       "      <td>0.026701</td>\n",
       "      <td>0.028792</td>\n",
       "      <td>-0.047958</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.061032</td>\n",
       "      <td>0.050650</td>\n",
       "      <td>-0.136059</td>\n",
       "      <td>-0.078563</td>\n",
       "      <td>-0.080907</td>\n",
       "      <td>0.085481</td>\n",
       "      <td>-0.081223</td>\n",
       "      <td>-0.090090</td>\n",
       "      <td>0.006764</td>\n",
       "      <td>0.016481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weatherCode</th>\n",
       "      <td>0.092631</td>\n",
       "      <td>0.015099</td>\n",
       "      <td>0.013540</td>\n",
       "      <td>-0.009417</td>\n",
       "      <td>0.080544</td>\n",
       "      <td>0.079583</td>\n",
       "      <td>0.018802</td>\n",
       "      <td>-0.015497</td>\n",
       "      <td>-0.017940</td>\n",
       "      <td>-0.088521</td>\n",
       "      <td>...</td>\n",
       "      <td>0.546828</td>\n",
       "      <td>-0.374527</td>\n",
       "      <td>-0.145292</td>\n",
       "      <td>0.451483</td>\n",
       "      <td>0.207779</td>\n",
       "      <td>0.186006</td>\n",
       "      <td>-0.014533</td>\n",
       "      <td>-0.013000</td>\n",
       "      <td>0.287993</td>\n",
       "      <td>-0.027161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipMM</th>\n",
       "      <td>0.088449</td>\n",
       "      <td>0.030528</td>\n",
       "      <td>0.032850</td>\n",
       "      <td>-0.001259</td>\n",
       "      <td>0.080523</td>\n",
       "      <td>0.081938</td>\n",
       "      <td>0.036953</td>\n",
       "      <td>-0.008627</td>\n",
       "      <td>-0.010310</td>\n",
       "      <td>-0.040026</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.271029</td>\n",
       "      <td>-0.141882</td>\n",
       "      <td>0.264055</td>\n",
       "      <td>0.142049</td>\n",
       "      <td>0.204750</td>\n",
       "      <td>-0.004917</td>\n",
       "      <td>-0.006112</td>\n",
       "      <td>0.201239</td>\n",
       "      <td>-0.027725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visibility</th>\n",
       "      <td>-0.021842</td>\n",
       "      <td>0.091271</td>\n",
       "      <td>0.105889</td>\n",
       "      <td>0.025225</td>\n",
       "      <td>-0.012637</td>\n",
       "      <td>-0.019312</td>\n",
       "      <td>0.102774</td>\n",
       "      <td>0.011786</td>\n",
       "      <td>0.013525</td>\n",
       "      <td>0.121191</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.271029</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.022496</td>\n",
       "      <td>-0.360342</td>\n",
       "      <td>-0.077107</td>\n",
       "      <td>-0.014927</td>\n",
       "      <td>0.150165</td>\n",
       "      <td>0.143353</td>\n",
       "      <td>-0.357275</td>\n",
       "      <td>-0.017840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pressure</th>\n",
       "      <td>-0.073886</td>\n",
       "      <td>-0.060437</td>\n",
       "      <td>-0.077368</td>\n",
       "      <td>-0.032988</td>\n",
       "      <td>-0.060215</td>\n",
       "      <td>-0.052188</td>\n",
       "      <td>-0.075025</td>\n",
       "      <td>-0.025933</td>\n",
       "      <td>-0.028700</td>\n",
       "      <td>0.042888</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.141882</td>\n",
       "      <td>0.022496</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.042421</td>\n",
       "      <td>-0.197691</td>\n",
       "      <td>-0.212974</td>\n",
       "      <td>-0.346612</td>\n",
       "      <td>-0.344379</td>\n",
       "      <td>0.134036</td>\n",
       "      <td>-0.052336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cloudcover</th>\n",
       "      <td>0.037856</td>\n",
       "      <td>-0.032332</td>\n",
       "      <td>-0.057288</td>\n",
       "      <td>-0.015192</td>\n",
       "      <td>0.029351</td>\n",
       "      <td>0.031330</td>\n",
       "      <td>-0.053260</td>\n",
       "      <td>-0.011573</td>\n",
       "      <td>-0.013609</td>\n",
       "      <td>-0.084233</td>\n",
       "      <td>...</td>\n",
       "      <td>0.264055</td>\n",
       "      <td>-0.360342</td>\n",
       "      <td>-0.042421</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.136903</td>\n",
       "      <td>0.128676</td>\n",
       "      <td>-0.246735</td>\n",
       "      <td>-0.243158</td>\n",
       "      <td>0.529071</td>\n",
       "      <td>0.076511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DewPointF</th>\n",
       "      <td>0.038140</td>\n",
       "      <td>0.006619</td>\n",
       "      <td>0.030099</td>\n",
       "      <td>0.018075</td>\n",
       "      <td>0.036910</td>\n",
       "      <td>0.038023</td>\n",
       "      <td>0.031685</td>\n",
       "      <td>-0.056142</td>\n",
       "      <td>-0.055773</td>\n",
       "      <td>0.122952</td>\n",
       "      <td>...</td>\n",
       "      <td>0.142049</td>\n",
       "      <td>-0.077107</td>\n",
       "      <td>-0.197691</td>\n",
       "      <td>0.136903</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.069259</td>\n",
       "      <td>0.649184</td>\n",
       "      <td>0.659521</td>\n",
       "      <td>0.352984</td>\n",
       "      <td>0.018882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindGustKmph</th>\n",
       "      <td>0.080309</td>\n",
       "      <td>0.090938</td>\n",
       "      <td>0.115164</td>\n",
       "      <td>0.006418</td>\n",
       "      <td>0.072551</td>\n",
       "      <td>0.071245</td>\n",
       "      <td>0.118263</td>\n",
       "      <td>0.004310</td>\n",
       "      <td>0.001647</td>\n",
       "      <td>-0.090128</td>\n",
       "      <td>...</td>\n",
       "      <td>0.204750</td>\n",
       "      <td>-0.014927</td>\n",
       "      <td>-0.212974</td>\n",
       "      <td>0.128676</td>\n",
       "      <td>-0.069259</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.144230</td>\n",
       "      <td>-0.170943</td>\n",
       "      <td>0.070899</td>\n",
       "      <td>-0.039873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tempF</th>\n",
       "      <td>0.035390</td>\n",
       "      <td>0.141935</td>\n",
       "      <td>0.146884</td>\n",
       "      <td>0.016331</td>\n",
       "      <td>0.036078</td>\n",
       "      <td>0.029618</td>\n",
       "      <td>0.150887</td>\n",
       "      <td>-0.032813</td>\n",
       "      <td>-0.029950</td>\n",
       "      <td>0.142461</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004917</td>\n",
       "      <td>0.150165</td>\n",
       "      <td>-0.346612</td>\n",
       "      <td>-0.246735</td>\n",
       "      <td>0.649184</td>\n",
       "      <td>-0.144230</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993143</td>\n",
       "      <td>-0.426260</td>\n",
       "      <td>-0.031699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindChillF</th>\n",
       "      <td>0.031992</td>\n",
       "      <td>0.132120</td>\n",
       "      <td>0.147577</td>\n",
       "      <td>0.015826</td>\n",
       "      <td>0.033097</td>\n",
       "      <td>0.027397</td>\n",
       "      <td>0.149695</td>\n",
       "      <td>-0.031766</td>\n",
       "      <td>-0.028669</td>\n",
       "      <td>0.138033</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006112</td>\n",
       "      <td>0.143353</td>\n",
       "      <td>-0.344379</td>\n",
       "      <td>-0.243158</td>\n",
       "      <td>0.659521</td>\n",
       "      <td>-0.170943</td>\n",
       "      <td>0.993143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.413693</td>\n",
       "      <td>-0.028340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>humidity</th>\n",
       "      <td>-0.000720</td>\n",
       "      <td>-0.204943</td>\n",
       "      <td>-0.191055</td>\n",
       "      <td>-0.001185</td>\n",
       "      <td>-0.005634</td>\n",
       "      <td>0.004276</td>\n",
       "      <td>-0.193531</td>\n",
       "      <td>-0.021616</td>\n",
       "      <td>-0.023853</td>\n",
       "      <td>-0.053684</td>\n",
       "      <td>...</td>\n",
