Airlines-data-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "00c30b19-c4e8-4eea-adda-09e2feef623f",
   "metadata": {},
   "source": [
    "# Objective\n",
    "\n",
    "The objective of this data analysis project is to identify opportunities to increase occupancy rate on low-performing flights, which can ultimately lead to increased profitability for the airline."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cf7559f-6274-4619-8f71-dff30b88e35d",
   "metadata": {},
   "source": [
    "Database URL : <i>https://www.kaggle.com/datasets/shahnbej007/travel-sqlite/data</i>\n",
    "\n",
    "Tech Stack : SQL, Python\n",
    "\n",
    "Author : Rajeev Ranjan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18d6e25d-bef5-4415-ab47-589f96231e99",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "53a09aad-79d6-4cec-9c96-50a43cb04577",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4212d219-8e1a-4aec-83b4-faa5bbfd2f07",
   "metadata": {},
   "source": [
    "# Connecting Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "611ab623-6504-4585-b308-4eb9b44e0428",
   "metadata": {},
   "outputs": [],
   "source": [
    "connection = sqlite3.connect('travel.sqlite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "21b2cc31-ff45-42a1-bd88-4277f54fb0bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aircrafts_data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>airports_data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>boarding_passes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bookings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>flights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>seats</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ticket_flights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>tickets</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name\n",
       "0   aircrafts_data\n",
       "1    airports_data\n",
       "2  boarding_passes\n",
       "3         bookings\n",
       "4          flights\n",
       "5            seats\n",
       "6   ticket_flights\n",
       "7          tickets"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tables in database\n",
    "table_list = pd.read_sql_query(\"select name from sqlite_master where type='table'\",connection)\n",
    "table_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7419af6-1b6d-484a-9a4a-eedef14007f8",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "03132490-c709-4074-a897-d213ba7b0cc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aircraft_code</th>\n",
       "      <th>model</th>\n",
       "      <th>range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>773</td>\n",
       "      <td>{\"en\": \"Boeing 777-300\", \"ru\": \"Боинг 777-300\"}</td>\n",
       "      <td>11100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>763</td>\n",
       "      <td>{\"en\": \"Boeing 767-300\", \"ru\": \"Боинг 767-300\"}</td>\n",
       "      <td>7900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SU9</td>\n",
       "      <td>{\"en\": \"Sukhoi Superjet-100\", \"ru\": \"Сухой Суп...</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>320</td>\n",
       "      <td>{\"en\": \"Airbus A320-200\", \"ru\": \"Аэробус A320-...</td>\n",
       "      <td>5700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321</td>\n",
       "      <td>{\"en\": \"Airbus A321-200\", \"ru\": \"Аэробус A321-...</td>\n",
       "      <td>5600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  aircraft_code                                              model  range\n",
       "0           773    {\"en\": \"Boeing 777-300\", \"ru\": \"Боинг 777-300\"}  11100\n",
       "1           763    {\"en\": \"Boeing 767-300\", \"ru\": \"Боинг 767-300\"}   7900\n",
       "2           SU9  {\"en\": \"Sukhoi Superjet-100\", \"ru\": \"Сухой Суп...   3000\n",
       "3           320  {\"en\": \"Airbus A320-200\", \"ru\": \"Аэробус A320-...   5700\n",
       "4           321  {\"en\": \"Airbus A321-200\", \"ru\": \"Аэробус A321-...   5600"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aircrafts_data = pd.read_sql_query('select * from aircrafts_data',connection)\n",
    "aircrafts_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "12c19af9-cc48-43e2-bf95-3c95a8ebe9b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aircrafts_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0f070f2e-2f39-451e-8b10-2d4a5c8e7de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>airport_code</th>\n",
       "      <th>airport_name</th>\n",
       "      <th>city</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>timezone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>YKS</td>\n",
       "      <td>{\"en\": \"Yakutsk Airport\", \"ru\": \"Якутск\"}</td>\n",
       "      <td>{\"en\": \"Yakutsk\", \"ru\": \"Якутск\"}</td>\n",
       "      <td>(129.77099609375,62.0932998657226562)</td>\n",
       "      <td>Asia/Yakutsk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MJZ</td>\n",
       "      <td>{\"en\": \"Mirny Airport\", \"ru\": \"Мирный\"}</td>\n",
