Airline Customer Satisfaction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "362d16af",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "055cd901",
   "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>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>Gate location</th>\n",
       "      <th>...</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",
       "      <th>satisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>2</td>\n",
       "      <td>...</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",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>3</td>\n",
       "      <td>...</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",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>3</td>\n",
       "      <td>...</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",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>3</td>\n",
       "      <td>...</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",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>3</td>\n",
       "      <td>...</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",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender   Customer Type  Age   Type of Travel     Class  Flight Distance  \\\n",
       "0  Female  Loyal Customer   65  Personal Travel       Eco              265   \n",
       "1    Male  Loyal Customer   47  Personal Travel  Business             2464   \n",
       "2  Female  Loyal Customer   15  Personal Travel       Eco             2138   \n",
       "3  Female  Loyal Customer   60  Personal Travel       Eco              623   \n",
       "4  Female  Loyal Customer   70  Personal Travel       Eco              354   \n",
       "\n",
       "   Seat comfort  Departure/Arrival time convenient  Food and drink  \\\n",
       "0             0                                  0               0   \n",
       "1             0                                  0               0   \n",
       "2             0                                  0               0   \n",
       "3             0                                  0               0   \n",
       "4             0                                  0               0   \n",
       "\n",
       "   Gate location  ...  Ease of Online booking  On-board service  \\\n",
       "0              2  ...                       3                 3   \n",
       "1              3  ...                       3                 4   \n",
       "2              3  ...                       2                 3   \n",
       "3              3  ...                       1                 1   \n",
       "4              3  ...                       2                 2   \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",
       "   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",
       "   satisfaction  \n",
       "0     satisfied  \n",
       "1     satisfied  \n",
       "2     satisfied  \n",
       "3     satisfied  \n",
       "4     satisfied  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Invistico_Airline.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "90a3cc24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(129880, 23)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b171565",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "be04f28c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Business travel    89693\n",
       "Personal Travel    40187\n",
       "Name: Type of Travel, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Type of Travel'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a3a431d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['satisfaction_numb'] = df['satisfaction'].map({'satisfied' : 1, 'dissatisfied' : 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "78524b66",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_unique_col_values(dataframe):\n",
    "    for columns in df:\n",
    "        if df[columns].dtypes == 'object':\n",
    "            print(f'{columns} : {df[columns].unique()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c9a418e9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender : ['Female' 'Male']\n",
      "Customer Type : ['Loyal Customer' 'disloyal Customer']\n",
      "Type of Travel : ['Personal Travel' 'Business travel']\n",
      "Class : ['Eco' 'Business' 'Eco Plus']\n",
      "satisfaction : ['satisfied' 'dissatisfied']\n"
     ]
    }
   ],
   "source": [
