Correlation Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import modules libraries in this section:\n",
    "import pyodbc\n",
    "import pandas as pd\n",
    "#import statsmodels.api as sm\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import warnings\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import os\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "air = pd.read_csv('C:\\\\Users\\\\Win 1809 UEFI\\\\Desktop\\\\9999.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>airline</th>\n",
       "      <th>Type_Of_Traveller</th>\n",
       "      <th>seat_type</th>\n",
       "      <th>Seat_Comfort</th>\n",
       "      <th>Cabin_Staff</th>\n",
       "      <th>Food_Beverages</th>\n",
       "      <th>Inflight_Entertainment</th>\n",
       "      <th>Ground_Service</th>\n",
       "      <th>Wifi_Connectivity</th>\n",
       "      <th>Value_Money</th>\n",
       "      <th>Recommended</th>\n",
       "      <th>airline_sentiment</th>\n",
       "      <th>overall_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Saudi Arabian</td>\n",
       "      <td>Family Leisure</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>6</td>\n",
       "      <td>poor inflight meal</td>\n",
       "      <td>Saudi Arabian Airlines flight from Medina to R...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         airline Type_Of_Traveller      seat_type  Seat_Comfort  Cabin_Staff  \\\n",
       "0  Saudi Arabian    Family Leisure  Economy Class           4.0          3.0   \n",
       "\n",
       "   Food_Beverages  Inflight_Entertainment  Ground_Service  Wifi_Connectivity  \\\n",
       "0             1.0                     NaN             4.0                NaN   \n",
       "\n",
       "   Value_Money  Recommended airline_sentiment  overall_rating  \\\n",
       "0            4            1           Neutral               6   \n",
       "\n",
       "                title                                             review  \n",
       "0  poor inflight meal  Saudi Arabian Airlines flight from Medina to R...  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4932, 15) \n",
      "\n",
      "Index(['airline', 'Type_Of_Traveller', 'seat_type', 'Seat_Comfort',\n",
      "       'Cabin_Staff', 'Food_Beverages', 'Inflight_Entertainment',\n",
      "       'Ground_Service', 'Wifi_Connectivity', 'Value_Money', 'Recommended',\n",
      "       'airline_sentiment', 'overall_rating', 'title', 'review'],\n",
      "      dtype='object') \n",
      "\n",
      "airline                    object\n",
      "Type_Of_Traveller          object\n",
      "seat_type                  object\n",
      "Seat_Comfort              float64\n",
      "Cabin_Staff               float64\n",
      "Food_Beverages            float64\n",
      "Inflight_Entertainment    float64\n",
      "Ground_Service            float64\n",
      "Wifi_Connectivity         float64\n",
      "Value_Money                 int64\n",
      "Recommended                 int64\n",
      "airline_sentiment          object\n",
      "overall_rating              int64\n",
      "title                      object\n",
      "review                     object\n",
      "dtype: object \n",
      "\n"
     ]
    }
   ],
   "source": [
    "df = air\n",
    "print(df.shape,'\\n')\n",
    "print(df.columns,'\\n')\n",
    "print(df.dtypes,'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Imputing missing values, dummy-coding columns, dropna to drop null values row, drop duplicates if any, drop irrelevant features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of reviews for each airline \n",
      "  airline\n",
      "Emirates                    1189\n",
      "Turkish Airlines            1051\n",
      "Qatar Airways               1023\n",
      "Etihad Airways               843\n",
      "Oman Air                     177\n",
      "Flydubai                     141\n",
      "Kuwait Airways               140\n",
      "Saudi Arabian                128\n",
      "Royal Jordanian Airlines     121\n",
      "Gulf Air                     119\n",
      "Name: airline_sentiment, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Total number of reviews for each airline \\n \",df.groupby('airline')['airline_sentiment'].count().sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "airline                      0\n",
       "Type_Of_Traveller            0\n",
       "seat_type                    0\n",
       "Seat_Comfort                73\n",
       "Cabin_Staff                 78\n",
       "Food_Beverages             263\n",
       "Inflight_Entertainment     521\n",
       "Ground_Service              35\n",