       "      <td>{\"en\": \"Mirnyj\", \"ru\": \"Мирный\"}</td>\n",
       "      <td>(114.03900146484375,62.534698486328125)</td>\n",
       "      <td>Asia/Yakutsk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KHV</td>\n",
       "      <td>{\"en\": \"Khabarovsk-Novy Airport\", \"ru\": \"Хабар...</td>\n",
       "      <td>{\"en\": \"Khabarovsk\", \"ru\": \"Хабаровск\"}</td>\n",
       "      <td>(135.18800354004,48.5279998779300001)</td>\n",
       "      <td>Asia/Vladivostok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PKC</td>\n",
       "      <td>{\"en\": \"Yelizovo Airport\", \"ru\": \"Елизово\"}</td>\n",
       "      <td>{\"en\": \"Petropavlovsk\", \"ru\": \"Петропавловск-К...</td>\n",
       "      <td>(158.453994750976562,53.1679000854492188)</td>\n",
       "      <td>Asia/Kamchatka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UUS</td>\n",
       "      <td>{\"en\": \"Yuzhno-Sakhalinsk Airport\", \"ru\": \"Хом...</td>\n",
       "      <td>{\"en\": \"Yuzhno-Sakhalinsk\", \"ru\": \"Южно-Сахали...</td>\n",
       "      <td>(142.718002319335938,46.8886985778808594)</td>\n",
       "      <td>Asia/Sakhalin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  airport_code                                       airport_name  \\\n",
       "0          YKS          {\"en\": \"Yakutsk Airport\", \"ru\": \"Якутск\"}   \n",
       "1          MJZ            {\"en\": \"Mirny Airport\", \"ru\": \"Мирный\"}   \n",
       "2          KHV  {\"en\": \"Khabarovsk-Novy Airport\", \"ru\": \"Хабар...   \n",
       "3          PKC        {\"en\": \"Yelizovo Airport\", \"ru\": \"Елизово\"}   \n",
       "4          UUS  {\"en\": \"Yuzhno-Sakhalinsk Airport\", \"ru\": \"Хом...   \n",
       "\n",
       "                                                city  \\\n",
       "0                  {\"en\": \"Yakutsk\", \"ru\": \"Якутск\"}   \n",
       "1                   {\"en\": \"Mirnyj\", \"ru\": \"Мирный\"}   \n",
       "2            {\"en\": \"Khabarovsk\", \"ru\": \"Хабаровск\"}   \n",
       "3  {\"en\": \"Petropavlovsk\", \"ru\": \"Петропавловск-К...   \n",
       "4  {\"en\": \"Yuzhno-Sakhalinsk\", \"ru\": \"Южно-Сахали...   \n",
       "\n",
       "                                 coordinates          timezone  \n",
       "0      (129.77099609375,62.0932998657226562)      Asia/Yakutsk  \n",
       "1    (114.03900146484375,62.534698486328125)      Asia/Yakutsk  \n",
       "2      (135.18800354004,48.5279998779300001)  Asia/Vladivostok  \n",
       "3  (158.453994750976562,53.1679000854492188)    Asia/Kamchatka  \n",
       "4  (142.718002319335938,46.8886985778808594)     Asia/Sakhalin  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airports_data = pd.read_sql_query('select * from airports_data',connection)\n",
    "airports_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "efb28a18-d334-401a-baba-8bf5e1e3f8d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(104, 5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airports_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2a0edce1-7d84-4ee1-b296-42cd65e78069",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticket_no</th>\n",
       "      <th>flight_id</th>\n",
       "      <th>boarding_no</th>\n",
       "      <th>seat_no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0005435212351</td>\n",
       "      <td>30625</td>\n",
       "      <td>1</td>\n",
       "      <td>2D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0005435212386</td>\n",
       "      <td>30625</td>\n",
       "      <td>2</td>\n",
       "      <td>3G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0005435212381</td>\n",
       "      <td>30625</td>\n",
       "      <td>3</td>\n",
       "      <td>4H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0005432211370</td>\n",
       "      <td>30625</td>\n",
       "      <td>4</td>\n",
       "      <td>5D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0005435212357</td>\n",
       "      <td>30625</td>\n",
       "      <td>5</td>\n",
       "      <td>11A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       ticket_no  flight_id  boarding_no seat_no\n",
       "0  0005435212351      30625            1      2D\n",
       "1  0005435212386      30625            2      3G\n",
       "2  0005435212381      30625            3      4H\n",
       "3  0005432211370      30625            4      5D\n",
       "4  0005435212357      30625            5     11A"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boarding_passes = pd.read_sql_query('select * from boarding_passes',connection)\n",
    "boarding_passes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7a9467c3-a2db-49d1-a3dc-fba26097bd91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(579686, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boarding_passes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "568f6b64-c1f5-4318-9111-6179cce1b2ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>book_ref</th>\n",
       "      <th>book_date</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00000F</td>\n",