    "print_unique_col_values(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f886758f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Customers')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAFoCAYAAABqqe1MAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAArkklEQVR4nO3deZhcVZnH8e+PAGExYQ2ICRCQoAIiIxFBUBBGFllF0KhoUEZcUBGXERwEB2QGRhFXUBRMYByQTUAWAQMBUQyE3bBIgAARhEAiCUsCSd7545yibypV1ZXuun3T3b/P89RTt87d3nv7VL91zrm3ShGBmZlZp61QdQBmZjYwOcGYmVkpnGDMzKwUTjBmZlYKJxgzMyuFE4yZmZXCCabDJB0qKfLjZUmvL8zbpTDv2Apim1Dbf1/vu6ckfVPSI5JeybFPaLHsjOX1+CR9u/C3H103T5L+IenK/HpyYdnFkl7Mx3aFpIMlrdDutruJqVhXd+n9UfZc3Xvj0DbX2VvS5fncLZD0hKSrJX2kxDi/nR8HlLWPgWTFqgMY4FYBvgL8e9WB9EeS9gZOqjqOPrAdsD5wRYN5AlYDNs6PvYFLJX04Il7puxCXH5IEnAn8W92sUfmxA3BeSbs/Pj9PBC4taR8Dhlsw5fuspLWqDqKvSVqlA5vZpjC9c0QoIg7twHaXN/vk5ysbzNsEWBV4D3BbLjsAOKW2QER8O58bRcSMEuNcXnyFruQyHXgf6RyNAD4K/LWiuJZ7koZIWqmv9ucEU65FwDDgi60WKnQNTCiULdV9UdeN8HlJv5Q0T9Ljefkhkr4jaZakpyR9V1LDVqqkN0u6PnfjzZD0+QbLfFTSLZJeyMvdKunDdctMKMQ0Ni+/APhsi+MdIumrku7J250n6SZJ+xWWmQx8p7DajcvSfdKKpK0kXSjpmdz19pikH0taJ8/fSNKiRl2Zkv6cy+/Kr98l6cr8N3gpH880Scc0O/cN7APcExGPN5oZEfMj4o/AXsDzufjzktbPMSzVRSZpdUmnSno4x/RPSffmOrNqN+dnHUk/zPXilVyfLpK0VZ4vSX/L+/tD3br/lctflTQiP87Ly8/N23tc0s8lrdvm+SlufyXgmPxyMbB/RPwhn6NnI+I8YOfC8u2+t4ZI+pak+5W6JOdJekDSuZLeUHvvFUIZr7ouvXbqdV6u1gU6Q9Ieku7LdecKSetJ2ia/j16UNEXStnXrD5V0XI51fv7bXtlguVqX8WRJh0j6G7AA2DLv5xe57s+X9Jyk2yWdtqx/k5Yiwo8OPoBDgciP8/Lzs8DqwC6FeccW1qmVTWiynV1yWXH95wrTQXqzXV5XFsBnC9ucUCh/qsGyHy0se0KD+bXH15psc3Zh+sstztFvWmz7c3mZyU3mH9piuzNqy7VYZlvgxSbbfhBYIy9XO5fTAeWyDfJ5DuCrueyzLY7llMJ+v10oH10oH5nLTiqUTW60bJ53emHeuGbbBs5oEde6LerYGvk8NFrvRWDbvNxX6Kp3xeO5P5f/Lr9+c4s4phTW26XNv/EOheV+38b7sd331jdaxDm2Lr6GdZI26nXd3/cFYH7dcjeT/l8Uyx4HVsrrrghc32Qf84EdG7wf5tBVb4PUM3B1k2280Mn/h27BlOtK4G5gHeAzHd72S8AYYN/8WqT++T1J3Sov5PKDmqw/GVib1PXyYi47EUDSJsA3c9lP83Jr0dWvfYIad/vdB2yal7+40U7zJ8YP5ZfXA68H3gY8mctOkTQsInYB/rOw6iaRuoAmNDmedp1KGtNYDHyQ9A+11t20OXBUnj4jP7+Rrk/EB5LO8yLg/3LZzaRzOAJYKR9PravrM6obkG+g1j3WaPylkb8VpjdqsdxO+flC4HWkv992pA8OC1qsdxTpPEA6L2uQjnsx6bydmudNIP1DE+kfNpK2JCUUgHPy8zOkLr2RwNC8vW/nedtJenuLWBopHvMDy7huK7Xz9WfSuRpGqpdHA7MjYnJEqLD8xOjqlpzQbr2u2+fqpHO8JqkeAewI3Ep6D/0gl20IvDNPfwR4b54eT+oa3ISU2IfS9fcpWpP0wWQdYDTwSOF4T6Ore/E9+XXHOMGU77/z81eBlTu43V9FxHSg2EXxp4i4JlI//L25bMMm6x8bEXMidb1ckss2Vbrq7X3AkFx2BKllModUuSFVyO0bbPOoiHg0b/eJJvvdozB9QkQ8HRH3kAZtIb2xd2iybq9IWg14d355U0RcEhFzSQO383P57vn5GuDRPP3J/Hxgfv5DRDyVp2cCHyaNj7wE/IOU6CH9M12vm7D2IX1indLuYbS53GP5eUfg2LyfFyPi+IiY12K92t9nPnB8RMyNiN8CN+XynSStGhGzSZ/YAQ6VJLrOz/PA7/L0HFLCuhr4Z5737cL+Nmf5UDtfWwDHAQeT3gP/ExGPtLF+T+r1K8DJEfE88KdC+fciYg7pnNXU3sd7FcomAi+T6ulbctl2uZ4XzSG1uGdHxGO5zteO9/2kLsddgb9HxLfaONa2OcGU70LgIeAN5E96bRrSzfzHASJifn1ZVrvCaGiT9YsJ4O+F6ZGkTzPdWbtB2b0NyuoV+92LMcwsTLez/55Yi646/9q+I2IB6Z/8a/uOiMXAz3PZQblVV2vJ1D6d16aPIH0ybDR42vRiB6ULIXYFrsr7a8eYwvRjTZeCr5H+Hm8gfQo/F5imNI62Zov1an+fWfm81NT+PkPo+tvXWnkbk47jg/n1BYV6eRTwP8DWpA8m9Zb1YpDiMb9pGdetafTeOpHUiliTFPPZwB3AA2rv0u+e1OtZEfFynm70Pi5eJVh7H3f33hCpnhf9re5vCalH5VHSOTyO9GHh4TwO1LGLAJxgSpb/cdS6YD7UZLFaRSq+2UZ3s+mFbZY1M6owPbIw/Xe6/tkCHFDoClDuJlghIn5dv8G6ZNdMcdujmkwXl+mkWl/0EvuTNJSufxDFfZ9N+tusRvoHPQSYR748NQ+W11orfwDWz+enUTdFI7vlbbfVPZYHxT+WX74C3NBs2Yh4ICK2JnXx7UvqblwEvIOUEJt5LdHm81JTO1+LSeeRiJgC3JnL/5PUJQTpXNUcnJ//QfqUvQJd3bo9cXshxt0lvaV+AUnFBNLWeyu3ON5NOs69SLcWvEBqYf1HG3H1pF43e7+2eh/XtrEYWLvJe/Pvdess9b6MiD9FxKakFtuBwA/zrL1p/n9qmTnB9I1zSJ9kmrVKap9ydpA0TNIolq210xMnSlpL0rvp6tp4OCL+AVxH1z/i70jaWtLKkkZLOgq4sRf7vbYw/a18NctWdF12+gKpH7xXJO1Z99g5Il6iq697Z0kH5H7x4+n6B/RafBExC7gov9wxP1+ctwOpxVJ7Dy0AXpY0Fvh4m2HuA7xK6o5rdSxDJe1EGtsZnot/GhHPtFjn3yV9oLD9C+j6R9PqU3Dt+FcBjpc0XNL+pP55gJsLxw9drZja+XmUrnMMXd3Ci0jJeSRpQL1HIuJVurqdVyDdE7RrPkfrKN1keVNhlbbeW5IOl/SxHO8NpE/0s/Ps4vmak58305JX4/VJvQZ+n59XAH4maWQ+9rflK8B+1M5GJJ0kaQ9Sl+UVdHWTQyd7EDp5xYAfS12hckih/EiWvFqjeBXZKYXyl0ifYF4qlNWudNmlUHZoYf1GV8pMzmUzCmUTCst2dxXZfzWYH622uQzn6KIW2/5CYblvF8pHt7HdGd3FTPoE/1KTZR4C1qzb5k51y+xaN/+mBtuZXh93o2MhdfdManAckxtss/i4GFi51XnqZht7NairtTq2Zj4PjdZ7CXhHXayrk8ZWasucUDe/0dWIxfNzaKu63eTvLOAXLY7vnz14b01osb3ilZjXNJi/2TLW69rfZkY3f8OlzgnpQ82NLfZT/B8wI5dNXob3yivA1p36f+gWTN/5BTCrybwTSN0xz5EG7c6k/Lv/30v6pDaf1Of7hYioXRlFRHwTOIT0qeuFHNd04H+Bpe6ZWUbjSMc3jfTJ/0XSIOeBEfGTXm67pYi4jXSBwiWk872Q1Gd+OrBDRPyzbvmb6bpx7wnSP4eij5FaFi+Quhe/TjpHLUl6G+mKqO66x4J07h/L+zkIOCi6v4t/AulT9ZOkfxqzgVtIlzZf3WylfPw7kK4efJx0fp4Dfgtsn89fcfkXWbJLrDgN6YPKT0hdO88DZ5E+bPVYJJ8mdbVdQXpfvUo61t+zZP1s9711MenS9CdI74nnSd1/X4iInxWW+xKpDjS6UKL0eh2pBbcHqdV9f97P86Txth8A329zUz8mJaqnSeduFjAJ2DvSxQkdUbu+38wakLQ66R/GxsCJEXFch7b7TdLX4GweEQ91YptVkXQhKfH9MSLe093yNng4wZg1IekB0v0MawBzgTHRYtxjsJF0DumS9toXur6/VevIBh93kZk19ybSTYp/BT7g5LKUjUjJ5e/AkU4uVs8tGDMzK4VbMGZmVgonGDMzK4V/cCxbd911Y/To0VWHYWbWr9x+++3PRkTDmzOdYLLRo0czderUqsMwM+tXJDX9Tjx3kZmZWSmcYMzMrBROMGZmVgonGDMzK4UTjJmZlcIJxszMSuEEY2ZmpXCCMTOzUvhGy35m9NFXVh3CgDLj5L2rDsFswHILxszMSuEEY2ZmpXCCMTOzUjjBmJlZKZxgzMysFE4wZmZWCicYMzMrRWkJRtLZkp6R9NdC2dqSrpP0UH5eqzDvGEnTJT0oaY9C+baS7s3zfiRJuXyopN/k8imSRhfWGZ/38ZCk8WUdo5mZNVdmC2YCsGdd2dHApIgYA0zKr5G0BTAO2DKvc7qkIXmdM4DDgTH5UdvmYcCciNgMOA04JW9rbeB44J3AdsDxxURmZmZ9o7QEExE3AbPrivcHJubpicABhfLzI2JBRDwKTAe2k7QBMDwibomIAM6pW6e2rYuA3XLrZg/guoiYHRFzgOtYOtGZmVnJ+noMZv2IeAogP6+Xy0cCTxSWm5nLRubp+vIl1omIhcDzwDottmVmZn1oeRnkV4OyaFHe03WW3Kl0uKSpkqbOmjWrrUDNzKw9fZ1gns7dXuTnZ3L5TGDDwnKjgCdz+agG5UusI2lFYA1Sl1yzbS0lIs6MiLERMXbEiBG9OCwzM6vX1wnmcqB2Vdd44LJC+bh8ZdgmpMH8W3M32jxJ2+fxlU/UrVPb1kHA9Xmc5hpgd0lr5cH93XOZmZn1odK+rl/SecAuwLqSZpKu7DoZuEDSYcDjwMEAETFN0gXAfcBC4IiIWJQ39TnSFWmrAlfnB8BZwLmSppNaLuPytmZLOhG4LS93QkTUX2xgZmYlKy3BRMRHmszarcnyJwEnNSifCmzVoHw+OUE1mHc2cHbbwZqZWcctL4P8ZmY2wDjBmJlZKZxgzMysFE4wZmZWCicYMzMrhROMmZmVwgnGzMxK4QRjZmalcIIxM7NSOMGYmVkpnGDMzKwUTjBmZlYKJxgzMyuFE4yZmZXCCcbMzErhBGNmZqVwgjEzs1I4wZiZWSmcYMzMrBROMGZmVgonGDMzK4UTjJmZlcIJxszMSuEEY2ZmpXCCMTOzUjjBmJlZKZxgzMysFE4wZmZWCicYMzMrRbcJRtKRkoYrOUvSHZJ274vgzMys/2qnBfOpiJgL7A6MAD4JnNybnUo6StI0SX+VdJ6kVSStLek6SQ/l57UKyx8jabqkByXtUSjfVtK9ed6PJCmXD5X0m1w+RdLo3sRrZmbLrp0Eo/z8fuBXEXF3oWyZSRoJfAkYGxFbAUOAccDRwKSIGANMyq+RtEWevyWwJ3C6pCF5c2cAhwNj8mPPXH4YMCciNgNOA07pabxmZtYz7SSY2yVdS0ow10gaBizu5X5XBFaVtCKwGvAksD8wMc+fCByQp/cHzo+IBRHxKDAd2E7SBsDwiLglIgI4p26d2rYuAnartW7MzKxvrNhqZv6nfBypa+yRiHhJ0jqkbrIeiYi/S/oe8DjwMnBtRFwraf2IeCov85Sk9fIqI4G/FDYxM5e9mqfry2vrPJG3tVDS88A6wLM9jdvMzJZNyxZMbhlcGhF3RMQ/c9lzEXFPT3eYx1b2BzYB3gCsLumQVqs0Cq1Feat16mM5XNJUSVNnzZrVOnAzM1sm7XSR/UXSOzq4z38FHo2IWRHxKnAJ8C7g6dztRX5+Ji8/E9iwsP4oUpfazDxdX77EOrkbbg1gdn0gEXFmRIyNiLEjRozo0OGZmRm0l2DeS0oyD0u6J1+11eMWDKlrbHtJq+UuuN2A+4HLgfF5mfHAZXn6cmBcvjJsE9Jg/q25O22epO3zdj5Rt05tWwcB1+fWmJmZ9ZGWYzDZXp3cYURMkXQRcAewELgTOBN4HXCBpMNISejgvPw0SRcA9+Xlj4iIRXlznwMmAKsCV+cHwFnAuZKmk1ou4zp5DGZm1r1uE0xEPCZpJ2BMRPxK0ghSMuixiDgeOL6ueAGpNdNo+ZOAkxqUTwW2alA+n5ygzMysGu3cyX888A3gmFy0EvC/ZQZlZmb9XztjMB8A9gNeBIiIJ4FhZQZlZmb9XzsJ5pU8QB4AklYvNyQzMxsI2kkwF0j6ObCmpE8DfwB+UW5YZmbW37UzyP89Se8D5gJvAo6LiOtKj8zMzPq1di5TJiKukzSltryktSNiqRsXzczMarpNMJI+A5xA+t6wxaSvYQlg03JDMzOz/qydFszXgC0jwl8UaWZmbWtnkP9h4KWyAzEzs4GlnRbMMcCf8xjMglphRHyptKjMzKzfayfB/By4HriX3v/QmJmZDRLtJJiFEfGV0iMxM7MBpZ0xmBvyD3NtIGnt2qP0yMzMrF9rpwXz0fx8TKHMlymbmVlL7dzJv0lfBGJmZgNLOzdarkT6Ya/35KLJwM/zzx2bmZk11E4X2Rmk34A5Pb/+eC77t7KCMjOz/q+dBPOOiHhb4fX1ku4uKyAzMxsY2rmKbJGkN9ZeSNoUWFReSGZmNhC004L5OulS5UdIX3S5MfCpUqMyM7N+r50EczMwhvRbMAIeKDUiMzMbENrpIrslIhZExD0RcXdELABuKTswMzPr35q2YCS9HhgJrCrpX0itF4DhwGp9EJuZmfVjrbrI9gAOBUYBp9KVYOYB3yw3LDMz6++aJpiImAhMlPTBiLi4D2MyM7MBoJ0xmFGShiv5paQ7JO1eemRmZtavtZNgPhURc4HdgfWATwInlxqVmZn1e+0kmNrYy/uBX0XE3YUyMzOzhtpJMLdLupaUYK6RNAz/sqWZmXWjnRstDwO2AR6JiJckrUPqJjMzM2uqnQSzU37eWnLPmJmZtafd7yKrWQXYDrgd2LWnO5W0JvBLYCvSr2N+CngQ+A0wGpgBfCgi5uTljyG1pBYBX4qIa3L5tsAEYFXgKuDIiAhJQ4FzgG2B54APR8SMnsZrZmbLrtsxmIjYt/B4HykpPN3L/f4Q+H1EvBl4G3A/cDQwKSLGAJPyayRtAYwDtgT2BE6XNCRv5wzgcNJ3pY3J8yElozkRsRlwGnBKL+M1M7Nl1M4gf72ZpCTTI5KGk34d8yyAiHglIv4J7A9MzItNBA7I0/sD5+fvQ3sUmA5sJ2kDYHhE3BIRQWqxFNepbesiYDe5f8/MrE+185PJPyZ1Y0FKSNsAvfnBsU2BWcCvJL2N1N12JLB+RDwFEBFPSVovLz8S+Eth/Zm57NU8XV9eW+eJvK2Fkp4H1gGerTu2w0ktIDbaaKNeHJKZmdVrZwxmamF6IXBeRPypl/t8O/DFiJgi6Yfk7rAmGrU8okV5q3WWLIg4EzgTYOzYsUvNNzOznmv1bcojgBH5O8mK5VtKGhERs3q4z5nAzIiYkl9fREowT0vaILdeNgCeKSy/YWH9UcCTuXxUg/LiOjMlrQisAczuYbxmZtYDrcZgfgyMaFA+ijRI3yMR8Q/gCUlvykW7AfcBlwPjc9l44LI8fTkwTtJQSZuQBvNvzd1p8yRtn8dXPlG3Tm1bBwHX53EaMzPrI626yN4aETfWF0bENZJO7eV+vwj8WtLKwCOkGzdXAC6QdBjwOHBw3t80SReQktBC4IiIWJS38zm6LlO+Oj8gXUBwrqTppJbLuF7Ga2Zmy6hVglmph/O6FRF3AWMbzNqtyfInASc1KJ9KgyvaImI+OUGZmVk1WnWRPSTp/fWFkvYitTrMzMyaatWCOQq4QtKHSJcSQ2p17ADsU3ZgZmbWvzVtwUTE34C3AjeSvr5ldJ7eOs8zMzNrquV9MBGxAPhVH8ViZmYDSE++KsbMzKxbTjBmZlaKpglG0qT87G8iNjOzZdZqDGYDSTsD+0k6n7rv94qIO0qNzMzM+rVWCeY40neEjQK+Xzcv6MUPjpmZ2cDXNMFExEXARZK+FREn9mFMZmY2AHT7df0RcaKk/Ug/EgYwOSKuKDcsMzPr77q9ikzSf5N+EOy+/Dgyl5mZmTXVzg+O7Q1sExGLASRNBO4EjikzMDMz69/avQ9mzcL0GiXEYWZmA0w7LZj/Bu6UdAPpUuX34NaLmZl1o51B/vMkTQbeQUow38i/SmlmZtZUOy0Y8s8TX15yLGZmNoD4u8jMzKwUTjBmZlaKlglG0gqS/tpXwZiZ2cDRMsHke1/ulrRRH8VjZmYDRDuD/BsA0yTdCrxYK4yI/UqLyszM+r12Esx/lh6FmZkNOO3cB3OjpI2BMRHxB0mrAUPKD83MzPqzdr7s8tPARcDPc9FI4NISYzIzswGgncuUjwB2BOYCRMRDwHplBmVmZv1fOwlmQUS8UnshaUXSL1qamZk11U6CuVHSN4FVJb0PuBD4XblhmZlZf9dOgjkamAXcC3wGuAo4tsygzMys/2vnKrLF+UfGppC6xh6MCHeRmZlZS+1cRbY38DDwI+AnwHRJe/V2x5KGSLpT0hX59dqSrpP0UH5eq7DsMZKmS3pQ0h6F8m0l3Zvn/UiScvlQSb/J5VMkje5tvGZmtmza6SI7FXhvROwSETsD7wVO68C+jwTuL7w+GpgUEWOASfk1krYAxgFbAnsCp0uq3YdzBnA4MCY/9szlhwFzImKzHOspHYjXzMyWQTsJ5pmImF54/QjwTG92KmkUsDfwy0Lx/sDEPD0ROKBQfn5ELIiIR4HpwHaSNgCGR8QtucvunLp1atu6CNit1roxM7O+0XQMRtKBeXKapKuAC0hjMAcDt/Vyvz8A/h0YVihbP/+wGRHxlKTavTYjgb8UlpuZy17N0/XltXWeyNtaKOl5YB3g2WIQkg4ntYDYaCN/n6eZWSe1asHsmx+rAE8DOwO7kK4oW6v5aq1J2ofUKrq93VUalEWL8lbrLFkQcWZEjI2IsSNGjGgzHDMza0fTFkxEfLKkfe4I7Cfp/aTkNVzS/wJPS9ogt142oKsbbiawYWH9UcCTuXxUg/LiOjPzjaFrALNLOh4zM2ugnavINpH0fUmXSLq89ujpDiPimIgYFRGjSYP310fEIcDlwPi82Hjgsjx9OTAuXxm2CWkw/9bcnTZP0vZ5fOUTdevUtnVQ3ocvrTYz60PtfF3/pcBZpLv3F5cYy8nABZIOAx4njfUQEdMkXQDcBywEjoiIRXmdzwETgFWBq/ODHO+5kqaTWi7jSozbzMwaaCfBzI+IH5Wx84iYDEzO088BuzVZ7iTgpAblU4GtGpTPJycoMzOrRjsJ5oeSjgeuBRbUCiPijtKiMjOzfq+dBPNW4OPArnR1kUV+bWZm1lA7CeYDwKbFr+w3MzPrTjt38t8NrFlyHGZmNsC004JZH3hA0m0sOQazX2lRmZlZv9dOgjm+9CjMzGzAaef3YG7si0DMzGxg6TbBSJpH1/d4rQysBLwYEcPLDMzMzPq3dlowxW88RtIBwHZlBWRmZgNDO1eRLSEiLsX3wJiZWTfa6SI7sPByBWAsDb763szMrKidq8j2LUwvBGaQfjHSzMysqXbGYMr6XRgzMxvAWv1k8nEt1ouIOLGEeMzMbIBo1YJ5sUHZ6sBhpN+3d4IxM7OmWv1k8qm1aUnDgCOBTwLnA6c2W8/MzAy6GYORtDbwFeBjwETg7RExpy8CMzOz/q3VGMx3gQOBM4G3RsQLfRaVmZn1e61utPwq8AbgWOBJSXPzY56kuX0TnpmZ9VetxmCW+S5/MzOzGicRMzMrhROMmZmVwgnGzMxK4QRjZmalcIIxM7NSOMGYmVkp2vm6fjOztow++sqqQxgwZpy8d9Uh9JpbMGZmVgonGDMzK4UTjJmZlaLPE4ykDSXdIOl+SdMkHZnL15Z0naSH8vNahXWOkTRd0oOS9iiUbyvp3jzvR5KUy4dK+k0unyJpdF8fp5nZYFdFC2Yh8NWIeAuwPXCEpC2Ao4FJETEGmJRfk+eNA7YE9gROlzQkb+sM4HBgTH7smcsPA+ZExGbAacApfXFgZmbWpc8TTEQ8FRF35Ol5wP3ASGB/0m/OkJ8PyNP7A+dHxIKIeBSYDmwnaQNgeETcEhEBnFO3Tm1bFwG71Vo3ZmbWNyodg8ldV/8CTAHWj4inICUhYL282EjgicJqM3PZyDxdX77EOhGxEHie9DPP9fs/XNJUSVNnzZrVoaMyMzOoMMFIeh1wMfDliGj1+zKNWh7RorzVOksWRJwZEWMjYuyIESO6C9nMzJZBJQlG0kqk5PLriLgkFz+du73Iz8/k8pnAhoXVRwFP5vJRDcqXWEfSisAawOzOH4mZmTVTxVVkAs4C7o+I7xdmXQ6Mz9PjgcsK5ePylWGbkAbzb83daPMkbZ+3+Ym6dWrbOgi4Po/TmJlZH6niq2J2BD4O3Cvprlz2TeBk4AJJhwGPAwcDRMQ0SRcA95GuQDsiIhbl9T4HTABWBa7OD0gJ7FxJ00ktl3ElH5OZmdXp8wQTETfTeIwEYLcm65wEnNSgfCqwVYPy+eQEZWZm1fCd/GZmVgonGDMzK4UTjJmZlcIJxszMSuEEY2ZmpXCCMTOzUjjBmJlZKZxgzMysFE4wZmZWCicYMzMrhROMmZmVwgnGzMxK4QRjZmalcIIxM7NSOMGYmVkpnGDMzKwUTjBmZlYKJxgzMyuFE4yZmZXCCcbMzErhBGNmZqVwgjEzs1I4wZiZWSmcYMzMrBROMGZmVgonGDMzK4UTjJmZlcIJxszMSuEEY2ZmpXCCMTOzUgzoBCNpT0kPSpou6eiq4zEzG0wGbIKRNAT4KbAXsAXwEUlbVBuVmdngMWATDLAdMD0iHomIV4Dzgf0rjsnMbNBYseoASjQSeKLweibwzuICkg4HDs8vX5D0YB/FNhisCzxbdRDd0SlVR2AVWe7rZz+qmxs3mzGQE4walMUSLyLOBM7sm3AGF0lTI2Js1XGYNeL62TcGchfZTGDDwutRwJMVxWJmNugM5ARzGzBG0iaSVgbGAZdXHJOZ2aAxYLvIImKhpC8A1wBDgLMjYlrFYQ0m7nq05ZnrZx9QRHS/lJmZ2TIayF1kZmZWIScYMzMrhROM9ZqkIZK+W3UcZo24flbHCcZ6LSIWAdtKanTvkVmlXD+rM2CvIrM+dydwmaQLgRdrhRFxSXUhmb3G9bMCTjDWKWsDzwG7FsoC8BvYlgeunxXwZcpmZlYKj8FYR0jaXNIkSX/Nr7eWdGzVcZmB62dVnGCsU34BHAO8ChAR95C+nsdseeD6WQEnGOuU1SLi1rqyhZVEYrY0188KOMFYpzwr6Y3kn0SQdBDwVLUhmb3G9bMCHuS3jpC0KekLBN8FzAEeBQ6JiBlVxmUGrp9VcYKxjpK0OrBCRMyrOhazeq6ffcsJxjpC0prAJ4DRFO6viogvVRSS2WtcP6vhGy2tU64C/gLcCyyuOBazeq6fFXALxjpC0h0R8faq4zBrxPWzGk4w1hGSjgJeAK4AFtTKI2J2ZUGZZa6f1XAXmXXKK8B3gf8gXwqanzetLCKzLq6fFXALxjpC0sPAOyPi2apjMavn+lkN32hpnTINeKnqIMyacP2sgLvIrFMWAXdJuoEl+7h9GagtD1w/K+AEY51yaX6YLY8uxfWzz3kMxjpG0srA5vnlgxHxapXxmBW5fvY9JxjrCEm7ABOBGYCADYHxEXFTdVGZJa6f1XCCsY6QdDvw0Yh4ML/eHDgvIratNjIz18+q+Coy65SVam9egIj4G7BShfGYFbl+VsCD/NYpUyWdBZybX38MuL3CeMyKXD8r4C4y6whJQ4EjgJ1Ifdw3AT+NiFcqDcwM18+qOMFYR0g6MiJ+2F2ZWRVcP6vhMRjrlPENyg7t6yDMmnD9rIDHYKxXJH0E+CiwiaTLC7OGA89VE5VZ4vpZLScY660/A08B6wKnFsrnAfdUEpFZF9fPCnkMxjoi/9b5yxGxON9j8Gbgat8tbcsD189qOMFYR+Qb2d4NrEX6adqpwEsR8bFKAzPD9bMqHuS3TlFEvAQcCPw4Ij4AbFFxTGY1rp8VcIKxTpGkHUg3sF2ZyzzGZ8sL188KOMFYp3wZOAb4bURMk7QpcEO1IZm95su4fvY5j8GYmVkp3ES0jsi/FLjUp5WI2LWCcMyW4PpZDScY65SvFaZXAT4ILKwoFrN6rp8VcBeZlUbSjRGxc9VxmDXi+lk+t2CsIyStXXi5ArAt8PqKwjFbgutnNZxgrFNuJ/Vxi9T18ChwWKURmXVx/ayAu8jMzKwUvg/GekXSIZI+3qD805I+WkVMZjWun9VyC8Z6RdKdwHsiYl5d+XDghojYtprIzFw/q+YWjPXWkPo3L0BEzAVWqiAesyLXzwo5wVhvrZS/Cn0JkoYBK1cQj1mR62eFnGCst84CLpI0ulaQp8/P88yq5PpZIV+mbL0SEd+T9AJwo6TXkS4FfRE4OSLOqDY6G+xcP6vlQX7rmPwGVqM+b7OquX72PScYMzMrhcdgzMysFE4wZmZWCg/yW69IOrDV/Ii4pK9iMavn+lktJxjrrX1bzAvAb2CrkutnhTzIb2ZmpXALxjpG0t7AlqRfDAQgIk6oLiKzLq6ffc+D/NYRkn4GfBj4Iuk3Nw4GNq40KLPM9bMa7iKzjpB0T0RsXXh+HXBJROxedWxmrp/VcAvGOuXl/PySpDcArwKbVBiPWZHrZwU8BmOdcoWkNYHvAneQrtD5RaURmXVx/ayAu8is4yQNBVaJiOerjsWsnutn33EXmXWEpKmSjpC0VkQs8JvXlieun9VwgrFOGQe8AbhN0vmS9pCkqoMyy1w/K+AuMusoSSsA+wBnAIuBs4EfRsTsSgMzw/Wzr7kFYx0jaWvgVNJA6sXAQcBc4Poq4zID188q+Coy6whJtwP/JP0M7dERsSDPmiJpx8oCM8P1syruIrOOkLRpRDxSdRxmjbh+VsNdZNYpz0n6fr5aZ6qkUyWtUXVQZpnrZwWcYKxTzgbmAR/Kj7nAryqNyKyL62cF3EVmHSHprojYprsysyq4flbDLRjrlJcl7VR7kQdOX26xvFlfcv2sgFsw1hGS3gacA9T6tecA4yPinuqiMkskbQNMJNVPAbOBQyPi7irjGuicYKyjJA0HiIi5kr4cET+oOCSz1xTrZ9WxDAZOMFYaSY9HxEZVx2GDl6SvtJofEd/vq1gGI99oaWXydz1Z1YZVHcBg5haMlcYtGLPBzVeRWa9ImidpboPHPNK315pVTtL/SBouaSVJkyQ9K+mQquMa6JxgrFciYlhEDG/wGBYR7oK15cXueWB/H2AmsDnw9WpDGvicYMxsMFgpP78fOM9fz983/AnTzAaD30l6gHRz5ecljQDmVxzTgOdBfjMbFCStBcyNiEWSVgOGR8Q/qo5rIHMLxswGLEm7RsT1kg4slBUXuaTvoxo8nGDMbCDbmfSLlfs2mBc4wZTKXWRmZlYKt2DMbMDyV8VUywnGzAay2lfFvAl4B3B5fr0vcFMlEQ0i7iIzswFP0rXAByNiXn49DLgwIvasNrKBzTdamtlgsBHwSuH1K8DoakIZPNxFZmaDwbnArZJ+S7p67AOkHyCzErmLzMwGBUlvB96dX94UEXdWGc9g4ARjZmal8BiMmZmVwgnGzMxK4QRj1oCk10s6X9LDku6TdJWkzXuwnQMkbVFGjG3u/6eS7srH8HKevkvSQVXFZIOHx2DM6ih9G+KfgYkR8bNctg0wLCL+uIzbmgBcEREXdTrOFvscEhGL6spG5zi26qs4zNyCMVvae4FXa8kFICLuiog/StpF0hW1ckk/kXRonj45txTukfQ9Se8C9gO+m1sNb5S0jaS/5GV+m79CHkmTJZ0m6SZJ90t6h6RLJD0k6TuF/R0i6da8vZ9LGpLLX5B0gqQpwA6tDk7SuZL2L7z+taT9JB0q6TJJv5f0oKTju9uvWStOMGZL2wq4fVlWkLQ26d6KLSNia+A7EfFn0leTfD0itomIh4FzgG/kZe4Fji9s5pWIeA/wM+Ay4Igcy6GS1pH0FuDDwI4RsQ2wCPhYXnd14K8R8c6IuLmbcH8JfDLHvQbwLuCqPG+7vM1tgIMlje1mv2ZN+UZLs86YS/qFxF9KuhK4on6B/M98zYi4MRdNBC4sLFL7nqx7gWkR8VRe7xFgQ2AnYFvgtvybJqsCz+R1FgEXtxNoRNyYx2bWAw4ELo6IhXmb10XEc3m/l+R9LmyxX7OmnGDMljYNaDYIvpAlW/6rAOR/0NsBuwHjgC8Auy7jfhfk58WF6drrFQGRxoWOabDu/Ppxl26cS2qFjAM+VSivH5SNbvZr1pS7yMyWdj0wVNKnawV5TGRn4DFgC0lDc4tktzz/dcAaEXEV8GVSFxPAPPI3+kbE88AcSbW7yT8O1Foz7ZgEHJRbHkhaW9LGPTtEJuQ4iYhphfL35e2uChwA/KnD+7VBxC0YszoREZI+APxA0tGkrq8ZwJcj4glJFwD3AA8Bta8bGQZcJmkV0if+o3L5+cAvJH2J1CoaD/ws/yb8I+SxkDbjuk/SscC1klYAXiWN0zzWg2N8WtL9wKV1s24mtW42A/4vIqYCdGq/Nrj4MmWzQSgnuHuBt+eWFflquLER8YUqY7OBw11kZoOMpH8FHgB+XEsuZmVwC8bMzErhFoyZmZXCCcbMzErhBGNmZqVwgjEzs1I4wZiZWSmcYMzMrBT/D7mzc0Yyk0TGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "customer_type = df['Customer Type'].value_counts()\n",
    "customer_type.plot(kind='bar')\n",
    "\n",
    "plt.title(' Number of Loyal/Disloyal Customers', fontsize=15, fontweight = 'bold')\n",
    "plt.xlabel('Customer Type')\n",
    "plt.ylabel('Number of Customers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "13bbefdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loyal Customer       106100\n",
       "disloyal Customer     23780\n",
       "Name: Customer Type, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Customer Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "47390247",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customer_type = df['Customer Type'].value_counts()\n",
    "\n",
    "labels = 'Loyal Customers', 'Disloyal Customers'\n",
    "sizes = [215, 130]\n",
    "colors = ['blue', 'green']\n",
    "explode = (0.1, 0,)  # explode 1st slice\n",
    "\n",
    "# Plot\n",
    "plt.pie(customer_type, explode=explode, labels=labels, colors=colors,\n",
    "autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "\n",
