Iv_Final_not smote.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Invistico_Airlines Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methods Used-\n",
    "\n",
    " 1. Logistic Regression\n",
    " 2. KNN algorithm\n",
    " 3. Decision Tree\n",
    " 4. Support Vector Machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import NullFormatter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.ticker as ticker\n",
    "from sklearn import preprocessing\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install -U imbalanced-learn --user"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "iv = pd.read_csv(r'C:\\Users\\ASUS\\Desktop\\Invistico_Airline.csv')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>satisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Customer Type</th>\n",
       "      <th>Age</th>\n",
       "      <th>Type of Travel</th>\n",
       "      <th>Class</th>\n",
       "      <th>Flight Distance</th>\n",
       "      <th>Seat comfort</th>\n",
       "      <th>Departure/Arrival time convenient</th>\n",
       "      <th>Food and drink</th>\n",
       "      <th>...</th>\n",
       "      <th>Online support</th>\n",
       "      <th>Ease of Online booking</th>\n",
       "      <th>On-board service</th>\n",
       "      <th>Leg room service</th>\n",
       "      <th>Baggage handling</th>\n",
       "      <th>Checkin service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Online boarding</th>\n",
       "      <th>Departure Delay in Minutes</th>\n",
       "      <th>Arrival Delay in Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>satisfied</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>65</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>265</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>satisfied</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>47</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>2464</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>310</td>\n",
       "      <td>305.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>satisfied</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>15</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>2138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>satisfied</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>60</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>623</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>satisfied</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>70</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  satisfaction  Gender   Customer Type  Age   Type of Travel     Class  \\\n",
       "0    satisfied  Female  Loyal Customer   65  Personal Travel       Eco   \n",
       "1    satisfied    Male  Loyal Customer   47  Personal Travel  Business   \n",
       "2    satisfied  Female  Loyal Customer   15  Personal Travel       Eco   \n",
       "3    satisfied  Female  Loyal Customer   60  Personal Travel       Eco   \n",
       "4    satisfied  Female  Loyal Customer   70  Personal Travel       Eco   \n",
       "\n",
       "   Flight Distance  Seat comfort  Departure/Arrival time convenient  \\\n",
       "0              265             0                                  0   \n",
       "1             2464             0                                  0   \n",
       "2             2138             0                                  0   \n",
       "3              623             0                                  0   \n",
       "4              354             0                                  0   \n",
       "\n",
       "   Food and drink  ...  Online support  Ease of Online booking  \\\n",
       "0               0  ...               2                       3   \n",
       "1               0  ...               2                       3   \n",
       "2               0  ...               2                       2   \n",
       "3               0  ...               3                       1   \n",
       "4               0  ...               4                       2   \n",
       "\n",
       "   On-board service  Leg room service  Baggage handling  Checkin service  \\\n",
       "0                 3                 0                 3                5   \n",
       "1                 4                 4                 4                2   \n",
       "2                 3                 3                 4                4   \n",
       "3                 1                 0                 1                4   \n",
       "4                 2                 0                 2                4   \n",
       "\n",
       "   Cleanliness  Online boarding  Departure Delay in Minutes  \\\n",
       "0            3                2                           0   \n",
       "1            3                2                         310   \n",
       "2            4                2                           0   \n",
       "3            1                3                           0   \n",
       "4            2                5                           0   \n",
       "\n",
       "   Arrival Delay in Minutes  \n",
       "0                       0.0  \n",
       "1                     305.0  \n",
       "2                       0.0  \n",
       "3                       0.0  \n",
       "4                       0.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iv.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['satisfaction', 'Gender', 'Customer Type', 'Age', 'Type of Travel',\n",
       "       'Class', 'Flight Distance', 'Seat comfort',\n",
       "       'Departure/Arrival time convenient', 'Food and drink', 'Gate location',\n",
       "       'Inflight wifi service', 'Inflight entertainment', 'Online support',\n",
       "       'Ease of Online booking', 'On-board service', 'Leg room service',\n",
       "       'Baggage handling', 'Checkin service', 'Cleanliness', 'Online boarding',\n",
       "       'Departure Delay in Minutes', 'Arrival Delay in Minutes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iv.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(129880, 23)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfied       71087\n",
       "dissatisfied    58793\n",
       "Name: satisfaction, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iv['satisfaction'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loyal Customer       106100\n",
       "disloyal Customer     23780\n",
       "Name: Customer Type, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iv['Customer Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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795fqbLVyfW3atCEmJoZ27doREhLC8uXL+eCDD4iLi8Pb25ugoCCCg4Np1aoVzz77LDabjbVr1zJ37lwCAwNp37490dHRtGnThjlz5pCens6aNWtKXI579uxZoqOjMcYwbtw4x/Lu3btXdPd/M+1hiJSz++67j6NHj5KdnY2Xlxdr166lS5cuZGZmkpyczPr16ykqKsLT09NxwvUnixcv5qOPPsJms/HYY4/x9NNPV1Avbn0vvPACL7zwwhXL33333auWb968ueMQ1M9FR0cTHR191XU+/fTTm2tkJaM9DJEK8Mgjj/DZZ59hjGH37t3cf//92O12zp8/z8KFC1m6dClFRUXs2bPHsc6hQ4dYt24dS5cuZenSpWzYsIEjR45UYC+kqtEehkgF6NKlC/Hx8TRq1IiwsDAAnJyccHV1ZcSIEdxxxx2cPHmyxAy0Bw4cICMjg/79+wPw448/cuzYMfz9/SuiC1IFKTBEKkCjRo24cOECixcvZsSIERw/fpzc3Fw2bNjAypUruXjxIhEREfx8qjd/f38aN27M/PnzsdlsLFy4kHvuuacCeyFVjQJDqrRLxcXXvbLpRutzdXa2VPaxxx7j73//O35+fhw/fhxnZ2dq1KhBREQE1apVo27duo75jeDyuY+HHnqI3r17U1hYSHBwML///e9Lre0i16PZai3QbLWXabba29PtsE1uJKhvd6WxLa41JrSHISK3vNK+n+ZWVpp7zL+kq6RERMQSBYZUObfxUdgbpm0hN0KBIVVK9erVOXv2rP5Qcjkszp49S/Xq1Su6KXKL0DkMqVJ8fHw4ceLELf3Us9JUvXp1fHx8KroZcotQYEiV4urqip+fX0U3Q+SWpENSIiJiiQJDREQsUWCIiIglCgwREbFEgSEiIpYoMERExBIFhoiIWKLAEBERSxQYIiJiiQJDREQsUWCIiIglCgwREbFEgSEiIpYoMERExBIFhoiIWKLAEBERSxQYIiJiiQJDylRycjLNmzcnJCSEVq1a8fXXXwMQHx9PQEAAzZo1o1+/fuTn55dYb/369YSEhJRYNnLkSHx9fQkJCSEkJIRevXoBUFBQwHPPPUeTJk24//77iY2NxW63l08HRaqQMgmM4uJioqOjiYyMpG/fvhw7doz09HR69+5Nnz59iIuLc3yh58yZQ48ePYiMjGT37t0AN1RWKq/9+/czevRoPvnkE1JTU5kwYQIRERF88cUXLF++nF27drFnzx6ys7OZPXs2ABcvXmTChAn06tWLoqKiEvVt3bqV5cuXk5qaSmpqKu+99x4AU6dOJT09nT179rBr1y4yMzOZO3duufdX5HZXJoGxadMmAJYvX87QoUOZNm0a06ZNY9iwYSxduhRjDBs3bmTv3r3s2LGDlStXMmvWLCZNmgRwQ2Wl8nJzc2P+/Pk0aNAAgLCwME6ePElBQQH5+flcvHiRS5cukZ+fT/Xq1QH49NNPycvLIykpqURdBQUFfPPNN7zyyisEBQXx3//93xw7dgyAlJQUIiMjqV69OjabjSeffJL333+/fDsrUgWUSWB07NiRhIQEADIyMqhTpw579+6lRYsWALRt25atW7eSkpJC69atsdlsNGzYkOLiYs6dO3dDZaXyuuuuu3j88ccBMMYwYsQIunbtSqdOnXjkkUfw9fWlfv36nD9/nueeew6AJ598ktdeew0vL68SdWVkZNChQwemTJnC7t27admyJd26dcMYQ3h4OO+99x65ubkUFhaydOlSMjMzy72/Irc7lzKr2MWFsWPH8tlnn/H666+zadMmbDYbAO7u7uTk5JCbm0utWrUc6/y03Bhjuay3t/c121BQUEBaWtpN9SMgIOCm1r/d/JbteeHCBcaPH8/Jkyd56623mDJlCt999x1ffPEFrq6uxMTE8MwzzzBhwgTHOunp6Vf8/mbOnAnAv/71Lx5//HEmTZrEhg0b6Nq1K0eOHCE0NBQvLy8effRRdu7cedO/e7l16