RegressionAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "import xgboost as xgb\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>index</th>\n",
       "      <th>Year</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Month</th>\n",
       "      <th>DayofMonth</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Dest</th>\n",
       "      <th>CRSDepTime</th>\n",
       "      <th>DepTime</th>\n",
       "      <th>DepDelayMinutes</th>\n",
       "      <th>...</th>\n",
       "      <th>precipMM</th>\n",
       "      <th>pressure</th>\n",
       "      <th>WindGustKmph</th>\n",
       "      <th>visibility</th>\n",
       "      <th>weatherCode</th>\n",
       "      <th>tempF</th>\n",
       "      <th>WindChillF</th>\n",
       "      <th>winddirDegree</th>\n",
       "      <th>humidity</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>745.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1030</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>113</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>108</td>\n",
       "      <td>65</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>820.0</td>\n",
       "      <td>821.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1030</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>113</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>108</td>\n",
       "      <td>65</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>724.0</td>\n",
       "      <td>759.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1030</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>113</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>108</td>\n",
       "      <td>65</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>738.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1030</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>113</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>108</td>\n",
       "      <td>65</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>800.0</td>\n",
       "      <td>820.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1030</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>113</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>108</td>\n",
       "      <td>65</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700279</th>\n",
       "      <td>1855978</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1025</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>122</td>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>117</td>\n",
       "      <td>85</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700280</th>\n",
       "      <td>1855982</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>1215.0</td>\n",
       "      <td>1244.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1020</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>122</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700281</th>\n",
       "      <td>1855987</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2025.0</td>\n",
       "      <td>2038.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1024</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>122</td>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>123</td>\n",
       "      <td>88</td>\n",
       "      <td>2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700282</th>\n",
       "      <td>1855990</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>2120.0</td>\n",
       "      <td>2306.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>122</td>\n",
       "      <td>37</td>\n",
       "      <td>34</td>\n",
       "      <td>219</td>\n",
       "      <td>90</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700283</th>\n",
       "      <td>1855991</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1023</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>122</td>\n",
       "      <td>41</td>\n",
       "      <td>35</td>\n",
       "      <td>177</td>\n",
       "      <td>89</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>700284 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          index    Year  Quarter  Month  DayofMonth  Origin  Dest  CRSDepTime  \\\n",
       "0             0  2016.0      1.0    1.0         1.0      13     6       745.0   \n",
       "1             1  2016.0      1.0    1.0         1.0      13    12       820.0   \n",
       "2             2  2016.0      1.0    1.0         1.0      13    14       724.0   \n",
       "3             4  2016.0      1.0    1.0         1.0      13     5       738.0   \n",
       "4             6  2016.0      1.0    1.0         1.0      13     4       800.0   \n",
       "...         ...     ...      ...    ...         ...     ...   ...         ...   \n",
       "700279  1855978  2017.0      4.0   12.0        17.0       4    12      2005.0   \n",
       "700280  1855982  2017.0      4.0   12.0        17.0       5    12      1215.0   \n",
       "700281  1855987  2017.0      4.0   12.0        17.0       4     5      2025.0   \n",
       "700282  1855990  2017.0      4.0   12.0        17.0      11     4      2120.0   \n",
       "700283  1855991  2017.0      4.0   12.0        17.0      13     5        17.0   \n",
       "\n",
       "        DepTime  DepDelayMinutes  ...  precipMM  pressure  WindGustKmph  \\\n",