    "plt.title('Customer Loyalty', fontsize=15,  fontweight = 'bold')\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a55c8223",
   "metadata": {},
   "outputs": [],
   "source": [
    "business_travel_df = df[df['Type of Travel'] == 'Business travel']\n",
    "personal_travel_df = df[df['Type of Travel'] == 'Personal Travel']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c37e00dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "business_travel_satisfaction = business_travel_df['satisfaction'].value_counts()\n",
    "\n",
    "labels = 'Satisfied', 'Dissatisfied'\n",
    "sizes = [215, 130]\n",
    "colors = ['blue', 'green']\n",
    "explode = (0.1, 0,)  # explode 1st slice\n",
    "\n",
    "# Plot\n",
    "plt.pie(business_travel_satisfaction, explode=explode, labels=labels, colors=colors,\n",
    "autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "\n",
    "plt.title('Business Travel Type Satisfaction', fontsize=15,  fontweight = 'bold')\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b47bf316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfied       52356\n",
       "dissatisfied    37337\n",
       "Name: satisfaction, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_travel_satisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6679ae9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "personal_travel_satisfaction = personal_travel_df['satisfaction'].value_counts()\n",
    "\n",
    "labels = 'Dissatisfied', 'Satisfied'\n",
    "sizes = [215, 130]\n",
    "colors = ['blue', 'green']\n",
    "explode = (0.1, 0,)  # explode 1st slice\n",
    "\n",
    "# Plot\n",
    "plt.pie(personal_travel_satisfaction, explode=explode, labels=labels, colors=colors,\n",
    "autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "\n",
    "plt.title('Personal Travel Type Satisfaction', fontsize=15,  fontweight = 'bold')\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "40e8a795",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dissatisfied    21456\n",
       "satisfied       18731\n",
       "Name: satisfaction, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "personal_travel_satisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d2649c81",
   "metadata": {},
   "outputs": [],
   "source": [
    "satisfied_df = df[df['satisfaction_numb'] == 1 ]\n",
    "dissatisfied_df = df[df['satisfaction_numb'] == 0 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "96b1bb85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average Departure Delay (mins)')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "satisfaction_group = df.groupby('satisfaction')['Departure Delay in Minutes'].mean()\n",
    "\n",
    "satisfaction_group.plot(kind='bar')\n",
    "\n",
    "plt.title('Satisfaction Based on Departure Delay', fontsize=15,  fontweight = 'bold')\n",
    "plt.xlabel('Satisfaction')\n",
    "plt.ylabel('Average Departure Delay (mins)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c83ba161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average Arival Delay (mins)')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "satisfaction_group = df.groupby('satisfaction')['Arrival Delay in Minutes'].mean()\n",
    "\n",
    "satisfaction_group.plot(kind='bar')\n",
    "\n",
    "plt.title('Satisfaction Based on Arrival Delay', fontsize=15,  fontweight = 'bold')\n",
    "plt.xlabel('Satisfaction')\n",
    "plt.ylabel('Average Arival Delay (mins)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c0f22986",
   "metadata": {},
   "outputs": [],
   "source": [
    "satisfied_class_count = satisfied_df['Class'].value_counts()\n",
    "dissatisfied_class_count = dissatisfied_df['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "19e67a8c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = 'Business', 'Eco', 'Eco Plus'\n",
    "sizes = [215, 130, 245]\n",
    "colors = ['blue', 'orange', 'green']\n",
    "explode = (0.1, 0, 0)  # explode 1st slice\n",
    "\n",
    "# Plot\n",
    "plt.pie(satisfied_class_count, explode=explode, labels=labels, colors=colors,\n",
    "autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "\n",
    "plt.title('Class Satisfaction', fontsize=15,  fontweight = 'bold')\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a0bdf65a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Business    44095\n",
       "Eco         22973\n",
       "Eco Plus     4019\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "satisfied_df['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0e5f71ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "business_df = df[df['Class'] == 'Business']\n",
    "eco_df = df[df['Class'] == 'Eco']\n",
    "eco_plus_df = df[df['Class'] == 'Eco Plus']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "723ca909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "business_class_satisfaction = business_df['satisfaction'].value_counts()\n",
    "\n",
    "labels = 'Satisfied', 'Dissatisfied'\n",
    "sizes = [215, 130]\n",
    "colors = ['blue', 'green']\n",
    "explode = (0.1, 0,)  # explode 1st slice\n",
    "\n",
    "# Plot\n",
    "plt.pie(business_class_satisfaction, explode=explode, labels=labels, colors=colors,\n",
    "autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "\n",