HtaUlmN/TILDIAZM2YwatQoevbsSUFBgWN5Xl4eXl5eeHh4kJeXV2K5p6cnTk5Olsv+Gjc3Nw2kUnaj2/PYsWNERkYSEBDABx98QI0aNUhISGDQoEGEhYUBMGrUKF566aUSdf/73/8u8fvbvXs33377LVFRUQCO/1QEBATg4uLC1KlTHYe+li5dSmBgoH73UmXd7Ni/VuCUySGpDz74gDfffBOAGjVqYLPZaNasGdu3bwdg8+bNhIWFERoaypYtW7Db7WRkZGC32/H29qZp06aWy0rllZOTQ7t27YiIiGD58uXUqFEDgNDQUFavXk1RURHGGFavXk3Lli1/tS4nJyeGDh3K999/D8Abb7xBcHAwPj4+rF27lueeew5jDLm5ubz22mv07du3zPsnUtWUyR7Gn/70J6Kjo+nbty9FRUWMHz+eu+++m4kTJzJr1iz8/f3p1KkTzs7OhIWF0atXL+x2O7GxsQCMHTvWclmpvObMmUN6ejpr1qxhzZo1juUff/wxL7/8Mk2bNsXNzY3mzZvzt7/97VfratasGbNnz6ZLly4UFxfj4+PDsmXLABgwYADbt2+nWbNmFBcX85e//IUePXqUad9EqiKbMcZUdCPKSlpaWqkclnhswmul0Jpb37opwyu6CSLXNHrtZxXdhEohsesjN13Htf526sY9ERGxRIEhIiKWKDBERMQSBYaIiFiiwBDLLhUXV3QTKgVtB6mqyvTGPbm9uDo760oUSucqFJFbkfYwRETEEgWGiIhYosAQERFLFBgiImKJAkNERCxRYIiIiCUKDBERsUSBISIiligwRETEEgWGiIhYosAQqUKSk5Np3rw5ISEhtGrViq+//hq73c6YMWMIDAwkKCiIiIgITp8+DcDRo0fp3LkzAQEBPPjgg6xYsQKADRs2EBIS4vi59957sdlspKSkOD7r/PnzBAcH8/XXX1dIX6X0aS4pkSpi//79jB49ml27dtGgQQPWrVtHREQEsbGxpKSksGvXLtzc3BgzZgwjR45k0aJF9OvXj/bt2/PJJ5+Qk5ND+/btuffee+nYsSOpqamOunv06EFERAQPPPAAAOvWrWP48OEcPXq0gnorZUF7GCJVhJubG/Pnz6dBgwYAhIWFcfLkSZo0aUJiYiJubm6O5enp6QCkpKTQv39/ADw9PWnfvn2J57PD5b2Wo0ePkpCQ4Fj2+uuvk5yc7PgsuT0oMESqiLvuuovHH38cAGMMI0aMoGvXrjz88MOEhoYCkJWVxeTJk3nqqacACA8PZ8GCBRhjOH36NOvWrSMzM9NRZ2FhIePHj+evf/0rLi7/OWDxySef8OCDD5Zj76Q8KDBEqpi8vDx69uzJoUOHmD9/vmP54cOHadu2La1bt2bw4MEAJCUlkZaWRnBwMAMHDuSJJ56gWrVqjnXef/99/P39ad26dbn3Q8qfpcBYuXJlideLFi0qk8aISNk6duwYrVq1wtnZmU2bNlGrVi0ANm3axEMPPUS/fv2YN28eNpsNgIsXL7JgwQL27NnD2rVrycrKonHjxo763nvvPZ555pkK6YuUv1896f3RRx/x+eefs337drZt2wZAcXExBw8e5Omnny6XBopI6cjJyaFdu3b069ePuLg4x/Jdu3bRvXt3li9fTufOnUusExcXR2hoKKNGjeLAgQOsXbuWiRMnApcPa23evJm//e1v5doPqTi/Ghht2rShbt26nD9/nl69egHg5OREo0aNyqVxIlJ65syZQ3p6OmvWrClx4rpu3boYYxg3bhzjxo0DwM/PjzVr1pCYmEhUVBRJSUm4uLiQlJTk+P6fOXOG3NxcfHx8KqQ/Uv5+NTBq1qxJeHg44eHhnD17loKCAuDyXoaI3Fqio6OJjo6+oXX+8Ic/8Pnnn1/1vbp163Lp0qVfXV+X1d5eLN2HMWnSJL788kvq1auHMQabzcby5cvLum0iIlKJWAqMb7/9lg0bNuDkpIuqRESqKksJcOeddzoOR4mISNVkaQ8jMzOT9u3bc+eddwLokJSISBVkKTBmzpxZ1u0QkRtUeKmIaq6aDk7Kj6XR9su5YwBeeumlUm+MiFhXzdWFxya8VtHNqBTWTRle0U2oEiwFRp06dYDLN+rs27cPu91epo0SEZHKx1JgREZGlnj97LPPlkljRESk8rIUGN9//73j36dPny4xW6WIiFQNlgIjNjbW8e+fHrAiIiJVi6XAWLx4MVlZWRw/fhwfHx+8vb3Lul0iIlLJWLpx7x//+AeRkZHMmzePXr168fe//72s2yUiIpWMpT2MhQsXsnr1atzd3cnNzaVfv35069atrNsmIiKViKU9DJvNhru7OwAeHh6OZ/+KiEjVYWkPw9fXl+nTpxMWFkZKSgq+vr5l3S4REalkLAVGz5492blzJ1u3buXjjz8u8RzgX7p06RLjx4/nhx9+oLCwkBdeeIHGjRszbtw4bDYbTZo0IS4uDicnJ+bMmcMXX3yBi4sL48ePJzg4mPT0dMtlRUSk/FgKjOnTpzN9+nQaN27MM888w7hx41iyZMlVy65du5ZatWqRmJhIVlYW3bt357777mPYsGGEh4cTGxvLxo0badiwITt27GDlypVkZmYyZMgQVq1axbRp0yyXFRGR8mMpMFxcXBwPfm/UqNGvPhejc+fOdOrUyfHa2dmZvXv30qJFCwDatm3LV199hZ+fH61bt8Zms9GwYUOKi4s5d+7cDZXV5b0iIuXHUmA0bNiQWbNmERISwu7du6lXr941y/50cjw3N5ehQ4cybNgwZsyYgc1mc7yfk5NDbm4utWrVKrFeTk6O44l+VspeLzAKCgpIS0uz0sVrCggIuKn15fZ0s+OqNGhsyrWU1fi0FBjTpk1j2bJlfPnll9x99928+OKLv1o+MzOTwYMH06dPH7p06UJiYqLjvby8PLy8vPDw8CAvL6/Eck9PzxJ7L9crez1ubm76UkmZ0LiSyuxmx+e1AsfSZbVubm7079+fSZMm8fTTT1OtWrVrlj1z5gwDBgxg9OjR9OjRA4CmTZuyfft2ADZv3kxYWBihoaFs2bIFu91ORkYGdrsdb2/vGyorIiLlp9SfvjJv3jyys7OZO3cuc+fOBSAmJoYpU6Ywa9Ys/P396dSpE87OzoSFhdGrVy/sdrtjvqqxY8cyceJES2VFRKT8lHpgTJgwgQkTJlyxPDk5+YplQ4YMYciQISWW+fn5WS4rIiLlx9IhKREREQWGiIhYosAQERFLFBgiImKJAkNERCxRYIiIiCUKDBERsUSBISIiligwRETEEgWGiIhYosAQERFLFBgiImKJAkNERCxRYIiIiCUKDBERsUSBISIiligwRETEEgWGiIhYosAQERFLFBgiImKJAkNERCxRYIiIiCUKDBERsUSBISIiligwRETEEgWGiIhYosAQERFLFBgiImKJAkNERCxRYIiIiCUKDBERsUSBISIiligwRETEEgWGiIhYosAQERFLFBgiImKJAkNERCxRYIiIiCUKDBERsUSBISIilpRZYHz77bdERUUBkJ6eTu/evenTpw9xcXHY7XYA5syZQ48ePYiMjGT37t03XFZERMpPmQTG22+/zYQJEygoKABg2rRpDBs2jKVLl2KMYePGjezdu5cdO3awcuVKZs2axaRJk264rIiIlJ8yCQxfX19mz57teL13715atGgBQNu2bdm6dSspKSm0bt0am81Gw4YNKS4u5ty5czdUVkREyk+ZBEanTp1wcXFxvDbGYLPZAHB3dycnJ4fc3Fw8PDwcZX5afiNlRUSk/Lhcv8jNc3L6Ty7l5eXh5eWFh4cHeXl5JZZ7enreUNnrKSgoIC0t7abaHhAQcFPry+3pZsdVadDYlGspq/FZLoHRtGlTtm/fTnh4OJs3b6Zly5b4+vqSmJjIwIEDOXnyJHa7HW9v7xsqez1ubm76UkmZ0LiSyuxmx+e1AqdcAmPs2LFMnDiRWbNm4e/vT6dOnXB2diYsLIxevXpht9uJjY294bIiIlJ+yiwwfHx8WLFiBQB+fn4kJydfUWbIkCEMGTKkxLIbKSsiIuVHN+6JiIglCgwREbFEgSEiIpYoMERExBIFhoiIWKLAEBERSxQYIiJiiQJDREQsUWCIiIglCpYEPikAAAfmSURBVAwREbFEgSEiIpYoMERExBIFhoiIWKLAEBERSxQYIiJiiQJDREQsUWCIiIglCgwREbFEgSEiIpYoMERExBIFhoiIWKLAEBERSxQYIiJiiQJDREQsUWCIiIglCgwREbFEgSEiIpYoMERExBIFhoiIWKLAEBERSxQYIiJiiQJDREQsUWCIiIglCgwREbFEgSEiIpYoMERExBIFhoiIWKLAEBERSxQYIiJiiQJDREQsUWCIiIglLhXdgBtht9uJj49n//79VKtWjSlTpnDnnXdWdLNERKqEW2oPY8OGDRQWFvLee+8xcuRIpk+fXtFNEhGpMm6pwEhJSaFNmzYAhISE8N1331Vwi0REqg6bMcZUdCOsiomJ4U9/+hMPP/wwAO3atWPDhg24uFz9yFpqaipubm7l2UQRkVteQUEBISEhVyy/pc5heHh4kJeX53htt9uvGRbAVTssIiK/zS11SCo0NJTNmzcDl/ce7rnnngpukYhI1XFLHZL66SqpAwcOYIxh6tSp3H333RXdLBGRKuGWCgwREak4t9QhKRERqTgKDBERsUSBIQBkZGTw+eefA/Dyyy+TkZFx1XLFxcUMHDiQ3r17s3DhQjZu3Gip/mXLljF79uxSa6/cmgoKCujQocOvjrEboXFbvm6py2ql7Gzbto0jR47QoUMHYmJirlnu9OnTZGVlsXr16nJsndxufm2M3QiN2/KlwLjNff/990RHR+Pi4oKzszOvvPIKr7/+OidPniQrK4u2bdsyZMgQ3nrrLfLz87n//vtZuHAh8fHxnD9/nhkzZuDi4oKXlxevvvoqEydO5OjRo8TGxlK3bl3q1KlD7969mTlzJjt37sQYQ//+/Xn00Uf5+uuvmTp1KjVr1sTJyUn3xVRReXl5jBo1iuzsbHx9fQGIioq65hg7ffr0FWO2Tp06xMbGatxWMB2Sus1t3bqVwMBAFixYwPPPP8+PP/5ISEgI77zzDsuWLWPZsmU4OzszaNAgnnjiCf74xz861t2wYQOPPPIIycnJ9OjRg+zsbOLi4mjcuDGTJ092lPvyyy85ceIEy5cvZ9GiRcybN4/s7GymTZvGzJkzWbBgAT4+PhXRfakE1qxZwz333MOSJUuIjIws8d7VxtjVxmxmZqbGbSWgwLjN9ejRg9/97nc8++yzLFmyBLvdzp49exg5ciRTp06lsLDwmus+//zznDt3jn79+vHJJ59c8676AwcOsHfvXqKionj22WcpKioiIyODf//73/j5+QGXb7qUqungwYMEBQUB0Lx58xLj6Gpj7Jdj1tnZmVq1amncVgIKjNvcxo0beeCBB0hKSqJz585069YNT09PZs6cyYABA8jPz8cYg5OTE3a7vcS6H374Id27d2fx4sU0adKEFStWXPUz/P39CQ8PZ/HixSQlJfHoo4/i4+ND3bp1OXz4MAB79uwp875K5eTv709qaioA+/bto6ioyPHe1cbYL8fs/PnzWb16tcZtJaBzGLe5Zs2aMXr0aGbPno2TkxNLly4lPj6elJQUatSowZ133smpU6e45557eOONNwgMDHSsGxQUxLhx47jjjjtwdXUtsTv/cx06dGDHjh306dOHCxcu0LFjRzw8PEhMTGTs2LG4u7vj7u5OzZo1y6vbUon07duX6Ohoevfujb+/P66uro73rjbGjDElxmx0dDTVqlVjxIgRGrcVTHd6i4iIJTokJSIiligwRETEEgWGiIhYosAQERFLFBgiImKJAkPkN/otE9/9+OOPluvfuXMn//rXvwB46aWXbr7BIjdJgSHyG23bto1du3YBlyfTa9iw4VXL/TTx3bJly27omv5Vq1Zx6tQpAObMmXPzDRa5SbpxT+QXynLCxhdffJH4+HgKCgo4f/48gwcPpmPHjmzatMkRCk2bNqVXr1783//9H3v37qVx48Y89dRTfPXVV+zbt4+EhAScnZ1xc3MjISEBu93OyJEjqV+/PsePHycoKIhJkyZV8FaU25IRkRKSk5PN5MmTTWFhodm6davZv3+/WbFihTHGmPz8fNOiRQtjjDGrVq0yiYmJxhhj/vznP5tDhw6Z6dOnm7feessUFxebzz77zPzwww/m+PHj5qmnnjLGGPPVV1+Zbdu2GWOMSUlJMf379zeXLl0y7du3N2fOnDHGGDN79mzzww8/mLFjx5ovv/zSGGNMq1atjDHGdO/e3ezbt88YY8xnn31mhgwZYo4fP25atGhhcnJyTFFRkWnXrp05depUOW0tqUq0hyHyCz169ODtt9/m2WefxdPTk5deeok9e/awbds2PDw8rjvx3bx58+jXrx+///3vCQ4OLlG+bt26vPHGG7z//vvYbDaKiorIysrCy8uL2rVrA79+vuLUqVMEBAQA8OCDDzJz5kwAfH198fDwcHxGQUHBTW8HkV/SOQyRXyjLCRv/93//l27dupGYmEh4eDjGGGrXrk12djbnz58HYMqUKezevRubzYb5xcw99erVc5wI37lzJ3fddRcANputjLaGyH9oD0PkF8pywsbOnTvz8ssv8+abb9KgQQOysrJwcnIiLi6O5557DicnJ5o2bUpQUBD79u3j1VdfLfFMhilTppCQkIAxBmdnZ6ZOnVpu20VEkw+KiIglOiQlIiKWKDBERMQSBYaIiFiiwBAREUsUGCIiYokCQ0RELFFgiIiIJQoMERGx5P8BWtW42NbIdFUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "fig=sns.countplot(x='satisfaction', hue='Gender', data=iv, palette='GnBu_d')\n",
    "for i in fig.patches:\n",
    "    # get_x pulls left or right; get_height pushes up or down\n",
    "    fig.text(i.get_x()+.07, i.get_height()+1000, str(round((i.get_height()), 2)), fontsize=12, color='black',\n",
    "                rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "fig=sns.countplot(x='Customer Type', hue='Gender', data=iv, palette='BuGn_r')\n",
    "\n",
    "for i in fig.patches:\n",
    "    # get_x pulls left or right; get_height pushes up or down\n",
    "    fig.text(i.get_x()+.07, i.get_height()+1000, str(round((i.get_height()), 2)), fontsize=12, color='black',\n",
    "                rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "fig=sns.countplot(x='Class', hue='Gender', data=iv,palette='PiYG_r')\n",
    "for i in fig.patches:\n",
    "    # get_x pulls left or right; get_height pushes up or down\n",
    "    fig.text(i.get_x()+.07, i.get_height()+1000, str(round((i.get_height()), 2)), fontsize=12, color='black',\n",
    "                rotation=0)"
   ]
  },
  {
   "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>Age</th>\n",
       "      <th>Flight Distance</th>\n",
       "      <th>Seat comfort</th>\n",
       "      <th>Departure/Arrival time convenient</th>\n",
       "      <th>Food and drink</th>\n",
       "      <th>Gate location</th>\n",
       "      <th>Inflight wifi service</th>\n",
       "      <th>Inflight entertainment</th>\n",
       "      <th>Online support</th>\n",
       "      <th>Ease of Online booking</th>\n",
       "      <th>On-board service</th>\n",
       "      <th>Leg room service</th>\n",
       "      <th>Baggage handling</th>\n",