       "0         741.0              0.0  ...       0.0      1030             8   \n",
       "1         821.0              1.0  ...       0.0      1030             8   \n",
       "2         759.0             35.0  ...       0.0      1030             8   \n",
       "3         740.0              2.0  ...       0.0      1030             8   \n",
       "4         820.0             20.0  ...       0.0      1030             8   \n",
       "...         ...              ...  ...       ...       ...           ...   \n",
       "700279   2013.0              8.0  ...       0.0      1025            16   \n",
       "700280   1244.0             29.0  ...       0.0      1020             8   \n",
       "700281   2038.0             13.0  ...       0.0      1024            16   \n",
       "700282   2306.0            106.0  ...       0.0      1016            12   \n",
       "700283    159.0            102.0  ...       0.8      1023            13   \n",
       "\n",
       "        visibility  weatherCode  tempF  WindChillF  winddirDegree  humidity  \\\n",
       "0               10          113     34          29            108        65   \n",
       "1               10          113     34          29            108        65   \n",
       "2               10          113     34          29            108        65   \n",
       "3               10          113     34          29            108        65   \n",
       "4               10          113     34          29            108        65   \n",
       "...            ...          ...    ...         ...            ...       ...   \n",
       "700279          10          122     32          29            117        85   \n",
       "700280          10          122     57          57             77        77   \n",
       "700281          10          122     32          29            123        88   \n",
       "700282           6          122     37          34            219        90   \n",
       "700283          10          122     41          35            177        89   \n",
       "\n",
       "        time  \n",
       "0        800  \n",
       "1        800  \n",
       "2        800  \n",
       "3        800  \n",
       "4        800  \n",
       "...      ...  \n",
       "700279  2000  \n",
       "700280  1300  \n",
       "700281  2100  \n",
       "700282  2300  \n",
       "700283   200  \n",
       "\n",
       "[700284 rows x 25 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pickle.load(open('./JoinedData.pkl','rb'))\n",
    "df.dropna(how='any',inplace=True)\n",
    "df.drop(columns=['FlightDate','date','ArrTime','ArrDel15','CRSArrTime','NearestDepTime'],inplace=True)\n",
    "\n",
    "le = LabelEncoder()\n",
    "df['Origin'] = le.fit_transform(df['Origin'])\n",
    "df['Dest'] = le.fit_transform(df['Dest'])\n",
    "\n",
    "df = df.apply(pd.to_numeric)\n",
    "df = df[df['ArrDelayMinutes'] > 0]\n",
    "\n",
    "df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(10,5))\n",
    "sns.distplot(df['ArrDelayMinutes'], hist=True, kde=True,bins=100, color = 'darkblue',\\\n",
    "             hist_kws={'edgecolor':'black'},\\\n",
    "             kde_kws={'linewidth': 4},label = 'Frequency')\n",
    "ax.legend(loc='upper right')\n",
    "plt.savefig('./Images/DelayFrequencyHistogram.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.loc[df['ArrDelayMinutes'].isin(list(range(0,100)))]\n",
    "df2 = df.loc[df['ArrDelayMinutes'].isin(list(range(100,200)))]\n",
    "df3 = df.loc[df['ArrDelayMinutes'].isin(list(range(200,500)))]\n",
    "df4 = df.loc[df['ArrDelayMinutes'].isin(list(range(500,1000)))]\n",
    "df5 = df.loc[df['ArrDelayMinutes'].isin(list(range(1000,2000)))]\n",
    "df6 = df.loc[df['ArrDelayMinutes'].isin(list(range(2000,int(np.max(df['ArrDelayMinutes'])))))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = pickle.load(open('./TrainedModels/LinearRegressor.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0 - 100 Class Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df = df1.iloc[:, df1.columns != 'ArrDelayMinutes']\n",
    "actual_delay = df1.iloc[:, df1.columns == 'ArrDelayMinutes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_delay = regressor.predict(input_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "210.78321813013457\n"
     ]
    }
   ],
   "source": [
    "print(mean_squared_error(y_pred=predicted_delay,y_true=actual_delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6029732039636624\n"
     ]
    }
   ],
   "source": [
    "print(r2_score(y_pred=predicted_delay,y_true=actual_delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.441774437061289\n"
     ]
    }
   ],
   "source": [
    "print(mean_absolute_error(y_true=actual_delay,y_pred=predicted_delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.518375189053856\n"
     ]
    }
   ],
   "source": [
    "print(np.sqrt(mean_squared_error(y_pred=predicted_delay,y_true=actual_delay)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 100 - 200 Class Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df = df2.iloc[:, df2.columns != 'ArrDelayMinutes']\n",
    "actual_delay = df2.iloc[:, df2.columns == 'ArrDelayMinutes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_delay = regressor.predict(input_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "756.3162847262723\n"