       "      <td>0.201239</td>\n",
       "      <td>-0.357275</td>\n",
       "      <td>0.134036</td>\n",
       "      <td>0.529071</td>\n",
       "      <td>0.352984</td>\n",
       "      <td>0.070899</td>\n",
       "      <td>-0.426260</td>\n",
       "      <td>-0.413693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.117704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>airport</th>\n",
       "      <td>0.020761</td>\n",
       "      <td>-0.024607</td>\n",
       "      <td>-0.044655</td>\n",
       "      <td>-0.000008</td>\n",
       "      <td>0.014527</td>\n",
       "      <td>0.008607</td>\n",
       "      <td>-0.045355</td>\n",
       "      <td>-0.040610</td>\n",
       "      <td>-0.033152</td>\n",
       "      <td>0.002300</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.027725</td>\n",
       "      <td>-0.017840</td>\n",
       "      <td>-0.052336</td>\n",
       "      <td>0.076511</td>\n",
       "      <td>0.018882</td>\n",
       "      <td>-0.039873</td>\n",
       "      <td>-0.031699</td>\n",
       "      <td>-0.028340</td>\n",
       "      <td>0.117704</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ArrDel15  CRSArrTime  CRSDepTime  DayofMonth  DepDel15  \\\n",
       "ArrDel15         1.000000    0.110601    0.127240   -0.007595  0.710538   \n",
       "CRSArrTime       0.110601    1.000000    0.487270   -0.001902  0.118568   \n",
       "CRSDepTime       0.127240    0.487270    1.000000    0.000354  0.154985   \n",
       "DayofMonth      -0.007595   -0.001902    0.000354    1.000000 -0.007067   \n",
       "DepDel15         0.710538    0.118568    0.154985   -0.007067  1.000000   \n",
       "DepDelayMinutes  0.546142    0.086941    0.104760   -0.006684  0.608080   \n",
       "DepTime          0.154322    0.513784    0.934008   -0.000778  0.183905   \n",
       "Dest             0.051256    0.009244    0.061127    0.000443  0.033684   \n",
       "DestAirportID    0.048184    0.010097    0.062914    0.000318  0.031411   \n",
       "Month           -0.023368   -0.004749   -0.001498    0.010127 -0.021137   \n",
       "Origin           0.020761   -0.024607   -0.044655   -0.000008  0.014527   \n",
       "OriginAirportID  0.019357   -0.025218   -0.045688   -0.000147  0.012233   \n",
       "Quarter         -0.022739   -0.004011   -0.001978    0.010357 -0.020276   \n",
       "Year             0.010234    0.000133    0.001672    0.000285  0.005314   \n",
       "windspeedKmph    0.085093    0.160427    0.154845    0.009468  0.080514   \n",
       "winddirDegree    0.019526    0.016975    0.050818    0.001262  0.022374   \n",
       "weatherCode      0.092631    0.015099    0.013540   -0.009417  0.080544   \n",
       "precipMM         0.088449    0.030528    0.032850   -0.001259  0.080523   \n",
       "visibility      -0.021842    0.091271    0.105889    0.025225 -0.012637   \n",
       "pressure        -0.073886   -0.060437   -0.077368   -0.032988 -0.060215   \n",
       "cloudcover       0.037856   -0.032332   -0.057288   -0.015192  0.029351   \n",
       "DewPointF        0.038140    0.006619    0.030099    0.018075  0.036910   \n",
       "WindGustKmph     0.080309    0.090938    0.115164    0.006418  0.072551   \n",
       "tempF            0.035390    0.141935    0.146884    0.016331  0.036078   \n",
       "WindChillF       0.031992    0.132120    0.147577    0.015826  0.033097   \n",
       "humidity        -0.000720   -0.204943   -0.191055   -0.001185 -0.005634   \n",
       "airport          0.020761   -0.024607   -0.044655   -0.000008  0.014527   \n",
       "\n",
       "                 DepDelayMinutes   DepTime      Dest  DestAirportID     Month  \\\n",
       "ArrDel15                0.546142  0.154322  0.051256       0.048184 -0.023368   \n",
       "CRSArrTime              0.086941  0.513784  0.009244       0.010097 -0.004749   \n",
       "CRSDepTime              0.104760  0.934008  0.061127       0.062914 -0.001498   \n",
       "DayofMonth             -0.006684 -0.000778  0.000443       0.000318  0.010127   \n",
       "DepDel15                0.608080  0.183905  0.033684       0.031411 -0.021137   \n",
       "DepDelayMinutes         1.000000  0.127716  0.019076       0.016701 -0.017678   \n",
       "DepTime                 0.127716  1.000000  0.061829       0.063417 -0.003646   \n",
       "Dest                    0.019076  0.061829  1.000000       0.993752  0.002829   \n",
       "DestAirportID           0.016701  0.063417  0.993752       1.000000  0.003311   \n",
       "Month                  -0.017678 -0.003646  0.002829       0.003311  1.000000   \n",
       "Origin                  0.008607 -0.045355 -0.040610      -0.033152  0.002300   \n",
       "OriginAirportID         0.006823 -0.046768 -0.033287      -0.026588  0.002796   \n",
       "Quarter                -0.015989 -0.003982  0.002776       0.003257  0.970557   \n",
       "Year                    0.003542  0.003710  0.000295       0.000474 -0.002256   \n",
       "windspeedKmph           0.071739  0.164111  0.008086       0.006310 -0.094982   \n",
       "winddirDegree           0.012503  0.047758  0.026701       0.028792 -0.047958   \n",
       "weatherCode             0.079583  0.018802 -0.015497      -0.017940 -0.088521   \n",
       "precipMM                0.081938  0.036953 -0.008627      -0.010310 -0.040026   \n",
       "visibility             -0.019312  0.102774  0.011786       0.013525  0.121191   \n",
       "pressure               -0.052188 -0.075025 -0.025933      -0.028700  0.042888   \n",
       "cloudcover              0.031330 -0.053260 -0.011573      -0.013609 -0.084233   \n",
       "DewPointF               0.038023  0.031685 -0.056142      -0.055773  0.122952   \n",
       "WindGustKmph            0.071245  0.118263  0.004310       0.001647 -0.090128   \n",
       "tempF                   0.029618  0.150887 -0.032813      -0.029950  0.142461   \n",
       "WindChillF              0.027397  0.149695 -0.031766      -0.028669  0.138033   \n",
       "humidity                0.004276 -0.193531 -0.021616      -0.023853 -0.053684   \n",
       "airport                 0.008607 -0.045355 -0.040610      -0.033152  0.002300   \n",
       "\n",
       "                 ...  precipMM  visibility  pressure  cloudcover  DewPointF  \\\n",
       "ArrDel15         ...  0.088449   -0.021842 -0.073886    0.037856   0.038140   \n",
       "CRSArrTime       ...  0.030528    0.091271 -0.060437   -0.032332   0.006619   \n",
       "CRSDepTime       ...  0.032850    0.105889 -0.077368   -0.057288   0.030099   \n",
       "DayofMonth       ... -0.001259    0.025225 -0.032988   -0.015192   0.018075   \n",
       "DepDel15         ...  0.080523   -0.012637 -0.060215    0.029351   0.036910   \n",
       "DepDelayMinutes  ...  0.081938   -0.019312 -0.052188    0.031330   0.038023   \n",
       "DepTime          ...  0.036953    0.102774 -0.075025   -0.053260   0.031685   \n",
       "Dest             ... -0.008627    0.011786 -0.025933   -0.011573  -0.056142   \n",
       "DestAirportID    ... -0.010310    0.013525 -0.028700   -0.013609  -0.055773   \n",
       "Month            ... -0.040026    0.121191  0.042888   -0.084233   0.122952   \n",
       "Origin           ... -0.027725   -0.017840 -0.052336    0.076511   0.018882   \n",
       "OriginAirportID  ... -0.028767   -0.019516 -0.057426    0.074003  -0.016751   \n",
       "Quarter          ... -0.037419    0.120011  0.037625   -0.086879   0.133693   \n",
       "Year             ...  0.008247    0.058635 -0.043998   -0.091035  -0.017624   \n",
       "windspeedKmph    ...  0.130669    0.033562 -0.203910    0.090122  -0.055783   \n",
       "winddirDegree    ... -0.061032    0.050650 -0.136059   -0.078563  -0.080907   \n",
       "weatherCode      ...  0.546828   -0.374527 -0.145292    0.451483   0.207779   \n",
       "precipMM         ...  1.000000   -0.271029 -0.141882    0.264055   0.142049   \n",
       "visibility       ... -0.271029    1.000000  0.022496   -0.360342  -0.077107   \n",
       "pressure         ... -0.141882    0.022496  1.000000   -0.042421  -0.197691   \n",
       "cloudcover       ...  0.264055   -0.360342 -0.042421    1.000000   0.136903   \n",