       "      <th>Checkin service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Online boarding</th>\n",
       "      <th>Departure Delay in Minutes</th>\n",
       "      <th>Arrival Delay in Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129880.000000</td>\n",
       "      <td>129487.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>39.427957</td>\n",
       "      <td>1981.409055</td>\n",
       "      <td>2.838597</td>\n",
       "      <td>2.990645</td>\n",
       "      <td>2.851994</td>\n",
       "      <td>2.990422</td>\n",
       "      <td>3.249130</td>\n",
       "      <td>3.383477</td>\n",
       "      <td>3.519703</td>\n",
       "      <td>3.472105</td>\n",
       "      <td>3.465075</td>\n",
       "      <td>3.485902</td>\n",
       "      <td>3.695673</td>\n",
       "      <td>3.340807</td>\n",
       "      <td>3.705759</td>\n",
       "      <td>3.352587</td>\n",
       "      <td>14.713713</td>\n",
       "      <td>15.091129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>15.119360</td>\n",
       "      <td>1027.115606</td>\n",
       "      <td>1.392983</td>\n",
       "      <td>1.527224</td>\n",
       "      <td>1.443729</td>\n",
       "      <td>1.305970</td>\n",
       "      <td>1.318818</td>\n",
       "      <td>1.346059</td>\n",
       "      <td>1.306511</td>\n",
       "      <td>1.305560</td>\n",
       "      <td>1.270836</td>\n",
       "      <td>1.292226</td>\n",
       "      <td>1.156483</td>\n",
       "      <td>1.260582</td>\n",
       "      <td>1.151774</td>\n",
       "      <td>1.298715</td>\n",
       "      <td>38.071126</td>\n",
       "      <td>38.465650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1359.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>1925.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>2544.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>6951.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1592.000000</td>\n",
       "      <td>1584.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Age  Flight Distance   Seat comfort  \\\n",
       "count  129880.000000    129880.000000  129880.000000   \n",
       "mean       39.427957      1981.409055       2.838597   \n",
       "std        15.119360      1027.115606       1.392983   \n",
       "min         7.000000        50.000000       0.000000   \n",
       "25%        27.000000      1359.000000       2.000000   \n",
       "50%        40.000000      1925.000000       3.000000   \n",
       "75%        51.000000      2544.000000       4.000000   \n",
       "max        85.000000      6951.000000       5.000000   \n",
       "\n",
       "       Departure/Arrival time convenient  Food and drink  Gate location  \\\n",
       "count                      129880.000000   129880.000000  129880.000000   \n",
       "mean                            2.990645        2.851994       2.990422   \n",
       "std                             1.527224        1.443729       1.305970   \n",
       "min                             0.000000        0.000000       0.000000   \n",
       "25%                             2.000000        2.000000       2.000000   \n",
       "50%                             3.000000        3.000000       3.000000   \n",
       "75%                             4.000000        4.000000       4.000000   \n",
       "max                             5.000000        5.000000       5.000000   \n",
       "\n",
       "       Inflight wifi service  Inflight entertainment  Online support  \\\n",
       "count          129880.000000           129880.000000   129880.000000   \n",
       "mean                3.249130                3.383477        3.519703   \n",
       "std                 1.318818                1.346059        1.306511   \n",
       "min                 0.000000                0.000000        0.000000   \n",
       "25%                 2.000000                2.000000        3.000000   \n",
       "50%                 3.000000                4.000000        4.000000   \n",
       "75%                 4.000000                4.000000        5.000000   \n",
       "max                 5.000000                5.000000        5.000000   \n",
       "\n",
       "       Ease of Online booking  On-board service  Leg room service  \\\n",
       "count           129880.000000     129880.000000     129880.000000   \n",
       "mean                 3.472105          3.465075          3.485902   \n",
       "std                  1.305560          1.270836          1.292226   \n",
       "min                  0.000000          0.000000          0.000000   \n",
       "25%                  2.000000          3.000000          2.000000   \n",
       "50%                  4.000000          4.000000          4.000000   \n",
       "75%                  5.000000          4.000000          5.000000   \n",
       "max                  5.000000          5.000000          5.000000   \n",
       "\n",
       "       Baggage handling  Checkin service    Cleanliness  Online boarding  \\\n",
       "count     129880.000000    129880.000000  129880.000000    129880.000000   \n",
       "mean           3.695673         3.340807       3.705759         3.352587   \n",
       "std            1.156483         1.260582       1.151774         1.298715   \n",
       "min            1.000000         0.000000       0.000000         0.000000   \n",
       "25%            3.000000         3.000000       3.000000         2.000000   \n",
       "50%            4.000000         3.000000       4.000000         4.000000   \n",
       "75%            5.000000         4.000000       5.000000         4.000000   \n",
       "max            5.000000         5.000000       5.000000         5.000000   \n",
       "\n",
       "       Departure Delay in Minutes  Arrival Delay in Minutes  \n",
       "count               129880.000000             129487.000000  \n",
       "mean                    14.713713                 15.091129  \n",
       "std                     38.071126                 38.465650  \n",
       "min                      0.000000                  0.000000  \n",
       "25%                      0.000000                  0.000000  \n",
       "50%                      0.000000                  0.000000  \n",
       "75%                     12.000000                 13.000000  \n",
       "max                   1592.000000               1584.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iv.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Detect Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction                           0\n",
       "Gender                                 0\n",
       "Customer Type                          0\n",
       "Age                                    0\n",
       "Type of Travel                         0\n",
       "Class                                  0\n",
       "Flight Distance                        0\n",
       "Seat comfort                           0\n",
       "Departure/Arrival time convenient      0\n",
       "Food and drink                         0\n",
       "Gate location                          0\n",
       "Inflight wifi service                  0\n",
       "Inflight entertainment                 0\n",
       "Online support                         0\n",
       "Ease of Online booking                 0\n",
       "On-board service                       0\n",
       "Leg room service                       0\n",
       "Baggage handling                       0\n",
       "Checkin service                        0\n",
       "Cleanliness                            0\n",
       "Online boarding                        0\n",
       "Departure Delay in Minutes             0\n",
       "Arrival Delay in Minutes             393\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iv.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete Rows with Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "iv=iv.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(129487, 23)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "iv = iv.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>satisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Customer Type</th>\n",
       "      <th>Age</th>\n",
       "      <th>Type of Travel</th>\n",
       "      <th>Class</th>\n",
       "      <th>Flight Distance</th>\n",
       "      <th>Seat comfort</th>\n",
       "      <th>Departure/Arrival time convenient</th>\n",
       "      <th>Food and drink</th>\n",
       "      <th>...</th>\n",
       "      <th>Online support</th>\n",
       "      <th>Ease of Online booking</th>\n",