    "plt.title('Business Class Satisfaction', fontsize=15,  fontweight = 'bold')\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cec89ddc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfied       52356\n",
       "dissatisfied    37337\n",
       "Name: satisfaction, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_travel_satisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ae420249",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eco_class_satisfaction = eco_df['satisfaction'].value_counts()\n",
    "\n",
    "labels = 'Dissatisfied', 'Satisfied'\n",
    "sizes = [215, 130]\n",
    "colors = ['blue', 'green']\n",
    "explode = (0.1, 0,)  # explode 1st slice\n",
    "\n",
    "# Plot\n",
    "plt.pie(eco_class_satisfaction, explode=explode, labels=labels, colors=colors,\n",
    "autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "\n",
    "plt.title('Eco Class Satisfaction', fontsize=15,  fontweight = 'bold')\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "97a4dfdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dissatisfied    35336\n",
       "satisfied       22973\n",
       "Name: satisfaction, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eco_class_satisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "108e4e97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eco_plus_class_satisfaction = eco_plus_df['satisfaction'].value_counts()\n",
    "\n",
    "labels = 'Dissatisfied', 'Satisfied'\n",
    "sizes = [215, 130]\n",
    "colors = ['blue', 'green']\n",
    "explode = (0.1, 0,)  # explode 1st slice\n",
    "\n",
    "# Plot\n",
    "plt.pie(eco_plus_class_satisfaction, explode=explode, labels=labels, colors=colors,\n",
    "autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "\n",
    "plt.title('Eco Plus Class Satisfaction', fontsize=15,  fontweight = 'bold')\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6bbc00fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dissatisfied    5392\n",
       "satisfied       4019\n",
       "Name: satisfaction, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eco_plus_class_satisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "76f37e78",
   "metadata": {},
   "outputs": [],
   "source": [
    "satisfied_gender_count = satisfied_df['Gender'].value_counts()\n",
    "dissatisfied_gender_count = dissatisfied_df['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fa959254",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = 'Female','Male'\n",
    "sizes = [215, 130]\n",
    "colors = ['blue', 'green']\n",
    "explode = (0.1, 0)  # explode 1st slice\n",
    "\n",
    "# Plot\n",
    "plt.pie(satisfied_gender_count, explode=explode, labels=labels, colors=colors,\n",
    "autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "\n",
    "plt.title('Gender Satisfaction', fontsize=15,  fontweight = 'bold')\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e9bf62bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    42928\n",
       "Male      28159\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "satisfied_df['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6f029de0",
   "metadata": {},
   "outputs": [],
   "source": [
    "female_df = df[df['Gender'] == 'Female']\n",
    "male_df = df[df['Gender'] == 'Male']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a741e027",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "female_gender_satisfaction = female_df['satisfaction'].value_counts()\n",
    "\n",
    "labels = 'Satisfied', 'Dissatisfied'\n",
    "sizes = [215, 130]\n",
    "colors = ['blue', 'green']\n",
    "explode = (0.1, 0,)  # explode 1st slice\n",
    "\n",
    "# Plot\n",
    "plt.pie(female_gender_satisfaction, explode=explode, labels=labels, colors=colors,\n",
    "autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "\n",
    "plt.title('Female Gender Satisfaction', fontsize=15,  fontweight = 'bold')\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "79ccd644",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfied       42928\n",
       "dissatisfied    22971\n",
       "Name: satisfaction, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_gender_satisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bf05d346",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_gender_satisfaction = male_df['satisfaction'].value_counts()\n",
    "\n",
    "labels = 'Dissatisfied', 'Satisfied'\n",
    "sizes = [215, 130]\n",
    "colors = ['blue', 'green']\n",
    "explode = (0.1, 0,)  # explode 1st slice\n",
    "\n",
    "# Plot\n",
    "plt.pie(male_gender_satisfaction, explode=explode, labels=labels, colors=colors,\n",
    "autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "\n",
    "plt.title('Male Gender Satisfaction', fontsize=15,  fontweight = 'bold')\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "10fdfbd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dissatisfied    35822\n",
       "satisfied       28159\n",
       "Name: satisfaction, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_gender_satisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2190c5e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(129880, 24)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2e09c2b",