       "DewPointF        ...  0.142049   -0.077107 -0.197691    0.136903   1.000000   \n",
       "WindGustKmph     ...  0.204750   -0.014927 -0.212974    0.128676  -0.069259   \n",
       "tempF            ... -0.004917    0.150165 -0.346612   -0.246735   0.649184   \n",
       "WindChillF       ... -0.006112    0.143353 -0.344379   -0.243158   0.659521   \n",
       "humidity         ...  0.201239   -0.357275  0.134036    0.529071   0.352984   \n",
       "airport          ... -0.027725   -0.017840 -0.052336    0.076511   0.018882   \n",
       "\n",
       "                 WindGustKmph     tempF  WindChillF  humidity   airport  \n",
       "ArrDel15             0.080309  0.035390    0.031992 -0.000720  0.020761  \n",
       "CRSArrTime           0.090938  0.141935    0.132120 -0.204943 -0.024607  \n",
       "CRSDepTime           0.115164  0.146884    0.147577 -0.191055 -0.044655  \n",
       "DayofMonth           0.006418  0.016331    0.015826 -0.001185 -0.000008  \n",
       "DepDel15             0.072551  0.036078    0.033097 -0.005634  0.014527  \n",
       "DepDelayMinutes      0.071245  0.029618    0.027397  0.004276  0.008607  \n",
       "DepTime              0.118263  0.150887    0.149695 -0.193531 -0.045355  \n",
       "Dest                 0.004310 -0.032813   -0.031766 -0.021616 -0.040610  \n",
       "DestAirportID        0.001647 -0.029950   -0.028669 -0.023853 -0.033152  \n",
       "Month               -0.090128  0.142461    0.138033 -0.053684  0.002300  \n",
       "Origin              -0.039873 -0.031699   -0.028340  0.117704  1.000000  \n",
       "OriginAirportID     -0.046741 -0.050462   -0.046612  0.100136  0.993755  \n",
       "Quarter             -0.092858  0.152983    0.148997 -0.054536  0.002292  \n",
       "Year                 0.040659 -0.010239    0.004545 -0.044559  0.000278  \n",
       "windspeedKmph        0.910852 -0.097257   -0.127656  0.014278 -0.030547  \n",
       "winddirDegree        0.085481 -0.081223   -0.090090  0.006764  0.016481  \n",
       "weatherCode          0.186006 -0.014533   -0.013000  0.287993 -0.027161  \n",
       "precipMM             0.204750 -0.004917   -0.006112  0.201239 -0.027725  \n",
       "visibility          -0.014927  0.150165    0.143353 -0.357275 -0.017840  \n",
       "pressure            -0.212974 -0.346612   -0.344379  0.134036 -0.052336  \n",
       "cloudcover           0.128676 -0.246735   -0.243158  0.529071  0.076511  \n",
       "DewPointF           -0.069259  0.649184    0.659521  0.352984  0.018882  \n",
       "WindGustKmph         1.000000 -0.144230   -0.170943  0.070899 -0.039873  \n",
       "tempF               -0.144230  1.000000    0.993143 -0.426260 -0.031699  \n",
       "WindChillF          -0.170943  0.993143    1.000000 -0.413693 -0.028340  \n",
       "humidity             0.070899 -0.426260   -0.413693  1.000000  0.117704  \n",
       "airport             -0.039873 -0.031699   -0.028340  0.117704  1.000000  \n",
       "\n",
       "[27 rows x 27 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Un-Sampled Data\n",
    "We will work the above classifier models using unsampled data, i.e data that has not be altered and predict if the flight is on time or delayed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this dataframe the variable we need to predict is '`ArrDel15` and therefore we need to divide it into two variables:\n",
    "* `X` - the input variable which helps us predict, which is all features except for `Arrdel15` and `ArrDelayMinutes`\n",
    "* `y` - the output variable or the predictor variable, which is basically the column `ArrDel15`\n",
    "\n",
    "We must then split them into train and test datasets using an inbuilt function from scikit learn called train_test_split : [LOOKUP](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['ArrDel15']\n",
    "X = df.drop(columns = ['ArrDel15'])\n",
    "# Spplitting the datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1295698, 26), (555300, 26), (1295698,), (555300,))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuracy measurement\n",
    "There are also a few metrics that we use for checking the accuracy of our model. Some of them are\n",
    "1. model.score()\n",
    "2. A classification report that gives us the precision and the recall and also the F1 score, where:\n",
    "    1. Precision is the fraction of how many of the items that are selected are relevant.\n",
    "    2. Recall is the fraction of how many relevant items are selected.\n",
    "    3. F1 score which is the harmonic mean between the Precision and the Recall\n",
    "3. A confusion matrix which gives us a matrix of the true negatives($C_{0, 0}$), false negatives($C_{1, 0}$), true positives($C_{0, 1}$) false positives($C_{1, 1}$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### METHOD\n",
    "Nearly all our classifiers follow the following method which is\n",
    "1. Call the classifier\n",
    "2. Fit it with out train data\n",
    "3. Run the accuracy metrics for our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression\n",
    "Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (a form of binary regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "logistic_model = LogisticRegression(max_iter=500000)\n",
    "logistic_classifier = logistic_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression Train Accuracy : 91.61895750398627 %\n",
      "\n",
      "Logistic regression Test Accuracy : 91.58040698721412 %\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.98      0.95    438828\n",
      "         1.0       0.89      0.68      0.77    116472\n",
      "\n",
      "    accuracy                           0.92    555300\n",
      "   macro avg       0.91      0.83      0.86    555300\n",
      "weighted avg       0.91      0.92      0.91    555300\n",
      "\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      " Confusion Matrix :- \n",
      "[[429014   9814]\n",
      " [ 36940  79532]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_logistic = logistic_model.predict(X_test)\n",
    "print(\"Logistic regression Train Accuracy : {} %\".format(logistic_classifier.score(X_train, \n",
    "                                                                                   y_train)*100))\n",
    "print(\"\\nLogistic regression Test Accuracy : {} %\".format(sklearn.metrics.accuracy_score(y_test, \n",
    "                                                                                 y_pred_logistic)*100))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\nClassification Report : \\n {}\".format(sklearn.metrics.classification_report(y_test, \n",
    "                                                                                     y_pred_logistic)))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\n Confusion Matrix :- \\n{}\".format(sklearn.metrics.confusion_matrix(y_test, \n",
    "                                                                            y_pred_logistic)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest\n",
    "Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes or mean prediction of the individual trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest_model = RandomForestClassifier()\n",
    "random_forest_classifier = random_forest_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Clasifier Train Accuracy : 99.99629543304073 %\n",
      "\n",
      "Random Forest Clasifier Test Accuracy : 91.81811633351342 %\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.98      0.95    438828\n",
      "         1.0       0.89      0.70      0.78    116472\n",
      "\n",
      "    accuracy                           0.92    555300\n",
      "   macro avg       0.91      0.84      0.87    555300\n",
      "weighted avg       0.92      0.92      0.91    555300\n",
      "\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      " Confusion Matrix :- \n",
      "[[428711  10117]\n",
      " [ 35317  81155]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_randomforest = random_forest_model.predict(X_test)\n",
    "print(\"Random Forest Clasifier Train Accuracy : {} %\".format(random_forest_classifier.score(X_train,\n",