       "      <th>On-board service</th>\n",
       "      <th>Leg room service</th>\n",
       "      <th>Baggage handling</th>\n",
       "      <th>Checkin service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Online boarding</th>\n",
       "      <th>Departure Delay in Minutes</th>\n",
       "      <th>Arrival Delay in Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>satisfied</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>65</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>265</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>satisfied</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>47</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>2464</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>310</td>\n",
       "      <td>305.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>satisfied</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>15</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>2138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>satisfied</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>60</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>623</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>satisfied</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>70</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129477</td>\n",
       "      <td>satisfied</td>\n",
       "      <td>Female</td>\n",
       "      <td>disloyal Customer</td>\n",
       "      <td>55</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>1953</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129478</td>\n",
       "      <td>satisfied</td>\n",
       "      <td>Female</td>\n",
       "      <td>disloyal Customer</td>\n",
       "      <td>70</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>1674</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>54</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129479</td>\n",
       "      <td>satisfied</td>\n",
       "      <td>Female</td>\n",
       "      <td>disloyal Customer</td>\n",
       "      <td>35</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>3287</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129480</td>\n",
       "      <td>satisfied</td>\n",
       "      <td>Female</td>\n",
       "      <td>disloyal Customer</td>\n",
       "      <td>69</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>2240</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129481</td>\n",
       "      <td>satisfied</td>\n",
       "      <td>Female</td>\n",
       "      <td>disloyal Customer</td>\n",
       "      <td>11</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>2752</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>129482 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction  Gender      Customer Type  Age   Type of Travel  \\\n",
       "0         satisfied  Female     Loyal Customer   65  Personal Travel   \n",
       "1         satisfied    Male     Loyal Customer   47  Personal Travel   \n",
       "2         satisfied  Female     Loyal Customer   15  Personal Travel   \n",
       "3         satisfied  Female     Loyal Customer   60  Personal Travel   \n",
       "4         satisfied  Female     Loyal Customer   70  Personal Travel   \n",
       "...             ...     ...                ...  ...              ...   \n",
       "129477    satisfied  Female  disloyal Customer   55  Personal Travel   \n",
       "129478    satisfied  Female  disloyal Customer   70  Personal Travel   \n",
       "129479    satisfied  Female  disloyal Customer   35  Personal Travel   \n",
       "129480    satisfied  Female  disloyal Customer   69  Personal Travel   \n",
       "129481    satisfied  Female  disloyal Customer   11  Personal Travel   \n",
       "\n",
       "           Class  Flight Distance  Seat comfort  \\\n",
       "0            Eco              265             0   \n",
       "1       Business             2464             0   \n",
       "2            Eco             2138             0   \n",
       "3            Eco              623             0   \n",
       "4            Eco              354             0   \n",
       "...          ...              ...           ...   \n",
       "129477       Eco             1953             5   \n",
       "129478       Eco             1674             5   \n",
       "129479       Eco             3287             5   \n",
       "129480       Eco             2240             5   \n",
       "129481       Eco             2752             5   \n",
       "\n",
       "        Departure/Arrival time convenient  Food and drink  ...  \\\n",
       "0                                       0               0  ...   \n",
       "1                                       0               0  ...   \n",
       "2                                       0               0  ...   \n",
       "3                                       0               0  ...   \n",
       "4                                       0               0  ...   \n",
       "...                                   ...             ...  ...   \n",
       "129477                                  2               5  ...   \n",
       "129478                                  4               5  ...   \n",
       "129479                                  4               5  ...   \n",
       "129480                                  4               5  ...   \n",
       "129481                                  5               5  ...   \n",
       "\n",
       "        Online support  Ease of Online booking  On-board service  \\\n",
       "0                    2                       3                 3   \n",
       "1                    2                       3                 4   \n",
       "2                    2                       2                 3   \n",
       "3                    3                       1                 1   \n",
       "4                    4                       2                 2   \n",
       "...                ...                     ...               ...   \n",
       "129477               5                       1                 1   \n",
       "129478               5                       5                 3   \n",
       "129479               2                       2                 4   \n",
       "129480               4                       4                 5   \n",
       "129481               2                       2                 3   \n",
       "\n",
       "        Leg room service  Baggage handling  Checkin service  Cleanliness  \\\n",
       "0                      0                 3                5            3   \n",
       "1                      4                 4                2            3   \n",
       "2                      3                 4                4            4   \n",
       "3                      0                 1                4            1   \n",
       "4                      0                 2                4            2   \n",
       "...                  ...               ...              ...          ...   \n",
       "129477                 1                 3                3            4   \n",
       "129478                 2                 4                5            4   \n",
       "129479                 5                 4                4            3   \n",
       "129480                 4                 4                3            4   \n",
       "129481                 5                 3                5            4   \n",
       "\n",
       "        Online boarding  Departure Delay in Minutes  Arrival Delay in Minutes  \n",
       "0                     2                           0                       0.0  \n",
       "1                     2                         310                     305.0  \n",
       "2                     2                           0                       0.0  \n",
       "3                     3                           0                       0.0  \n",
       "4                     5                           0                       0.0  \n",
       "...                 ...                         ...                       ...  \n",
       "129477                1                           0                       0.0  \n",
       "129478                5                          54                      46.0  \n",
       "129479                2                           9                       0.0  \n",
       "129480                4                           4                       0.0  \n",
       "129481                2                           5                       0.0  \n",