   "metadata": {},
   "source": [
    "### Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "570c9934",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a59d14c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy.drop('satisfaction_numb', axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6a87ef64",
   "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>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>Gate location</th>\n",
       "      <th>...</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",
       "      <th>satisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>2</td>\n",
       "      <td>...</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",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>3</td>\n",
       "      <td>...</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",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>3</td>\n",
       "      <td>...</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",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>3</td>\n",
       "      <td>...</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",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>3</td>\n",
       "      <td>...</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",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender   Customer Type  Age   Type of Travel     Class  Flight Distance  \\\n",
       "0  Female  Loyal Customer   65  Personal Travel       Eco              265   \n",
       "1    Male  Loyal Customer   47  Personal Travel  Business             2464   \n",
       "2  Female  Loyal Customer   15  Personal Travel       Eco             2138   \n",
       "3  Female  Loyal Customer   60  Personal Travel       Eco              623   \n",
       "4  Female  Loyal Customer   70  Personal Travel       Eco              354   \n",
       "\n",
       "   Seat comfort  Departure/Arrival time convenient  Food and drink  \\\n",
       "0             0                                  0               0   \n",
       "1             0                                  0               0   \n",
       "2             0                                  0               0   \n",
       "3             0                                  0               0   \n",
       "4             0                                  0               0   \n",
       "\n",
       "   Gate location  ...  Ease of Online booking  On-board service  \\\n",
       "0              2  ...                       3                 3   \n",
       "1              3  ...                       3                 4   \n",
       "2              3  ...                       2                 3   \n",
       "3              3  ...                       1                 1   \n",
       "4              3  ...                       2                 2   \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",
       "   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",
       "   satisfaction  \n",
       "0     satisfied  \n",
       "1     satisfied  \n",
       "2     satisfied  \n",
       "3     satisfied  \n",
       "4     satisfied  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "24eb2ae3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender : ['Female' 'Male']\n",
      "Customer Type : ['Loyal Customer' 'disloyal Customer']\n",
      "Type of Travel : ['Personal Travel' 'Business travel']\n",
      "Class : ['Eco' 'Business' 'Eco Plus']\n",
      "satisfaction : ['satisfied' 'dissatisfied']\n"
     ]
    }
   ],
   "source": [
    "print_unique_col_values(df_copy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3cf76786",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy.replace({\n",
    "    'satisfied' : 1,\n",
    "    'dissatisfied' : 0,\n",
    "    'Female': 1,\n",
    "    'Male' : 0,\n",
    "    'Loyal Customer' : 1,\n",
    "    'disloyal Customer' : 0,\n",
    "    'Personal Travel' : 1,\n",
    "    'Business travel' : 0\n",
    "}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5054a720",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.get_dummies(df_copy, columns=['Class'], drop_first='True')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "429b29f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_scale = ['Age', 'Flight Distance','Gate location', 'Inflight wifi service', 'Inflight entertainment', 'Online support', 'Ease of Online booking', 'On-board service', 'Leg room service', 'Baggage handling', 'Checkin service', 'Cleanliness', 'Online boarding', 'Departure Delay in Minutes', 'Arrival Delay in Minutes']\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "df1[cols_to_scale] = scaler.fit_transform(df1[cols_to_scale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "867f171b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9e70f5d8",
   "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>Gender</th>\n",
       "      <th>Customer Type</th>\n",
       "      <th>Age</th>\n",
       "      <th>Type of Travel</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>...</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",