    "                                                                                            y_train)*100))\n",
    "print(\"\\nRandom Forest Clasifier Test Accuracy : {} %\".format(sklearn.metrics.accuracy_score(y_test, \n",
    "                                                                                     y_pred_randomforest)*100))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\nClassification Report : \\n {}\".format(sklearn.metrics.classification_report(y_test, \n",
    "                                                                                     y_pred_randomforest)))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\n Confusion Matrix :- \\n{}\".format(sklearn.metrics.confusion_matrix(y_test, \n",
    "                                                                            y_pred_randomforest)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extra trees Classifier\n",
    "Extremely Randomized Trees Classifier(Extra Trees Classifier) is a type of ensemble learning technique which aggregates the results of multiple de-correlated decision trees collected in a “forest” to output it's classification result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "extra_trees_model = ExtraTreesClassifier()\n",
    "extra_trees_classifier = extra_trees_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extra trees Clasifier Train Accuracy : 99.99984564304336 %\n",
      "\n",
      "Extra trees  Clasifier Test Accuracy : 91.1955699621826 %\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.96      0.95    438828\n",
      "         1.0       0.83      0.74      0.78    116472\n",
      "\n",
      "    accuracy                           0.91    555300\n",
      "   macro avg       0.88      0.85      0.86    555300\n",
      "weighted avg       0.91      0.91      0.91    555300\n",
      "\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      " Confusion Matrix :- \n",
      "[[420716  18112]\n",
      " [ 30779  85693]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_extratrees = extra_trees_model.predict(X_test)\n",
    "print(\"Extra trees Clasifier Train Accuracy : {} %\".format(extra_trees_classifier.score(X_train,\n",
    "                                                                                        y_train)*100))\n",
    "print(\"\\nExtra trees  Clasifier Test Accuracy : {} %\".format(sklearn.metrics.accuracy_score(y_test,\n",
    "                                                                                    y_pred_extratrees)*100))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\nClassification Report : \\n {}\".format(sklearn.metrics.classification_report(y_test, \n",
    "                                                                                     y_pred_extratrees)))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\n Confusion Matrix :- \\n{}\".format(sklearn.metrics.confusion_matrix(y_test, \n",
    "                                                                            y_pred_extratrees)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision trees Classifier\n",
    "The decision tree classifier creates the classification model by building a decision tree. Each node in the tree specifies a test on an attribute, each branch descending from that node corresponds to one of the possible values for that attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "decision_trees_model = DecisionTreeClassifier()\n",
    "decision_trees_classifier = decision_trees_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Trees Clasifier Train Accuracy : 99.99984564304336 %\n",
      "\n",
      "Decision Trees Clasifier Test Accuracy : 86.77093462992977 %\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.91      0.92    438828\n",
      "         1.0       0.68      0.70      0.69    116472\n",
      "\n",
      "    accuracy                           0.87    555300\n",
      "   macro avg       0.80      0.81      0.80    555300\n",
      "weighted avg       0.87      0.87      0.87    555300\n",
      "\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      " Confusion Matrix :- \n",
      "[[399925  38903]\n",
      " [ 34558  81914]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_decisiontrees = decision_trees_model.predict(X_test)\n",
    "print(\"Decision Trees Clasifier Train Accuracy : {} %\".format(decision_trees_classifier.score(X_train,\n",
    "                                                                                           y_train)*100))\n",
    "print(\"\\nDecision Trees Clasifier Test Accuracy : {} %\".format(sklearn.metrics.accuracy_score(y_test,\n",
    "                                                                                      y_pred_decisiontrees)*100))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\nClassification Report : \\n {}\".format(sklearn.metrics.classification_report(y_test, \n",
    "                                                                                     y_pred_decisiontrees)))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\n Confusion Matrix :- \\n{}\".format(sklearn.metrics.confusion_matrix(y_test, \n",
    "                                                                            y_pred_decisiontrees)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Booster Classifier\n",
    "Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "gradient_boost_model = GradientBoostingClassifier(random_state=0)\n",
    "gradient_boost_clf = gradient_boost_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Booster Clasifier Train Accuracy : 91.71134014253322 %\n",
      "\n",
      "Gradient Booster Clasifier Test Accuracy : 91.65514136502792 %\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.98      0.95    438828\n",
      "         1.0       0.89      0.68      0.77    116472\n",
      "\n",
      "    accuracy                           0.92    555300\n",
      "   macro avg       0.91      0.83      0.86    555300\n",
      "weighted avg       0.92      0.92      0.91    555300\n",
      "\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      " Confusion Matrix :- \n",
      "[[429494   9334]\n",
      " [ 37005  79467]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_gradientboost = gradient_boost_model.predict(X_test)\n",
    "print(\"Gradient Booster Clasifier Train Accuracy : {} %\".format(gradient_boost_clf.score(X_train,\n",
    "                                                                                         y_train)*100))\n",
    "print(\"\\nGradient Booster Clasifier Test Accuracy : {} %\".format(sklearn.metrics.accuracy_score(y_test,\n",
    "                                                                                        y_pred_gradientboost)*100))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\nClassification Report : \\n {}\".format(sklearn.metrics.classification_report(y_test, \n",
    "                                                                                     y_pred_gradientboost)))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\n Confusion Matrix :- \\n{}\".format(sklearn.metrics.confusion_matrix(y_test, \n",
    "                                                                            y_pred_gradientboost)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost Classifier\n",
    "XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost\n",
    "from xgboost import XGBClassifier\n",
    "xgboost_model = XGBClassifier()\n",
    "xbgoost_classifier = xgboost_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Clasifier Train Accuracy : 92.15496203590652 %\n",
      "\n",
      "XGBoost Trees Clasifier Test Accuracy : 91.91374032054746 %\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.98      0.95    438828\n",
      "         1.0       0.90      0.69      0.78    116472\n",
      "\n",
      "    accuracy                           0.92    555300\n",
      "   macro avg       0.91      0.84      0.87    555300\n",
      "weighted avg       0.92      0.92      0.92    555300\n",
      "\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      " Confusion Matrix :- \n",