       "\n",
       "[129482 rows x 23 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iv.head(-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dummy Creation\n",
    "\n",
    "##  Satisfaction-------> \n",
    "  \n",
    "  ### * Satisfied = 1\n",
    "  ### * Dissatisfied = 0\n",
    "  \n",
    "## Gender--------->\n",
    " \n",
    " ### * Female = 1\n",
    " ### * Male = 0\n",
    " \n",
    "## Customer Type------->\n",
    " \n",
    " ### * Loyal Customer = 1\n",
    " ### * Disloyal Customer = 0\n",
    " \n",
    "## Type of Travel--------->\n",
    " \n",
    " ### * Business Travel = 1\n",
    " ### * Personal Travel = 0\n",
    " \n",
    "## Class------------->\n",
    " \n",
    " ### * Business = reference Class\n",
    " ### * Eco\n",
    " ### * Eco Plus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfied       70882\n",
       "dissatisfied    58605\n",
       "Name: satisfaction, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iv['satisfaction'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "iv['satisfaction'].replace(to_replace=['satisfied','dissatisfied'], value=[1,0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    65703\n",
       "Male      63784\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iv['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "iv['Gender'].replace(to_replace=['Female','Male'], value=[1,0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loyal Customer       105773\n",
       "disloyal Customer     23714\n",
       "Name: Customer Type, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iv['Customer Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "iv['Customer Type'].replace(to_replace=['Loyal Customer','disloyal Customer'], value=[1,0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Business travel    89445\n",
       "Personal Travel    40042\n",
       "Name: Type of Travel, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iv['Type of Travel'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "iv['Type of Travel'].replace(to_replace=['Business travel','Personal Travel'], value=[1,0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Class = pd.get_dummies(iv['Class'],prefix='class',drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>class_Eco</th>\n",
       "      <th>class_Eco Plus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   class_Eco  class_Eco Plus\n",
       "0          1               0\n",
       "1          0               0\n",
       "2          1               0\n",
       "3          1               0\n",
       "4          1               0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Class.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "iv = pd.concat([iv, Class], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "iv=iv.drop(['Class'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['satisfaction', 'Gender', 'Customer Type', 'Age', 'Type of Travel',\n",
       "       'Flight Distance', 'Seat comfort', 'Departure/Arrival time convenient',\n",
       "       'Food and drink', 'Gate location', 'Inflight wifi service',\n",
       "       'Inflight entertainment', 'Online support', 'Ease of Online booking',\n",
       "       'On-board service', 'Leg room service', 'Baggage handling',\n",
       "       'Checkin service', 'Cleanliness', 'Online boarding',\n",
       "       'Departure Delay in Minutes', 'Arrival Delay in Minutes', 'class_Eco',\n",
       "       'class_Eco Plus'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iv.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols=['Gender', 'Customer Type', 'Age', 'Type of Travel',\n",
    "       'Flight Distance', 'Seat comfort', 'Departure/Arrival time convenient',\n",
    "       'Food and drink', 'Gate location', 'Inflight wifi service',\n",
    "       'Inflight entertainment', 'Online support', 'Ease of Online booking',\n",
    "       'On-board service', 'Leg room service', 'Baggage handling',\n",
    "       'Checkin service', 'Cleanliness', 'Online boarding',\n",
    "       'Departure Delay in Minutes', 'Arrival Delay in Minutes', 'class_Eco',\n",
    "       'class_Eco Plus']\n",
    "\n",
    "X=iv[feature_cols]\n",
    "y=iv['satisfaction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21a4a337ec8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "str_list = [] # empty list to contain columns with strings (words)\n",
    "for colname, colvalue in iv.iteritems():\n",
    "    if type(colvalue[1]) == str:\n",
    "         str_list.append(colname)\n",
    "# Get to the numeric columns by inversion            \n",
    "num_list = iv.columns.difference(str_list) \n",
    "# Create Dataframe containing only numerical features\n",
    "house_num = iv[num_list]\n",
    "f, ax = plt.subplots(figsize=(16, 12))\n",
    "plt.title('Pearson Correlation of features')\n",
    "# Draw the heatmap using seaborn\n",
    "#sns.heatmap(house_num.astype(float).corr(),linewidths=0.25,vmax=1.0, square=True, cmap=\"PuBuGn\", linecolor='k', annot=True)\n",
    "sns.heatmap(house_num.astype(float).corr(),linewidths=0.25,vmax=1.0, square=True, cmap=\"cubehelix\", linecolor='k', annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***********   Top Factors Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methods Used\n",
    "\n",
    "### Recursive Factor Exclusion , Linear Regression , Ridge , Lasso , Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE, f_regression\n",
    "from sklearn.linear_model import (LinearRegression, Ridge, Lasso)\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define dictionary to store our rankings\n",
    "ranks = {}\n",
    "# Create our function which stores the feature rankings to the ranks dictionary\n",
    "def ranking(ranks, names, order=1):\n",
    "    minmax = MinMaxScaler()\n",
    "    ranks = minmax.fit_transform(order*np.array([ranks]).T).T[0]\n",
    "    ranks = map(lambda x: round(x,2), ranks)\n",
    "    return dict(zip(names, ranks))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 23 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n"
     ]
    }
   ],
   "source": [
    "# Construct our Linear Regression model\n",
    "lr = LinearRegression(normalize=True)\n",
    "lr.fit(X,y)\n",
    "#stop the search when only the last feature is left\n",
    "rfe = RFE(lr, n_features_to_select=1, verbose =3 )\n",
    "rfe.fit(X,y)\n",
    "ranks[\"RFE\"] = ranking(list(map(float, rfe.ranking_)), feature_cols, order=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Linear Regression\n",
    "lr = LinearRegression(normalize=True)\n",
    "lr.fit(X,y)\n",
    "ranks[\"LinReg\"] = ranking(np.abs(lr.coef_), feature_cols)\n",
    "\n",
    "# Using Ridge \n",
    "ridge = Ridge(alpha = 7)\n",
    "ridge.fit(X,y)\n",
    "ranks['Ridge'] = ranking(np.abs(ridge.coef_), feature_cols)\n",
    "\n",
    "# Using Lasso\n",
    "lasso = Lasso(alpha=.05)\n",
    "lasso.fit(X, y)\n",
    "ranks[\"Lasso\"] = ranking(np.abs(lasso.coef_), feature_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 50building tree 2 of 50\n",
      "building tree 3 of 50\n",
      "building tree 4 of 50\n",
      "\n",
      "building tree 5 of 50\n",
      "building tree 6 of 50\n",
      "building tree 7 of 50\n",
      "building tree 8 of 50\n",
      "building tree 9 of 50\n",
      "building tree 10 of 50\n",
      "building tree 11 of 50\n",
      "building tree 12 of 50\n",
      "building tree 13 of 50building tree 14 of 50\n",
      "\n",
      "building tree 15 of 50\n",
      "building tree 16 of 50\n",
      "building tree 17 of 50\n",
      "building tree 18 of 50\n",
      "building tree 19 of 50\n",
      "building tree 20 of 50\n",
      "building tree 21 of 50\n",
      "building tree 22 of 50\n",
      "building tree 23 of 50\n",
      "building tree 24 of 50\n",
      "building tree 25 of 50\n",
      "building tree 26 of 50\n",
      "building tree 27 of 50\n",
      "building tree 28 of 50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:   13.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 29 of 50\n",
      "building tree 30 of 50\n",
      "building tree 31 of 50\n",
      "building tree 32 of 50\n",
      "building tree 33 of 50\n",
      "building tree 34 of 50\n",
      "building tree 35 of 50\n",
      "building tree 36 of 50\n",
      "building tree 37 of 50\n",
      "building tree 38 of 50\n",
      "building tree 39 of 50\n",
      "building tree 40 of 50\n",
      "building tree 41 of 50\n",
      "building tree 42 of 50building tree 43 of 50\n",
      "\n",
      "building tree 44 of 50\n",