       "      <th>satisfaction</th>\n",
       "      <th>Class_Eco</th>\n",
       "      <th>Class_Eco Plus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.743590</td>\n",
       "      <td>1</td>\n",
       "      <td>0.031155</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.512821</td>\n",
       "      <td>1</td>\n",
       "      <td>0.349804</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.194724</td>\n",
       "      <td>0.192551</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.102564</td>\n",
       "      <td>1</td>\n",
       "      <td>0.302565</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.679487</td>\n",
       "      <td>1</td>\n",
       "      <td>0.083031</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.807692</td>\n",
       "      <td>1</td>\n",
       "      <td>0.044052</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Customer Type       Age  Type of Travel  Flight Distance  \\\n",
       "0       1              1  0.743590               1         0.031155   \n",
       "1       0              1  0.512821               1         0.349804   \n",
       "2       1              1  0.102564               1         0.302565   \n",
       "3       1              1  0.679487               1         0.083031   \n",
       "4       1              1  0.807692               1         0.044052   \n",
       "\n",
       "   Seat comfort  Departure/Arrival time convenient  Food and drink  \\\n",
       "0             0                                  0               0   \n",
       "1             0                                  0               0   \n",
       "2             0                                  0               0   \n",
       "3             0                                  0               0   \n",
       "4             0                                  0               0   \n",
       "\n",
       "   Gate location  Inflight wifi service  ...  Leg room service  \\\n",
       "0            0.4                    0.4  ...               0.0   \n",
       "1            0.6                    0.0  ...               0.8   \n",
       "2            0.6                    0.4  ...               0.6   \n",
       "3            0.6                    0.6  ...               0.0   \n",
       "4            0.6                    0.8  ...               0.0   \n",
       "\n",
       "   Baggage handling  Checkin service  Cleanliness  Online boarding  \\\n",
       "0              0.50              1.0          0.6              0.4   \n",
       "1              0.75              0.4          0.6              0.4   \n",
       "2              0.75              0.8          0.8              0.4   \n",
       "3              0.00              0.8          0.2              0.6   \n",
       "4              0.25              0.8          0.4              1.0   \n",
       "\n",
       "   Departure Delay in Minutes  Arrival Delay in Minutes  satisfaction  \\\n",
       "0                    0.000000                  0.000000             1   \n",
       "1                    0.194724                  0.192551             1   \n",
       "2                    0.000000                  0.000000             1   \n",
       "3                    0.000000                  0.000000             1   \n",
       "4                    0.000000                  0.000000             1   \n",
       "\n",
       "   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  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c38ffcd",
   "metadata": {},
   "source": [
    "### Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "49b90460",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df1.drop('satisfaction', axis='columns')\n",
    "y = df1.satisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f292a7f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "88844a6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(max_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "97eab021",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "9db8c6b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yp = model.predict(X_test)\n",
    "yp[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7959e9b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87828     1\n",
       "113431    1\n",
       "129051    1\n",
       "99669     1\n",
       "79437     0\n",
       "Name: satisfaction, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "84933d6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = []\n",
    "for element in yp:\n",
    "    if element > 0.5:\n",
    "        y_pred.append(1)\n",
    "    else:\n",
    "        y_pred.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "68ef934b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.81      0.82     11736\n",
      "           1       0.85      0.85      0.85     14162\n",
      "\n",
      "    accuracy                           0.83     25898\n",
      "   macro avg       0.83      0.83      0.83     25898\n",
      "weighted avg       0.83      0.83      0.83     25898\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ed69cde",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}