       "Wifi_Connectivity         2664\n",
       "Value_Money                  0\n",
       "Recommended                  0\n",
       "airline_sentiment            0\n",
       "overall_rating               0\n",
       "title                        0\n",
       "review                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify null values \n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage null or na values in df\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "airline                    0.00\n",
       "Type_Of_Traveller          0.00\n",
       "seat_type                  0.00\n",
       "Seat_Comfort               1.48\n",
       "Cabin_Staff                1.58\n",
       "Food_Beverages             5.33\n",
       "Inflight_Entertainment    10.56\n",
       "Ground_Service             0.71\n",
       "Wifi_Connectivity         54.01\n",
       "Value_Money                0.00\n",
       "Recommended                0.00\n",
       "airline_sentiment          0.00\n",
       "overall_rating             0.00\n",
       "title                      0.00\n",
       "review                     0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Percentage null or na values in df\")\n",
    "((df.isnull() | df.isna()).sum() * 100 / df.index.size).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Impute median 3 as a neutral score for empty rating ranging from 1-5. \n",
    "df['Seat_Comfort'].fillna(3.0,inplace=True)\n",
    "df['Cabin_Staff'].fillna(3.0,inplace=True)\n",
    "df['Food_Beverages'].fillna(3.0,inplace=True) \n",
    "df['Inflight_Entertainment'].fillna(3.0,inplace=True)\n",
    "df['Ground_Service'].fillna(3.0,inplace=True)\n",
    "df['Wifi_Connectivity'].fillna(3.0,inplace=True)\n",
    "df['Value_Money'].fillna(3.0,inplace=True)          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# change recommended value from True or False to 1 or 0 \n",
    "df_recomm = pd.get_dummies(df['Recommended'], drop_first=True)\n",
    "df_recomm.columns = ['Recommended']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# concatenate all 3 dataframes together\n",
    "df = pd.concat([df, df_recomm], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop irrelevant columns and featured columns that had been dummy-coded. \n",
    "df.drop(columns=['title','airline_sentiment'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\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>airline</th>\n",
       "      <th>Type_Of_Traveller</th>\n",
       "      <th>seat_type</th>\n",
       "      <th>Seat_Comfort</th>\n",
       "      <th>Cabin_Staff</th>\n",
       "      <th>Food_Beverages</th>\n",
       "      <th>Inflight_Entertainment</th>\n",
       "      <th>Ground_Service</th>\n",
       "      <th>Wifi_Connectivity</th>\n",
       "      <th>Value_Money</th>\n",
       "      <th>Recommended</th>\n",
       "      <th>overall_rating</th>\n",
       "      <th>review</th>\n",
       "      <th>Recommended</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Saudi Arabian</td>\n",
       "      <td>Family Leisure</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Saudi Arabian Airlines flight from Medina to R...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Saudi Arabian</td>\n",
       "      <td>Business</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>I fly regularly between Dubai and Riyadh but t...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Saudi Arabian</td>\n",
       "      <td>Solo Leisure</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Damman to Dubai in economy class with Saudi Ar...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Saudi Arabian</td>\n",
       "      <td>Couple Leisure</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>Istanbul to Johannesburg via Jeddah, and Saudi...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Saudi Arabian</td>\n",
       "      <td>Family Leisure</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Kuala Lumpur to Jeddah with Saudi Arabian Airl...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4927</th>\n",
       "      <td>Turkish Airlines</td>\n",
       "      <td>Business</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Berlin to Erbil via Istanbul. Over two hours...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4928</th>\n",
       "      <td>Turkish Airlines</td>\n",
       "      <td>Family Leisure</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>London Gatwick - Istanbul - Tashkent Departed...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4929</th>\n",
       "      <td>Turkish Airlines</td>\n",