      "[[429767   9061]\n",
      " [ 35842  80630]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_xgboost = xgboost_model.predict(X_test)\n",
    "print(\"XGBoost Clasifier Train Accuracy : {} %\".format(xbgoost_classifier.score(X_train, y_train)*100))\n",
    "print(\"\\nXGBoost Trees Clasifier Test Accuracy : {} %\".format(sklearn.metrics.accuracy_score(y_test, y_pred_xgboost)*100))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\nClassification Report : \\n {}\".format(sklearn.metrics.classification_report(y_test, y_pred_xgboost)))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\n Confusion Matrix :- \\n{}\".format(sklearn.metrics.confusion_matrix(y_test, y_pred_xgboost)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sampled Data\n",
    "We will now work out models on oversampled and undersampled data.\n",
    "For the purpose of this project we will be looking at:\n",
    "* SMOTE oversampling [LOOKUP](https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.over_sampling.SMOTE.html)\n",
    "* Random Undersampling [LOOKUP](https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.under_sampling.RandomUnderSampler.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Considering SMOTE (Synthetic Minority Oversampling Technique)\n",
    "SMOTE is an oversampling technique that generates synthetic samples from the minority class. It is used to obtain a synthetically class-balanced or nearly class-balanced training set, which is then used to train the classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Number of values: \n",
      "0.0    1024173\n",
      "1.0     271525\n",
      "Name: ArrDel15, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Looking at the original value counts\n",
    "print(\"Original Number of values: \\n{}\".format(y_train.value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values after SMOTE: \n",
      "1.0    1024173\n",
      "0.0    1024173\n",
      "Name: ArrDel15, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import imblearn.over_sampling\n",
    "over_sampler_SMOTE = imblearn.over_sampling.SMOTE(random_state = 285)\n",
    "X_train_SMOTE, y_train_SMOTE = over_sampler_SMOTE.fit_resample(X_train, y_train)\n",
    "\n",
    "# Now looking at the values for y_train\n",
    "print(\"Values after SMOTE: \\n{}\".format(y_train_SMOTE.value_counts()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression\n",
    "Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (a form of binary regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "logistic_model = LogisticRegression(max_iter=500000)\n",
    "logistic_classifier_SMOTE = logistic_model.fit(X_train_SMOTE, y_train_SMOTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression Train Accuracy : 86.29289192353245 %\n",
      "\n",
      "Logistic regression Test Accuracy : 89.4819016747704 %\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.94      0.93      0.93    438828\n",
      "         1.0       0.74      0.78      0.76    116472\n",
      "\n",
      "    accuracy                           0.89    555300\n",
      "   macro avg       0.84      0.85      0.84    555300\n",
      "weighted avg       0.90      0.89      0.90    555300\n",
      "\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      " Confusion Matrix :- \n",
      "[[406556  32272]\n",
      " [ 26135  90337]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_logistic_SMOTE = logistic_model.predict(X_test)\n",
    "print(\"Logistic regression Train Accuracy : {} %\".format(logistic_classifier_SMOTE.score(X_train_SMOTE,\n",
    "                                                                                         y_train_SMOTE)*100))\n",
    "print(\"\\nLogistic regression Test Accuracy : {} %\".format(sklearn.metrics.accuracy_score(y_test, \n",
    "                                                                                 y_pred_logistic_SMOTE)*100))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\nClassification Report : \\n {}\".format(sklearn.metrics.classification_report(y_test, \n",
    "                                                                                     y_pred_logistic_SMOTE)))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\n Confusion Matrix :- \\n{}\".format(sklearn.metrics.confusion_matrix(y_test, y_pred_logistic_SMOTE)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest\n",
    "Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes or mean prediction of the individual trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest_model = RandomForestClassifier()\n",
    "random_forest_classifier_SMOTE = random_forest_model.fit(X_train_SMOTE,y_train_SMOTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Clasifier Train Accuracy : 99.99917006208912 %\n",
      "\n",
      "Random Forest Clasifier Test Accuracy : 91.45506933189267 %\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.96      0.95    438828\n",
      "         1.0       0.84      0.73      0.78    116472\n",
      "\n",
      "    accuracy                           0.91    555300\n",
      "   macro avg       0.88      0.85      0.86    555300\n",
      "weighted avg       0.91      0.91      0.91    555300\n",
      "\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      " Confusion Matrix :- \n",
      "[[422280  16548]\n",
      " [ 30902  85570]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_randomforest_SMOTE = random_forest_model.predict(X_test)\n",
    "print(\"Random Forest Clasifier Train Accuracy : {} %\".format(random_forest_classifier_SMOTE.score(X_train_SMOTE, \n",
    "                                                                                                  y_train_SMOTE)*100))\n",
    "print(\"\\nRandom Forest Clasifier Test Accuracy : {} %\".format(sklearn.metrics.accuracy_score(y_test, \n",
    "                                                                                     y_pred_randomforest_SMOTE)*100))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\nClassification Report : \\n {}\".format(sklearn.metrics.classification_report(y_test, \n",
    "                                                                                     y_pred_randomforest_SMOTE)))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\n Confusion Matrix :- \\n{}\".format(sklearn.metrics.confusion_matrix(y_test, \n",
    "                                                                            y_pred_randomforest_SMOTE)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extra trees Classifier\n",
    "Extremely Randomized Trees Classifier(Extra Trees Classifier) is a type of ensemble learning technique which aggregates the results of multiple de-correlated decision trees collected in a “forest” to output it's classification result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "extra_trees_model = ExtraTreesClassifier()\n",
    "extra_trees_classifier_SMOTE = extra_trees_model.fit(X_train_SMOTE,y_train_SMOTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extra trees Clasifier Train Accuracy : 99.99990236024577 %\n",
      "\n",
      "Extra trees  Clasifier Test Accuracy : 90.65946335314244 %\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.94      0.95      0.94    438828\n",
      "         1.0       0.79      0.76      0.77    116472\n",
      "\n",
      "    accuracy                           0.91    555300\n",
      "   macro avg       0.86      0.85      0.86    555300\n",
      "weighted avg       0.91      0.91      0.91    555300\n",
      "\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      " Confusion Matrix :- \n",
      "[[414748  24080]\n",
      " [ 27788  88684]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_extratrees_SMOTE = extra_trees_model.predict(X_test)\n",
    "print(\"Extra trees Clasifier Train Accuracy : {} %\".format(extra_trees_classifier_SMOTE.score(X_train_SMOTE,\n",
    "                                                                                              y_train_SMOTE)*100))\n",
    "print(\"\\nExtra trees  Clasifier Test Accuracy : {} %\".format(sklearn.metrics.accuracy_score(y_test, \n",