      "building tree 45 of 50\n",
      "building tree 46 of 50\n",
      "building tree 47 of 50\n",
      "building tree 48 of 50\n",
      "building tree 49 of 50building tree 50 of 50\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:   26.4s finished\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_jobs=-1, n_estimators=50, verbose=3)\n",
    "rf.fit(X,y)\n",
    "ranks[\"RF\"] = ranking(rf.feature_importances_, feature_cols);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t                Lasso\tLinReg\tRF\tRFE\tRidge\tMean\n",
      "Gender        \t       0.0\t0.46\t0.05\t0.86\t0.46\t0.37\n",
      "Customer Type        \t       0.0\t1.0\t0.05\t1.0\t1.0\t0.61\n",
      "Age        \t       0.01\t0.0\t0.06\t0.14\t0.0\t0.04\n",
      "Type of Travel        \t       0.0\t0.36\t0.06\t0.73\t0.36\t0.3\n",
      "Flight Distance        \t       0.0\t0.0\t0.1\t0.0\t0.0\t0.02\n",
      "Seat comfort        \t       0.0\t0.17\t0.45\t0.59\t0.17\t0.28\n",
      "Departure/Arrival time convenient        \t       0.0\t0.1\t0.06\t0.45\t0.1\t0.14\n",
      "Food and drink        \t       0.0\t0.11\t0.02\t0.55\t0.11\t0.16\n",
      "Gate location        \t       0.0\t0.05\t0.04\t0.32\t0.05\t0.09\n",
      "Inflight wifi service        \t       0.0\t0.03\t0.02\t0.18\t0.03\t0.05\n",
      "Inflight entertainment        \t       1.0\t0.34\t1.0\t0.82\t0.34\t0.7\n",
      "Online support        \t       0.08\t0.05\t0.05\t0.27\t0.05\t0.1\n",
      "Ease of Online booking        \t       0.45\t0.12\t0.14\t0.77\t0.12\t0.32\n",
      "On-board service        \t       0.3\t0.14\t0.04\t0.68\t0.14\t0.26\n",
      "Leg room service        \t       0.19\t0.1\t0.04\t0.5\t0.1\t0.19\n",
      "Baggage handling        \t       0.0\t0.05\t0.05\t0.23\t0.05\t0.08\n",
      "Checkin service        \t       0.14\t0.13\t0.05\t0.64\t0.13\t0.22\n",
      "Cleanliness        \t       0.0\t0.04\t0.04\t0.36\t0.04\t0.1\n",
      "Online boarding        \t       0.0\t0.07\t0.03\t0.41\t0.07\t0.12\n",
      "Departure Delay in Minutes        \t       0.0\t0.0\t0.02\t0.05\t0.0\t0.01\n",
      "Arrival Delay in Minutes        \t       0.01\t0.0\t0.03\t0.09\t0.0\t0.03\n",
      "class_Eco        \t       0.0\t0.36\t0.02\t0.91\t0.36\t0.33\n",
      "class_Eco Plus        \t       0.0\t0.39\t0.0\t0.95\t0.39\t0.35\n"
     ]
    }
   ],
   "source": [
    "# Create empty dictionary to store the mean value calculated from all the scores\n",
    "r = {}\n",
    "for name in feature_cols:\n",
    "    r[name] = round(np.mean([ranks[method][name] \n",
    "                             for method in ranks.keys()]), 2)\n",
    " \n",
    "methods = sorted(ranks.keys())\n",
    "ranks[\"Mean\"] = r\n",
    "methods.append(\"Mean\")\n",
    " \n",
    "print(\"\\t                %s\" % \"\\t\".join(methods))\n",
    "for name in feature_cols:\n",
    "    print(\"%s        \\t       %s\" % (name, \"\\t\".join(map(str, \n",
    "                         [ranks[method][name] for method in methods]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put the mean scores into a Pandas dataframe\n",
    "meanplot = pd.DataFrame(list(r.items()), columns= ['Feature','Mean Ranking'])\n",
    "\n",
    "# Sort the dataframe\n",
    "meanplot = meanplot.sort_values('Mean Ranking', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x21a4b135608>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 2325.6x1224 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot the ranking of the features\n",
    "sns.factorplot(x=\"Mean Ranking\", y=\"Feature\", data = meanplot, kind=\"bar\", \n",
    "               size=17, aspect=1.9, palette='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Factors Exclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Customer Type', 'Age', 'Type of Travel', 'Flight Distance',\n",
       "       'Seat comfort', 'Departure/Arrival time convenient', 'Food and drink',\n",
       "       'Gate location', 'Inflight wifi service', 'Inflight entertainment',\n",
       "       'Online support', 'Ease of Online booking', 'On-board service',\n",
       "       'Leg room service', 'Baggage handling', 'Checkin service',\n",
       "       'Cleanliness', 'Online boarding', 'Departure Delay in Minutes',\n",
       "       'Arrival Delay in Minutes', 'class_Eco', 'class_Eco Plus'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols=['Gender', 'Customer Type', 'Age', 'Type of Travel',\n",
    "       'Flight Distance', 'Seat comfort', 'Departure/Arrival time convenient',\n",
    "       'Food and drink', 'Gate location', 'Inflight wifi service',\n",
    "       'Inflight entertainment', 'Online support', 'Ease of Online booking',\n",
    "       'On-board service', 'Leg room service', 'Baggage handling',\n",
    "       'Checkin service', 'Cleanliness', 'Online boarding',\n",
    "       'Departure Delay in Minutes', 'Arrival Delay in Minutes', 'class_Eco',\n",
    "       'class_Eco Plus']\n",
    "\n",
    "dep_col=['satisfaction']\n",
    "\n",
    "X1=iv[feature_cols]\n",
    "y1=iv[dep_col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True False  True False  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True False  True  True  True]\n",
      "[1 1 2 1 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "iv_vars=iv.columns.values.tolist()\n",
    "y=['satisfaction']\n",
    "X=[i for i in iv_vars if i not in y]\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "logreg = LogisticRegression(random_state=1, solver='newton-cg',\n",
    "                         multi_class='multinomial',max_iter=1200)\n",
    "rfe = RFE(logreg,20)\n",
    "rfe = rfe.fit(X1, y1.values.ravel())\n",
    "print(rfe.support_)\n",
    "print(rfe.ranking_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols=['Gender', 'Customer Type', 'Age', 'Type of Travel',\n",
    "       'Flight Distance', 'Seat comfort', 'Departure/Arrival time convenient',\n",
    "       'Food and drink', 'Gate location', 'Inflight wifi service',\n",
    "       'Inflight entertainment', 'Online support', 'Ease of Online booking',\n",
    "       'On-board service', 'Leg room service', 'Baggage handling',\n",
    "       'Checkin service', 'Cleanliness', 'Online boarding',\n",
    "       'Departure Delay in Minutes', 'Arrival Delay in Minutes', 'class_Eco',\n",
    "       'class_Eco Plus']\n",
    "\n",
    "X=iv[feature_cols]\n",
    "y=iv['satisfaction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.444192\n",
      "         Iterations 6\n",
      "                                  Results: Logit\n",
      "===================================================================================\n",
      "Model:                     Logit                 Pseudo R-squared:      0.355      \n",
      "Dependent Variable:        satisfaction          AIC:                   115080.2256\n",
      "Date:                      2020-03-08 10:24      BIC:                   115304.9663\n",
      "No. Observations:          129487                Log-Likelihood:        -57517.    \n",
      "Df Model:                  22                    LL-Null:               -89171.    \n",
      "Df Residuals:              129464                LLR p-value:           0.0000     \n",
      "Converged:                 1.0000                Scale:                 1.0000     \n",
      "No. Iterations:            6.0000                                                  \n",
      "-----------------------------------------------------------------------------------\n",
      "                                   Coef.  Std.Err.     z     P>|z|   [0.025  0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Gender                             0.6299   0.0149   42.2235 0.0000  0.6007  0.6592\n",
      "Customer Type                      0.9012   0.0214   42.0435 0.0000  0.8592  0.9433\n",
      "Age                               -0.0269   0.0005  -51.7959 0.0000 -0.0279 -0.0258\n",
      "Type of Travel                    -0.6687   0.0193  -34.5801 0.0000 -0.7066 -0.6308\n",
      "Flight Distance                   -0.0004   0.0000  -55.3733 0.0000 -0.0004 -0.0004\n",
      "Seat comfort                       0.3345   0.0086   38.7425 0.0000  0.3176  0.3514\n",
      "Departure/Arrival time convenient -0.2012   0.0064  -31.6683 0.0000 -0.2137 -0.1887\n",
      "Food and drink                    -0.2739   0.0087  -31.4007 0.0000 -0.2910 -0.2568\n",
      "Gate location                     -0.0564   0.0070   -8.0592 0.0000 -0.0701 -0.0427\n",
      "Inflight wifi service             -0.2716   0.0082  -33.2663 0.0000 -0.2876 -0.2556\n",
      "Inflight entertainment             0.5863   0.0077   76.3063 0.0000  0.5712  0.6013\n",