       "      <td>Couple Leisure</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Amsterdam to Abu Dhabi via Istanbul. Delays,...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4930</th>\n",
       "      <td>Turkish Airlines</td>\n",
       "      <td>Family Leisure</td>\n",
       "      <td>Economy Class</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Istanbul to Johannesburg. Our flight on 30 D...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4931</th>\n",
       "      <td>Turkish Airlines</td>\n",
       "      <td>Family Leisure</td>\n",
       "      <td>Business Class</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>Johannesburg to Istanbul. We were well taken...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4905 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               airline Type_Of_Traveller       seat_type  Seat_Comfort  \\\n",
       "0        Saudi Arabian    Family Leisure   Economy Class           4.0   \n",
       "1        Saudi Arabian          Business   Economy Class           4.0   \n",
       "2        Saudi Arabian      Solo Leisure   Economy Class           3.0   \n",
       "3        Saudi Arabian    Couple Leisure   Economy Class           4.0   \n",
       "4        Saudi Arabian    Family Leisure   Economy Class           2.0   \n",
       "...                ...               ...             ...           ...   \n",
       "4927  Turkish Airlines          Business   Economy Class           3.0   \n",
       "4928  Turkish Airlines    Family Leisure   Economy Class           2.0   \n",
       "4929  Turkish Airlines    Couple Leisure   Economy Class           3.0   \n",
       "4930  Turkish Airlines    Family Leisure   Economy Class           1.0   \n",
       "4931  Turkish Airlines    Family Leisure  Business Class           5.0   \n",
       "\n",
       "      Cabin_Staff  Food_Beverages  Inflight_Entertainment  Ground_Service  \\\n",
       "0             3.0             1.0                     3.0             4.0   \n",
       "1             5.0             4.0                     3.0             4.0   \n",
       "2             4.0             5.0                     2.0             2.0   \n",
       "3             5.0             4.0                     3.0             4.0   \n",
       "4             3.0             3.0                     2.0             4.0   \n",
       "...           ...             ...                     ...             ...   \n",
       "4927          3.0             2.0                     3.0             1.0   \n",
       "4928          1.0             2.0                     1.0             1.0   \n",
       "4929          1.0             3.0                     3.0             1.0   \n",
       "4930          1.0             1.0                     2.0             1.0   \n",
       "4931          5.0             5.0                     4.0             5.0   \n",
       "\n",
       "      Wifi_Connectivity  Value_Money  Recommended  overall_rating  \\\n",
       "0                   3.0            4            1               6   \n",
       "1                   3.0            4            1               8   \n",
       "2                   3.0            4            0               6   \n",
       "3                   3.0            4            1               9   \n",
       "4                   3.0            3            0               6   \n",
       "...                 ...          ...          ...             ...   \n",
       "4927                3.0            2            0               1   \n",
       "4928                3.0            1            0               1   \n",
       "4929                2.0            3            0               4   \n",
       "4930                3.0            1            0               2   \n",
       "4931                5.0            5            1              10   \n",
       "\n",
       "                                                 review  Recommended  \n",
       "0     Saudi Arabian Airlines flight from Medina to R...            1  \n",
       "1     I fly regularly between Dubai and Riyadh but t...            1  \n",
       "2     Damman to Dubai in economy class with Saudi Ar...            0  \n",
       "3     Istanbul to Johannesburg via Jeddah, and Saudi...            1  \n",
       "4     Kuala Lumpur to Jeddah with Saudi Arabian Airl...            0  \n",
       "...                                                 ...          ...  \n",
       "4927    Berlin to Erbil via Istanbul. Over two hours...            0  \n",
       "4928   London Gatwick - Istanbul - Tashkent Departed...            0  \n",
       "4929    Amsterdam to Abu Dhabi via Istanbul. Delays,...            0  \n",