    "                                                                                     y_pred_extratrees_SMOTE)*100))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\nClassification Report : \\n {}\".format(sklearn.metrics.classification_report(y_test, \n",
    "                                                                                     y_pred_extratrees_SMOTE)))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\n Confusion Matrix :- \\n{}\".format(sklearn.metrics.confusion_matrix(y_test, \n",
    "                                                                            y_pred_extratrees_SMOTE)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision trees Classifier\n",
    "The decision tree classifier creates the classification model by building a decision tree. Each node in the tree specifies a test on an attribute, each branch descending from that node corresponds to one of the possible values for that attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "decision_trees_model = DecisionTreeClassifier()\n",
    "decision_trees_classifier_SMOTE = decision_trees_model.fit(X_train_SMOTE, y_train_SMOTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Trees Clasifier Train Accuracy : 99.99984564304336 %\n",
      "\n",
      "Decision Trees Clasifier Test Accuracy : 86.21033675490726 %\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.90      0.91    438828\n",
      "         1.0       0.66      0.70      0.68    116472\n",
      "\n",
      "    accuracy                           0.86    555300\n",
      "   macro avg       0.79      0.80      0.80    555300\n",
      "weighted avg       0.87      0.86      0.86    555300\n",
      "\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      " Confusion Matrix :- \n",
      "[[396883  41945]\n",
      " [ 34629  81843]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_decisiontrees_SMOTE = decision_trees_model.predict(X_test)\n",
    "print(\"Decision Trees Clasifier Train Accuracy : {} %\".format(decision_trees_classifier_SMOTE.score(X_train,\n",
    "                                                                                                    y_train)*100))\n",
    "print(\"\\nDecision Trees Clasifier Test Accuracy : {} %\".format(sklearn.metrics.accuracy_score(y_test,\n",
    "                                                                                      y_pred_decisiontrees_SMOTE)*100))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\nClassification Report : \\n {}\".format(sklearn.metrics.classification_report(y_test, \n",
    "                                                                                     y_pred_decisiontrees_SMOTE)))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\n Confusion Matrix :- \\n{}\".format(sklearn.metrics.confusion_matrix(y_test, \n",
    "                                                                            y_pred_decisiontrees_SMOTE)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Booster Classifier\n",
    "Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "gradient_boost_model = GradientBoostingClassifier(random_state=0)\n",
    "gradient_boost_clf_SMOTE = gradient_boost_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Booster Clasifier Train Accuracy : 91.71134014253322 %\n",
      "\n",
      "Gradient Booster Clasifier Test Accuracy : 91.65514136502792 %\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.98      0.95    438828\n",
      "         1.0       0.89      0.68      0.77    116472\n",
      "\n",
      "    accuracy                           0.92    555300\n",
      "   macro avg       0.91      0.83      0.86    555300\n",
      "weighted avg       0.92      0.92      0.91    555300\n",
      "\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      " Confusion Matrix :- \n",
      "[[429494   9334]\n",
      " [ 37005  79467]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_gradientboost_SMOTE = gradient_boost_model.predict(X_test)\n",
    "print(\"Gradient Booster Clasifier Train Accuracy : {} %\".format(gradient_boost_clf_SMOTE.score(X_train,\n",
    "                                                                                               y_train)*100))\n",
    "print(\"\\nGradient Booster Clasifier Test Accuracy : {} %\".format(sklearn.metrics.accuracy_score(y_test, \n",
    "                                                                                               y_pred_gradientboost_SMOTE)*100))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\nClassification Report : \\n {}\".format(sklearn.metrics.classification_report(y_test, \n",
    "                                                                                     y_pred_gradientboost_SMOTE)))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\n Confusion Matrix :- \\n{}\".format(sklearn.metrics.confusion_matrix(y_test,\n",
    "                                                                            y_pred_gradientboost_SMOTE)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost Classifier\n",
    "XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "xgboost_model = XGBClassifier()\n",
    "xgboost_classifier_SMOTE = xgboost_model.fit(X_train_SMOTE, y_train_SMOTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Clasifier Train Accuracy : 93.81559560738275 %\n",
      "\n",
      "XGBoost Clasifier Test Accuracy : 91.72447325769855 %\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.97      0.95    438828\n",
      "         1.0       0.88      0.70      0.78    116472\n",
      "\n",
      "    accuracy                           0.92    555300\n",
      "   macro avg       0.90      0.84      0.87    555300\n",
      "weighted avg       0.92      0.92      0.91    555300\n",
      "\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      " Confusion Matrix :- \n",
      "[[427296  11532]\n",
      " [ 34422  82050]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_xgboost_SMOTE = xgboost_model.predict(X_test)\n",
    "print(\"XGBoost Clasifier Train Accuracy : {} %\".format(xgboost_classifier_SMOTE.score(X_train_SMOTE, \n",
    "                                                                                           y_train_SMOTE)*100))\n",
    "print(\"\\nXGBoost Clasifier Test Accuracy : {} %\".format(sklearn.metrics.accuracy_score(y_test, \n",
    "                                                                                              y_pred_xgboost_SMOTE)*100))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\nClassification Report : \\n {}\".format(sklearn.metrics.classification_report(y_test, \n",
    "                                                                                     y_pred_xgboost_SMOTE)))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\n Confusion Matrix :- \\n{}\".format(sklearn.metrics.confusion_matrix(y_test, \n",
    "                                                                            y_pred_xgboost_SMOTE)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Considering Random Undersampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Number of values: \n",
      "0.0    1024173\n",
      "1.0     271525\n",
      "Name: ArrDel15, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Looking at the original value counts\n",
    "print(\"Original Number of values: \\n{}\".format(y_train.value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values after Random Under Sampling: \n",
      "1.0    271525\n",
      "0.0    271525\n",
      "Name: ArrDel15, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "random_under_sampler = RandomUnderSampler(random_state = 285)\n",
    "X_train_RUS, y_train_RUS = random_under_sampler.fit_resample(X_train,y_train)\n",
    "print(\"Values after Random Under Sampling: \\n{}\".format(y_train_RUS.value_counts()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression\n",
    "Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (a form of binary regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "logistic_model = LogisticRegression(max_iter=500000)\n",
    "logistic_classifier_RUS = logistic_model.fit(X_train_RUS, y_train_RUS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression Train Accuracy : 85.29012061504466 %\n",