      "Online support                    -0.0172   0.0084   -2.0423 0.0411 -0.0338 -0.0007\n",
      "Ease of Online booking             0.5640   0.0107   52.7321 0.0000  0.5431  0.5850\n",
      "On-board service                   0.1715   0.0075   22.8527 0.0000  0.1568  0.1862\n",
      "Leg room service                   0.0975   0.0065   15.0781 0.0000  0.0848  0.1101\n",
      "Baggage handling                  -0.1087   0.0084  -12.8759 0.0000 -0.1252 -0.0921\n",
      "Checkin service                    0.1298   0.0063   20.6822 0.0000  0.1175  0.1421\n",
      "Cleanliness                       -0.1658   0.0087  -19.0591 0.0000 -0.1828 -0.1487\n",
      "Online boarding                    0.0126   0.0093    1.3644 0.1725 -0.0055  0.0308\n",
      "Departure Delay in Minutes         0.0036   0.0007    4.8379 0.0000  0.0021  0.0050\n",
      "Arrival Delay in Minutes          -0.0085   0.0007  -11.6097 0.0000 -0.0099 -0.0070\n",
      "class_Eco                         -2.0276   0.0177 -114.6567 0.0000 -2.0623 -1.9930\n",
      "class_Eco Plus                    -1.9133   0.0298  -64.2996 0.0000 -1.9716 -1.8550\n",
      "===================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "logit_model=sm.Logit(y,X)\n",
    "result=logit_model.fit()\n",
    "print(result.summary2())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arrival delay , Departure delay , Flight distance is removed from features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols=['Gender', 'Customer Type', 'Age', 'Type of Travel', 'Seat comfort', 'Departure/Arrival time convenient',\n",
    "       'Food and drink', 'Gate location', 'Inflight wifi service',\n",
    "       'Inflight entertainment', 'Online support', 'Ease of Online booking',\n",
    "       'On-board service', 'Leg room service', 'Baggage handling',\n",
    "       'Checkin service', 'Cleanliness',\n",
    "        'class_Eco',\n",
    "       'class_Eco Plus']\n",
    "\n",
    "X=iv[feature_cols]\n",
    "y=iv['satisfaction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.461213\n",
      "         Iterations 6\n",
      "                                  Results: Logit\n",
      "===================================================================================\n",
      "Model:                     Logit                 Pseudo R-squared:      0.330      \n",
      "Dependent Variable:        satisfaction          AIC:                   119480.1435\n",
      "Date:                      2020-03-08 10:25      BIC:                   119665.7989\n",
      "No. Observations:          129487                Log-Likelihood:        -59721.    \n",
      "Df Model:                  18                    LL-Null:               -89171.    \n",
      "Df Residuals:              129468                LLR p-value:           0.0000     \n",
      "Converged:                 1.0000                Scale:                 1.0000     \n",
      "No. Iterations:            6.0000                                                  \n",
      "-----------------------------------------------------------------------------------\n",
      "                                   Coef.  Std.Err.     z     P>|z|   [0.025  0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Gender                             0.6876   0.0146   47.1149 0.0000  0.6590  0.7162\n",
      "Customer Type                      0.5964   0.0204   29.1762 0.0000  0.5564  0.6365\n",
      "Age                               -0.0203   0.0005  -41.3343 0.0000 -0.0213 -0.0193\n",
      "Type of Travel                    -1.0235   0.0182  -56.2460 0.0000 -1.0591 -0.9878\n",
      "Seat comfort                       0.3689   0.0085   43.4572 0.0000  0.3523  0.3855\n",
      "Departure/Arrival time convenient -0.2156   0.0062  -34.5784 0.0000 -0.2279 -0.2034\n",
      "Food and drink                    -0.2844   0.0086  -33.2008 0.0000 -0.3012 -0.2676\n",
      "Gate location                     -0.0818   0.0068  -11.9691 0.0000 -0.0952 -0.0684\n",
      "Inflight wifi service             -0.2990   0.0077  -39.0346 0.0000 -0.3140 -0.2840\n",
      "Inflight entertainment             0.5627   0.0074   75.6838 0.0000  0.5481  0.5772\n",
      "Online support                    -0.0248   0.0079   -3.1499 0.0016 -0.0402 -0.0094\n",
      "Ease of Online booking             0.5970   0.0096   62.1205 0.0000  0.5782  0.6159\n",
      "On-board service                   0.1700   0.0074   23.0058 0.0000  0.1555  0.1845\n",
      "Leg room service                   0.0726   0.0064   11.4081 0.0000  0.0601  0.0851\n",
      "Baggage handling                  -0.1658   0.0083  -19.9861 0.0000 -0.1821 -0.1495\n",
      "Checkin service                    0.0958   0.0061   15.7812 0.0000  0.0839  0.1077\n",
      "Cleanliness                       -0.1935   0.0085  -22.6680 0.0000 -0.2102 -0.1767\n",
      "class_Eco                         -2.0799   0.0173 -120.1095 0.0000 -2.1138 -2.0460\n",
      "class_Eco Plus                    -1.9117   0.0292  -65.5173 0.0000 -1.9689 -1.8545\n",
      "===================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "logit_model=sm.Logit(y,X)\n",
    "result=logit_model.fit()\n",
    "print(result.summary2())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### split x and y into training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "X= preprocessing.StandardScaler().fit(X).transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiate the model (using the default parameters)\n",
    "logreg = LogisticRegression(random_state=1, solver='lbfgs',\n",
    "                         multi_class='multinomial',max_iter=1200000)\n",
    "\n",
    "# fit the model with data\n",
    "logreg.fit(x_train,y_train)\n",
    "\n",
    "#\n",
    "y_pred=logreg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.23619541  0.36389779 -0.04267602  0.16396101  0.19549628 -0.15231677\n",
      "  -0.15520323  0.07460673 -0.01718462  0.47263663  0.08598539  0.19253204\n",
      "   0.18646477  0.14167471  0.04371847  0.19232384  0.04880537 -0.17359003\n",
      "  -0.09624875]]\n",
      "[0.17352499]\n"
     ]
    }
   ],
   "source": [
    "print(logreg.coef_)\n",
    "print(logreg.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12052,  2680],\n",
       "       [ 2671, 14969]], dtype=int64)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
    "cnf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 257.44, 'Predicted label')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_names=[0,1] # name  of classes\n",
    "fig, ax = plt.subplots()\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "# create heatmap\n",
    "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"viridis\" ,fmt='g')\n",
    "ax.xaxis.set_label_position(\"top\")\n",
    "plt.tight_layout()\n",
    "plt.title('Confusion matrix', y=1.1)\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.82      0.82     14732\n",
      "           1       0.85      0.85      0.85     17640\n",
      "\n",
      "    accuracy                           0.83     32372\n",
      "   macro avg       0.83      0.83      0.83     32372\n",
      "weighted avg       0.83      0.83      0.83     32372\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuracy & precision "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8347028296058322\n",
      "Precision: 0.8481500368292821\n",
      "Recall: 0.8485827664399093\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
    "print(\"Precision:\",metrics.precision_score(y_test, y_pred))\n",
    "print(\"Recall:\",metrics.recall_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "logit_roc_auc = roc_auc_score(y_test, logreg.predict(x_test))\n",
    "fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(x_test)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area under curve :  0.9081325059890912\n"
     ]
    }
   ],
   "source": [
    "print(\"Area under curve : \", logit_roc_auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Neighbour Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import jaccard_similarity_score\n",
    "from sklearn.metrics import jaccard_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainScores={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best K is : 7 | Cross validation Accuracy : 0.9243679815786614\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "bestScore=0.0\n",
    "accList=[]\n",
    "\n",
    "for k in range(3,12):\n",
    "    \n",
    "    clf_knn = KNeighborsClassifier(n_neighbors=k,algorithm='auto')\n",
    "    \n",