       "4930    Istanbul to Johannesburg. Our flight on 30 D...            0  \n",
       "4931    Johannesburg to Istanbul. We were well taken...            1  \n",
       "\n",
       "[4905 rows x 14 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drops any row with null/Nan values as well as drops any duplicate (if exist)\n",
    "df.dropna(inplace=True)\n",
    "df.drop_duplicates() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ground_Service'] = df['Ground_Service'].astype('float')\n",
    "df['Wifi_Connectivity'] = df['Wifi_Connectivity'].astype('float')\n",
    "df['Value_Money'] = df['Value_Money'].astype('float')\n",
    "df['overall_rating'] = df['overall_rating'].astype('float')\n",
    "df['seat_type'] = df['seat_type'].astype('object')\n",
    "df['airline'] = df['airline'].astype('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['airline', 'Type_Of_Traveller', 'seat_type', 'Seat_Comfort',\n",
      "       'Cabin_Staff', 'Food_Beverages', 'Inflight_Entertainment',\n",
      "       'Ground_Service', 'Wifi_Connectivity', 'Value_Money', 'Recommended',\n",
      "       'overall_rating', 'review', 'Recommended'],\n",
      "      dtype='object')\n",
      "airline                   0\n",
      "Type_Of_Traveller         0\n",
      "seat_type                 0\n",
      "Seat_Comfort              0\n",
      "Cabin_Staff               0\n",
      "Food_Beverages            0\n",
      "Inflight_Entertainment    0\n",
      "Ground_Service            0\n",
      "Wifi_Connectivity         0\n",
      "Value_Money               0\n",
      "Recommended               0\n",
      "overall_rating            0\n",
      "review                    0\n",
      "Recommended               0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(4932, 14)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.columns)\n",
    "print(df.isnull().sum())\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "airline                    object\n",
       "Type_Of_Traveller          object\n",
       "seat_type                  object\n",
       "Seat_Comfort              float64\n",
       "Cabin_Staff               float64\n",
       "Food_Beverages            float64\n",
       "Inflight_Entertainment    float64\n",
       "Ground_Service            float64\n",
       "Wifi_Connectivity         float64\n",
       "Value_Money               float64\n",
       "Recommended                 int64\n",
       "overall_rating            float64\n",
       "review                     object\n",
       "Recommended                 uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder = LabelEncoder()\n",
    "df['airline'] = labelencoder.fit_transform(df['airline'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cabin_class'] = df['seat_type']\n",
    "# create dummy-coded columns for cabin class types\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder = LabelEncoder()\n",
    "df['seat_type'] = labelencoder.fit_transform(df['seat_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Economy Class     3482\n",
       "Business Class    1360\n",
       "First Class         90\n",
       "Name: cabin_class, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cabin_class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    3482\n",
       "0    1360\n",
       "2      90\n",
       "Name: seat_type, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['seat_type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  correlation analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a method for charting a correlation heat map: \n",
    "\n",
    "def correlation_heat_map(df):\n",
    "    corrs = df.corr()\n",
    "\n",
    "    # Set the default matplotlib figure size:\n",
    "    fig, ax = plt.subplots(figsize=(11,7))\n",
    "\n",
    "    # Generate a mask for the upper triangle (taken from seaborn example gallery)\n",
    "    mask = np.zeros_like(corrs, dtype=np.bool)\n",
    "    mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "    # Plot the heatmap with seaborn.\n",
    "    # Assign the matplotlib axis the function returns. This will let us resize the labels.\n",
    "    ax = sns.heatmap(corrs, mask=mask, annot=True)\n",
    "\n",
    "    # Resize the labels.\n",
    "    ax.set_xticklabels(ax.xaxis.get_ticklabels(), fontsize=14, rotation=90)\n",
    "    ax.set_yticklabels(ax.yaxis.get_ticklabels(), fontsize=14, rotation=0)\n",
    "\n",
    "    # If you put plt.show() at the bottom, it prevents those useless printouts from matplotlib.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_heat_map(air)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}