       "      <td>2017-07-05 03:12:00+03</td>\n",
       "      <td>265700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000012</td>\n",
       "      <td>2017-07-14 09:02:00+03</td>\n",
       "      <td>37900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000068</td>\n",
       "      <td>2017-08-15 14:27:00+03</td>\n",
       "      <td>18100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000181</td>\n",
       "      <td>2017-08-10 13:28:00+03</td>\n",
       "      <td>131800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0002D8</td>\n",
       "      <td>2017-08-07 21:40:00+03</td>\n",
       "      <td>23600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  book_ref               book_date  total_amount\n",
       "0   00000F  2017-07-05 03:12:00+03        265700\n",
       "1   000012  2017-07-14 09:02:00+03         37900\n",
       "2   000068  2017-08-15 14:27:00+03         18100\n",
       "3   000181  2017-08-10 13:28:00+03        131800\n",
       "4   0002D8  2017-08-07 21:40:00+03         23600"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bookings = pd.read_sql_query('select * from bookings',connection)\n",
    "bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "081bf2fd-e932-49ac-95b8-1f442e793ceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(262788, 3)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bookings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6d7a334e-63c7-493b-b30c-225fec2f3780",
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flight_id</th>\n",
       "      <th>flight_no</th>\n",
       "      <th>scheduled_departure</th>\n",
       "      <th>scheduled_arrival</th>\n",
       "      <th>departure_airport</th>\n",
       "      <th>arrival_airport</th>\n",
       "      <th>status</th>\n",
       "      <th>aircraft_code</th>\n",
       "      <th>actual_departure</th>\n",
       "      <th>actual_arrival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1185</td>\n",
       "      <td>PG0134</td>\n",
       "      <td>2017-09-10 09:50:00+03</td>\n",
       "      <td>2017-09-10 14:55:00+03</td>\n",
       "      <td>DME</td>\n",
       "      <td>BTK</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>319</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3979</td>\n",
       "      <td>PG0052</td>\n",
       "      <td>2017-08-25 14:50:00+03</td>\n",
       "      <td>2017-08-25 17:35:00+03</td>\n",
       "      <td>VKO</td>\n",
       "      <td>HMA</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>CR2</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4739</td>\n",
       "      <td>PG0561</td>\n",
       "      <td>2017-09-05 12:30:00+03</td>\n",
       "      <td>2017-09-05 14:15:00+03</td>\n",
       "      <td>VKO</td>\n",
       "      <td>AER</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>763</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5502</td>\n",
       "      <td>PG0529</td>\n",
       "      <td>2017-09-12 09:50:00+03</td>\n",
       "      <td>2017-09-12 11:20:00+03</td>\n",
       "      <td>SVO</td>\n",
       "      <td>UFA</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>763</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6938</td>\n",
       "      <td>PG0461</td>\n",
       "      <td>2017-09-04 12:25:00+03</td>\n",
       "      <td>2017-09-04 13:20:00+03</td>\n",
       "      <td>SVO</td>\n",
       "      <td>ULV</td>\n",
       "      <td>Scheduled</td>\n",
       "      <td>SU9</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   flight_id flight_no     scheduled_departure       scheduled_arrival  \\\n",
       "0       1185    PG0134  2017-09-10 09:50:00+03  2017-09-10 14:55:00+03   \n",
       "1       3979    PG0052  2017-08-25 14:50:00+03  2017-08-25 17:35:00+03   \n",
       "2       4739    PG0561  2017-09-05 12:30:00+03  2017-09-05 14:15:00+03   \n",
       "3       5502    PG0529  2017-09-12 09:50:00+03  2017-09-12 11:20:00+03   \n",
       "4       6938    PG0461  2017-09-04 12:25:00+03  2017-09-04 13:20:00+03   \n",
       "\n",
       "  departure_airport arrival_airport     status aircraft_code actual_departure  \\\n",
       "0               DME             BTK  Scheduled           319               \\N   \n",
       "1               VKO             HMA  Scheduled           CR2               \\N   \n",
       "2               VKO             AER  Scheduled           763               \\N   \n",
       "3               SVO             UFA  Scheduled           763               \\N   \n",
       "4               SVO             ULV  Scheduled           SU9               \\N   \n",
       "\n",
       "  actual_arrival  \n",
       "0             \\N  \n",
       "1             \\N  \n",
       "2             \\N  \n",
       "3             \\N  \n",
       "4             \\N  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights = pd.read_sql_query('select * from flights',connection)\n",
    "flights.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c79eac5f-79e2-4ac1-846e-75ecaf9fbcf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aircraft_code</th>\n",