      "\n",
      "Logistic regression Test Accuracy : 89.65604177921844 %\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.94      0.93      0.93    438828\n",
      "         1.0       0.74      0.78      0.76    116472\n",
      "\n",
      "    accuracy                           0.90    555300\n",
      "   macro avg       0.84      0.85      0.85    555300\n",
      "weighted avg       0.90      0.90      0.90    555300\n",
      "\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      " Confusion Matrix :- \n",
      "[[407266  31562]\n",
      " [ 25878  90594]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_logistic_RUS = logistic_model.predict(X_test)\n",
    "print(\"Logistic regression Train Accuracy : {} %\".format(logistic_classifier_RUS.score(X_train_RUS, \n",
    "                                                                                   y_train_RUS)*100))\n",
    "print(\"\\nLogistic regression Test Accuracy : {} %\".format(sklearn.metrics.accuracy_score(y_test, \n",
    "                                                                                 y_pred_logistic_RUS)*100))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\nClassification Report : \\n {}\".format(sklearn.metrics.classification_report(y_test, \n",
    "                                                                                     y_pred_logistic_RUS)))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\n Confusion Matrix :- \\n{}\".format(sklearn.metrics.confusion_matrix(y_test, \n",
    "                                                                            y_pred_logistic_RUS)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest\n",
    "Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes or mean prediction of the individual trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest_model = RandomForestClassifier()\n",
    "random_forest_classifier_RUS = random_forest_model.fit(X_train_RUS, y_train_RUS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Clasifier Train Accuracy : 99.99815854893657 %\n",
      "\n",
      "Random Forest Clasifier Test Accuracy : 89.27462632811093 %\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.95      0.92      0.93    438828\n",
      "         1.0       0.72      0.81      0.76    116472\n",
      "\n",
      "    accuracy                           0.89    555300\n",
      "   macro avg       0.83      0.86      0.85    555300\n",
      "weighted avg       0.90      0.89      0.90    555300\n",
      "\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      " Confusion Matrix :- \n",
      "[[401586  37242]\n",
      " [ 22316  94156]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_randomforest_RUS = random_forest_model.predict(X_test)\n",
    "print(\"Random Forest Clasifier Train Accuracy : {} %\".format(random_forest_classifier_RUS.score(X_train_RUS,\n",
    "                                                                                            y_train_RUS)*100))\n",
    "print(\"\\nRandom Forest Clasifier Test Accuracy : {} %\".format(sklearn.metrics.accuracy_score(y_test, \n",
    "                                                                                     y_pred_randomforest_RUS)*100))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\nClassification Report : \\n {}\".format(sklearn.metrics.classification_report(y_test, \n",
    "                                                                                     y_pred_randomforest_RUS)))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\n Confusion Matrix :- \\n{}\".format(sklearn.metrics.confusion_matrix(y_test, \n",
    "                                                                            y_pred_randomforest_RUS)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extra trees Classifier\n",
    "Extremely Randomized Trees Classifier(Extra Trees Classifier) is a type of ensemble learning technique which aggregates the results of multiple de-correlated decision trees collected in a “forest” to output it's classification result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "extra_trees_model = ExtraTreesClassifier()\n",
    "extra_trees_classifier_RUS = extra_trees_model.fit(X_train_RUS, y_train_RUS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extra trees Clasifier Train Accuracy : 99.99963170978732 %\n",
      "\n",
      "Extra trees  Clasifier Test Accuracy : 88.66882766072393 %\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.95      0.91      0.93    438828\n",
      "         1.0       0.70      0.82      0.75    116472\n",
      "\n",
      "    accuracy                           0.89    555300\n",
      "   macro avg       0.82      0.86      0.84    555300\n",
      "weighted avg       0.90      0.89      0.89    555300\n",
      "\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      " Confusion Matrix :- \n",
      "[[397230  41598]\n",
      " [ 21324  95148]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_extratrees_RUS = extra_trees_model.predict(X_test)\n",
    "print(\"Extra trees Clasifier Train Accuracy : {} %\".format(extra_trees_classifier.score(X_train_RUS,\n",
    "                                                                                        y_train_RUS)*100))\n",
    "print(\"\\nExtra trees  Clasifier Test Accuracy : {} %\".format(sklearn.metrics.accuracy_score(y_test,\n",
    "                                                                                    y_pred_extratrees_RUS)*100))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\nClassification Report : \\n {}\".format(sklearn.metrics.classification_report(y_test, \n",
    "                                                                                     y_pred_extratrees_RUS)))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\n Confusion Matrix :- \\n{}\".format(sklearn.metrics.confusion_matrix(y_test, \n",
    "                                                                            y_pred_extratrees_RUS)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision trees Classifier\n",
    "The decision tree classifier creates the classification model by building a decision tree. Each node in the tree specifies a test on an attribute, each branch descending from that node corresponds to one of the possible values for that attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "decision_trees_model = DecisionTreeClassifier()\n",
    "decision_trees_classifier_RUS = decision_trees_model.fit(X_train_RUS, y_train_RUS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Trees Clasifier Train Accuracy : 99.99981585489365 %\n",
      "\n",
      "Decision Trees Clasifier Test Accuracy : 79.10102647217721 %\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.94      0.79      0.86    438828\n",
      "         1.0       0.50      0.80      0.62    116472\n",
      "\n",
      "    accuracy                           0.79    555300\n",
      "   macro avg       0.72      0.79      0.74    555300\n",
      "weighted avg       0.85      0.79      0.81    555300\n",
      "\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      " Confusion Matrix :- \n",
      "[[346001  92827]\n",
      " [ 23225  93247]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_decisiontrees_RUS = decision_trees_model.predict(X_test)\n",
    "print(\"Decision Trees Clasifier Train Accuracy : {} %\".format(decision_trees_classifier_RUS.score(X_train_RUS,\n",
    "                                                                                           y_train_RUS)*100))\n",
    "print(\"\\nDecision Trees Clasifier Test Accuracy : {} %\".format(sklearn.metrics.accuracy_score(y_test,\n",
    "                                                                                      y_pred_decisiontrees_RUS)*100))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\nClassification Report : \\n {}\".format(sklearn.metrics.classification_report(y_test, \n",
    "                                                                                     y_pred_decisiontrees_RUS)))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\n Confusion Matrix :- \\n{}\".format(sklearn.metrics.confusion_matrix(y_test, \n",