     ]
    }
   ],
   "source": [
    "print(mean_squared_error(y_pred=predicted_delay,y_true=actual_delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.03494937782578167\n"
     ]
    }
   ],
   "source": [
    "print(r2_score(y_pred=predicted_delay,y_true=actual_delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.739545896361765\n"
     ]
    }
   ],
   "source": [
    "print(mean_absolute_error(y_true=actual_delay,y_pred=predicted_delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.50120515043427\n"
     ]
    }
   ],
   "source": [
    "print(np.sqrt(mean_squared_error(y_pred=predicted_delay,y_true=actual_delay)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 200 - 500 Class Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df = df3.iloc[:, df3.columns != 'ArrDelayMinutes']\n",
    "actual_delay = df3.iloc[:, df3.columns == 'ArrDelayMinutes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_delay = regressor.predict(input_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1046.4281786618342\n"
     ]
    }
   ],
   "source": [
    "print(mean_squared_error(y_pred=predicted_delay,y_true=actual_delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7605085908639139\n"
     ]
    }
   ],
   "source": [
    "print(r2_score(y_pred=predicted_delay,y_true=actual_delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.15461527631305\n"
     ]
    }
   ],
   "source": [
    "print(mean_absolute_error(y_true=actual_delay,y_pred=predicted_delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32.34854214121301\n"
     ]
    }
   ],
   "source": [
    "print(np.sqrt(mean_squared_error(y_pred=predicted_delay,y_true=actual_delay)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 500 - 1000 Class Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df = df4.iloc[:, df4.columns != 'ArrDelayMinutes']\n",
    "actual_delay = df4.iloc[:, df4.columns == 'ArrDelayMinutes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_delay = regressor.predict(input_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1795.413778018662\n"
     ]
    }
   ],
   "source": [
    "print(mean_squared_error(y_pred=predicted_delay,y_true=actual_delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9083921955917476\n"
     ]
    }
   ],
   "source": [
    "print(r2_score(y_pred=predicted_delay,y_true=actual_delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35.1792096446359\n"
     ]
    }
   ],
   "source": [
    "print(mean_absolute_error(y_true=actual_delay,y_pred=predicted_delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42.37232325491089\n"
     ]
    }
   ],
   "source": [
    "print(np.sqrt(mean_squared_error(y_pred=predicted_delay,y_true=actual_delay)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1000 - 2000 Class Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df = df5.iloc[:, df5.columns != 'ArrDelayMinutes']\n",
    "actual_delay = df5.iloc[:, df5.columns == 'ArrDelayMinutes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_delay = regressor.predict(input_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4834.639605102934\n"
     ]
    }
   ],
   "source": [
    "print(mean_squared_error(y_pred=predicted_delay,y_true=actual_delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8224974095103371\n"
     ]
    }
   ],
   "source": [
    "print(r2_score(y_pred=predicted_delay,y_true=actual_delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65.54959999344597\n"
     ]
    }
   ],
   "source": [
    "print(mean_absolute_error(y_true=actual_delay,y_pred=predicted_delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69.53157272133957\n"
     ]
    }
   ],
   "source": [
    "print(np.sqrt(mean_squared_error(y_pred=predicted_delay,y_true=actual_delay)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  > 2000 Class Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df = df6.iloc[:, df5.columns != 'ArrDelayMinutes']\n",
    "actual_delay = df6.iloc[:, df5.columns == 'ArrDelayMinutes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_delay = regressor.predict(input_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11236.181367586421\n"
     ]
    }
   ],
   "source": [
    "print(mean_squared_error(y_pred=predicted_delay,y_true=actual_delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mahesh/miniconda3/envs/icell/lib/python3.7/site-packages/sklearn/metrics/_regression.py:582: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
      "  warnings.warn(msg, UndefinedMetricWarning)\n"
     ]
    }
   ],
   "source": [
    "print(r2_score(y_pred=predicted_delay,y_true=actual_delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "106.00085550403082\n"
     ]
    }
   ],
   "source": [
    "print(mean_absolute_error(y_true=actual_delay,y_pred=predicted_delay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "106.00085550403082\n"
     ]
    }
   ],
   "source": [
    "print(np.sqrt(mean_squared_error(y_pred=predicted_delay,y_true=actual_delay)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2142.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(df['ArrDelayMinutes'])"
   ]
  }
 ],
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