    "    # using 10 fold cross validation for scoring the classifier's accuracy\n",
    "    scores = cross_val_score(clf_knn, x_train, y_train, cv=10)\n",
    "    score=scores.mean()\n",
    "    accList.append(score)\n",
    "    \n",
    "    if score > bestScore:\n",
    "        bestScore=score\n",
    "        best_clf=clf_knn\n",
    "        bestK=k\n",
    "        \n",
    "print(\"Best K is :\",bestK,\"| Cross validation Accuracy :\",bestScore)\n",
    "clf_knn=best_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(3,12),accList)\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('CV Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_knn.fit(x_train,y_train)\n",
    "y_pred=best_clf.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainScores['KNN-jaccard']=jaccard_score(y_train, y_pred)\n",
    "trainScores['KNN-f1-score']=f1_score(y_train, y_pred, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'SVM-jaccard': 0.7370052533690084,\n",
       " 'SVM-f1-score': 0.8340138822294998,\n",
       " 'LogReg-jaccard': 0.7366851854868929,\n",
       " 'LogReg-f1-score': 0.8335598291866103,\n",
       " 'LogReg-logLoss': 0.3902171722607835,\n",
       " 'KNN-jaccard': 0.8994998550304436,\n",
       " 'KNN-f1-score': 0.9429704985724753}"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainScores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "\n",
    "clf_tree = tree.DecisionTreeClassifier()\n",
    "clf_tree = clf_tree.fit(x_train, y_train)\n",
    "\n",
    "y_pred=clf_tree.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainScores['Tree-jaccard']=jaccard_score(y_train, y_pred)\n",
    "trainScores['Tree-f1-score']=f1_score(y_train, y_pred, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'SVM-jaccard': 0.7370052533690084,\n",
       " 'SVM-f1-score': 0.8340138822294998,\n",
       " 'LogReg-jaccard': 0.7366851854868929,\n",
       " 'LogReg-f1-score': 0.8335598291866103,\n",
       " 'LogReg-logLoss': 0.3902171722607835,\n",
       " 'KNN-jaccard': 0.8994998550304436,\n",
       " 'KNN-f1-score': 0.9429704985724753,\n",
       " 'Tree-jaccard': 0.9998497455064516,\n",
       " 'Tree-f1-score': 0.9999176239314149}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainScores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  *******   Decision tree graph (Use if needed, heavy lag)  ***************"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "!pip install graphviz\n",
    "!pip install pydotplus\n",
    "import graphviz \n",
    "import pydotplus\n",
    "\n",
    "dot_data = tree.export_graphviz(clf_tree, out_file=None, \n",
    "                     feature_names=['Gender', 'Customer Type', 'Age', 'Type of Travel', 'Flight Distance',\n",
    "       'Seat comfort', 'Departure/Arrival time convenient', 'Food and drink',\n",
    "       'Gate location', 'Inflight wifi service', 'Inflight entertainment',\n",
    "       'Online support', 'Ease of Online booking', 'On-board service',\n",
    "       'Leg room service', 'Baggage handling', 'Checkin service',\n",
    "       'Cleanliness', 'Online boarding', 'Departure Delay in Minutes',\n",
    "       'Arrival Delay in Minutes', 'class_Eco', 'class_Eco Plus'],  \n",
    "                     class_names='satisfaction',  \n",
    "                     filled=True, rounded=True,  \n",
    "                     special_characters=True) \n",
    "\n",
    "graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "graph.set_size('\"16,16!\"')\n",
    "gvz_graph = graphviz.Source(graph.to_string())\n",
    "\n",
    "gvz_graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=y_train.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "clf_svm = svm.LinearSVC(random_state=7,max_iter=120000)\n",
    "clf_svm.fit(x_train, y_train)  \n",
    "\n",
    "y_pred=clf_svm.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainScores['SVM-jaccard']=jaccard_score(y_train, y_pred)\n",
    "trainScores['SVM-f1-score']=f1_score(y_train, y_pred, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'SVM-jaccard': 0.7370052533690084, 'SVM-f1-score': 0.8340138822294998}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainScores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LogReg 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "clf_log = LogisticRegression(random_state=1, solver='newton-cg',\n",
    "                         multi_class='multinomial')\n",
    "clf_log.fit(x_train, y_train)\n",
    "\n",
    "y_pred=clf_log.predict(x_train)\n",
    "y_proba=clf_log.predict_proba(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainScores['LogReg-jaccard']=jaccard_score(y_train, y_pred)\n",
    "trainScores['LogReg-f1-score']=f1_score(y_train, y_pred, average='weighted')  \n",
    "trainScores['LogReg-logLoss']=log_loss(y_train, y_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'SVM-jaccard': 0.7370052533690084,\n",
       " 'SVM-f1-score': 0.8340138822294998,\n",
       " 'LogReg-jaccard': 0.7366851854868929,\n",
       " 'LogReg-f1-score': 0.8335598291866103,\n",
       " 'LogReg-logLoss': 0.3902171722607835}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainScores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "testScores={}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Knn test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_pred=clf_knn.predict(x_test)\n",
    "testScores['KNN-jaccard']=jaccard_score(y_test, knn_pred)\n",
    "testScores['KNN-f1-score']=f1_score(y_test, knn_pred, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_pred=clf_tree.predict(x_test)\n",
    "testScores['Tree-jaccard']=jaccard_score(y_test, tree_pred)\n",
    "testScores['Tree-f1-score']=f1_score(y_test, tree_pred, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_pred=clf_svm.predict(x_test)\n",
    "testScores['SVM-jaccard']=jaccard_score(y_test, svm_pred)\n",
    "testScores['SVM-f1-score']=f1_score(y_test, svm_pred, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LogReg Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_pred=clf_log.predict(x_test)\n",
    "proba=clf_log.predict_proba(x_test)\n",
    "testScores['LogReg-jaccard']=jaccard_score(y_test, log_pred)\n",
    "testScores['LogReg-f1-score']=f1_score(y_test, log_pred, average='weighted')  \n",
    "testScores['LogReg-logLoss']=log_loss(y_test, proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'SVM-jaccard': 0.7370755507367799,\n",
       " 'SVM-f1-score': 0.8352121228205367,\n",
       " 'LogReg-jaccard': 0.7366633858267716,\n",
       " 'LogReg-f1-score': 0.8346986547067784,\n",
       " 'LogReg-logLoss': 0.38730160621027276,\n",
       " 'KNN-jaccard': 0.8721535458685752,\n",
       " 'KNN-f1-score': 0.9272722744156864,\n",
       " 'Tree-jaccard': 0.892742281303602,\n",
       " 'Tree-f1-score': 0.9381819036875686}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testScores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Scores Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>Algorithm</th>\n",
       "      <th>Jaccard</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>LogLoss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>KNN</td>\n",
       "      <td>0.872154</td>\n",
       "      <td>0.927272</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.892742</td>\n",
       "      <td>0.938182</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>SVM</td>\n",
       "      <td>0.737076</td>\n",
       "      <td>0.835212</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.736663</td>\n",
       "      <td>0.834699</td>\n",
       "      <td>0.387302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Algorithm   Jaccard  F1-score   LogLoss\n",
       "0                 KNN  0.872154  0.927272        NA\n",
       "1       Decision Tree  0.892742  0.938182        NA\n",
       "2                 SVM  0.737076  0.835212        NA\n",
       "3  LogisticRegression  0.736663  0.834699  0.387302"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jaccard = [0.8721535458685752,0.892742281303602,0.7370755507367799,0.7366633858267716]\n",
    "F1_score = [0.9272722744156864,0.9381819036875686,0.8352121228205367,0.8346986547067784]\n",
    "LogLoss = ['NA','NA','NA',0.38730160621027276]\n",
    "\n",
    "    \n",
    "df = {'Algorithm': ['KNN', 'Decision Tree', 'SVM', 'LogisticRegression'], \\\n",
    "     'Jaccard': Jaccard, 'F1-score': F1_score, 'LogLoss': LogLoss}\n",
    "\n",
    "Report = pd.DataFrame(data=df, columns=['Algorithm', 'Jaccard', 'F1-score', 'LogLoss'], index=None)\n",
    "Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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