    "                                                                            y_pred_decisiontrees_RUS)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Booster Classifier\n",
    "Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "gradient_boost_model = GradientBoostingClassifier(random_state=0)\n",
    "gradient_boost_clf_RUS = gradient_boost_model.fit(X_train_RUS, y_train_RUS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Booster Clasifier Train Accuracy : 83.14556670656478 %\n",
      "\n",
      "Gradient Booster Clasifier Test Accuracy : 89.52980370970647 %\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.94      0.92      0.93    438828\n",
      "         1.0       0.73      0.79      0.76    116472\n",
      "\n",
      "    accuracy                           0.90    555300\n",
      "   macro avg       0.84      0.86      0.85    555300\n",
      "weighted avg       0.90      0.90      0.90    555300\n",
      "\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      " Confusion Matrix :- \n",
      "[[405522  33306]\n",
      " [ 24835  91637]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_gradientboost_RUS = gradient_boost_model.predict(X_test)\n",
    "print(\"Gradient Booster Clasifier Train Accuracy : {} %\".format(gradient_boost_clf.score(X_train_RUS,\n",
    "                                                                                         y_train_RUS)*100))\n",
    "print(\"\\nGradient Booster Clasifier Test Accuracy : {} %\".format(sklearn.metrics.accuracy_score(y_test,\n",
    "                                                                                        y_pred_gradientboost_RUS)*100))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\nClassification Report : \\n {}\".format(sklearn.metrics.classification_report(y_test, \n",
    "                                                                                     y_pred_gradientboost_RUS)))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\n Confusion Matrix :- \\n{}\".format(sklearn.metrics.confusion_matrix(y_test, \n",
    "                                                                            y_pred_gradientboost_RUS)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost Classifier\n",
    "XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "xgboost_model = XGBClassifier()\n",
    "xgboost_classifier_RUS = xgboost_model.fit(X_train_RUS, y_train_RUS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Clasifier Train Accuracy : 86.64082497007642 %\n",
      "\n",
      "XGBoost Clasifier Test Accuracy : 89.59571402845309 %\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.95      0.92      0.93    438828\n",
      "         1.0       0.73      0.80      0.76    116472\n",
      "\n",
      "    accuracy                           0.90    555300\n",
      "   macro avg       0.84      0.86      0.85    555300\n",
      "weighted avg       0.90      0.90      0.90    555300\n",
      "\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      " Confusion Matrix :- \n",
      "[[404364  34464]\n",
      " [ 23311  93161]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_xgboost_RUS = xgboost_model.predict(X_test)\n",
    "print(\"XGBoost Clasifier Train Accuracy : {} %\".format(xgboost_classifier_RUS.score(X_train_RUS, \n",
    "                                                                                           y_train_RUS)*100))\n",
    "print(\"\\nXGBoost Clasifier Test Accuracy : {} %\".format(sklearn.metrics.accuracy_score(y_test, \n",
    "                                                                                              y_pred_xgboost_RUS)*100))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\nClassification Report : \\n {}\".format(sklearn.metrics.classification_report(y_test, \n",
    "                                                                                     y_pred_xgboost_RUS)))\n",
    "print(\"-------------------------------------------------------------------------------------------------\")\n",
    "print(\"\\n Confusion Matrix :- \\n{}\".format(sklearn.metrics.confusion_matrix(y_test, \n",
    "                                                                            y_pred_xgboost_RUS)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results for Class 0 (Arrived on Time) Unsampled Data\n",
    "\n",
    "|MODEL                   |Precision | Recall | f1 Score|\n",
    "|------------------------|----------|--------|---------|\n",
    "| ``Logistic Regression``| 0.92     | 0.98   |0.95     |\n",
    "| ``Random Forest``      | 0.92     | 0.98   |0.95     |\n",
    "| `` Extra Trees``       | 0.93     | 0.96   |0.95     |\n",
    "| ``Decision Trees``     | 0.92     | 0.91   |0.92     |\n",
    "| ``Gradient Booster``   | 0.92     | 0.98   |0.95     |\n",
    "| ``XGBoost``            | 0.92     | 0.98   |0.95     |\n",
    "\n",
    "              \n",
    "## Results for Class 1 (Arrived Late) Unsampled Data\n",
    "\n",
    "|MODEL                   |Precision | Recall | f1 Score|\n",
    "|------------------------|----------|--------|---------|\n",
    "| ``Logistic Regression``| 0.89     | 0.68   |0.77     |\n",
    "| ``Random Forest``      | 0.89     | 0.70   |0.78     |\n",
    "| `` Extra Trees``       | 0.83     | 0.74   |0.78     |\n",
    "| ``Decision Trees``     | 0.68     | 0.70   |0.69     |\n",
    "| ``Gradient Booster``   | 0.90     | 0.68   |0.78     |\n",
    "| ``XGBoost``            | 0.90     | 0.70   |0.78     |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results for Class 0 (Arrived on Time) for Over-sampled Data\n",
    "\n",
    "|MODEL                   |Precision | Recall | f1 Score|\n",
    "|------------------------|----------|--------|---------|\n",
    "| ``Logistic Regression``| 0.94     | 0.93   |0.93     |\n",
    "| ``Random Forest``      | 0.93     | 0.96   |0.95     |\n",
    "| `` Extra Trees``       | 0.94     | 0.95   |0.94     |\n",
    "| ``Decision Trees``     | 0.92     | 0.90   |0.91     |\n",
    "| ``Gradient Booster``   | 0.92     | 0.98   |0.95     |\n",
    "| ``XGBoost``            | 0.92     | 0.98   |0.95     |\n",
    "\n",
    "              \n",
    "## Results for Class 1 (Arrived Late) for Over-sampled Data\n",
    "\n",
    "|MODEL                   |Precision | Recall | f1 Score|\n",
    "|------------------------|----------|--------|---------|\n",
    "| ``Logistic Regression``| 0.74     | 0.78   |0.76     |\n",
    "| ``Random Forest``      | 0.84     | 0.74   |0.78     |\n",
    "| `` Extra Trees``       | 0.79     | 0.76   |0.77     |\n",
    "| ``Decision Trees``     | 0.66     | 0.71   |0.68     |\n",
    "| ``Gradient Booster``   | 0.90     | 0.68   |0.78     |\n",
    "| ``XGBoost``            | 0.90     | 0.70   |0.78     |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results for Class 0 (Arrived on Time) for Under-sampled Data\n",
    "\n",
    "|MODEL                   |Precision | Recall | f1 Score|\n",
    "|------------------------|----------|--------|---------|\n",
    "| ``Logistic Regression``| 0.94     | 0.93   |0.93     |\n",
    "| ``Random Forest``      | 0.95     | 0.91   |0.93     |\n",
    "| `` Extra Trees``       | 0.95     | 0.90   |0.93     |\n",
    "| ``Decision Trees``     | 0.94     | 0.79   |0.86     |\n",
    "| ``Gradient Booster``   | 0.94     | 0.92   |0.93     |\n",
    "| ``XGBoost``            | 0.95     | 0.92   |0.93     |\n",
    "\n",
    "              \n",
    "## Results for Class 1 (Arrived Late) for Under-sampled Data\n",
    "\n",
    "|MODEL                   |Precision | Recall | f1 Score|\n",
    "|------------------------|----------|--------|---------|\n",
    "| ``Logistic Regression``| 0.74     | 0.78   |0.76     |\n",
    "| ``Random Forest``      | 0.72     | 0.81   |0.76     |\n",
    "| `` Extra Trees``       | 0.70     | 0.82   |0.75     |\n",
    "| ``Decision Trees``     | 0.50     | 0.80   |0.62     |\n",
    "| ``Gradient Booster``   | 0.73     | 0.79   |0.76     |\n",
    "| ``XGBoost``            | 0.73     | 0.80   |0.76     |"
   ]
  }
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