       "      <th>seat_no</th>\n",
       "      <th>fare_conditions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>319</td>\n",
       "      <td>2A</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>319</td>\n",
       "      <td>2C</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>319</td>\n",
       "      <td>2D</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>319</td>\n",
       "      <td>2F</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>319</td>\n",
       "      <td>3A</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  aircraft_code seat_no fare_conditions\n",
       "0           319      2A        Business\n",
       "1           319      2C        Business\n",
       "2           319      2D        Business\n",
       "3           319      2F        Business\n",
       "4           319      3A        Business"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seats = pd.read_sql_query('select * from seats',connection)\n",
    "seats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1fbd3e69-481d-4a1c-9376-c4db31c760fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1339, 3)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seats.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "823156b1-213a-47fb-a1f1-ce35e099cd89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticket_no</th>\n",
       "      <th>flight_id</th>\n",
       "      <th>fare_conditions</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0005432159776</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0005435212351</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0005435212386</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0005435212381</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0005432211370</td>\n",
       "      <td>30625</td>\n",
       "      <td>Business</td>\n",
       "      <td>42100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ticket_no  flight_id fare_conditions  amount\n",
       "0  0005432159776      30625        Business   42100\n",
       "1  0005435212351      30625        Business   42100\n",
       "2  0005435212386      30625        Business   42100\n",
       "3  0005435212381      30625        Business   42100\n",
       "4  0005432211370      30625        Business   42100"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ticket_flights = pd.read_sql_query('select * from ticket_flights',connection)\n",
    "ticket_flights.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2e1146ba-33fb-4ff3-8ed6-252d6420cde7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1045726, 4)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ticket_flights.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f70c0709-2480-461b-b6c1-daac6b2fff73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticket_no</th>\n",
       "      <th>book_ref</th>\n",
       "      <th>passenger_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0005432000987</td>\n",
       "      <td>06B046</td>\n",
       "      <td>8149 604011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0005432000988</td>\n",
       "      <td>06B046</td>\n",
       "      <td>8499 420203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0005432000989</td>\n",
       "      <td>E170C3</td>\n",
       "      <td>1011 752484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0005432000990</td>\n",
       "      <td>E170C3</td>\n",
       "      <td>4849 400049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0005432000991</td>\n",
       "      <td>F313DD</td>\n",
       "      <td>6615 976589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ticket_no book_ref passenger_id\n",
       "0  0005432000987   06B046  8149 604011\n",
       "1  0005432000988   06B046  8499 420203\n",
       "2  0005432000989   E170C3  1011 752484\n",
       "3  0005432000990   E170C3  4849 400049\n",
       "4  0005432000991   F313DD  6615 976589"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickets = pd.read_sql_query('select * from tickets',connection)\n",
    "tickets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "94c962a2-4d20-4dfc-8380-c15e41a22482",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(366733, 3)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickets.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f1a8d44a-069e-4139-ba5b-684adeaae253",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "aircrafts_data:\n",
      "\n",
      "('aircraft_code', 'character(3)')\n",
      "('model', 'jsonb')\n",
      "('range', 'INTEGER')\n",
      "\n",
      "\n",
      "airports_data:\n",
      "\n",
      "('airport_code', 'character(3)')\n",
      "('airport_name', 'jsonb')\n",
      "('city', 'jsonb')\n",
      "('coordinates', 'point')\n",
      "('timezone', 'TEXT')\n",
      "\n",
      "\n",
      "boarding_passes:\n",
      "\n",
      "('ticket_no', 'character(13)')\n",
      "('flight_id', 'INTEGER')\n",
      "('boarding_no', 'INTEGER')\n",
      "('seat_no', 'character varying(4)')\n",
      "\n",
      "\n",
      "bookings:\n",
      "\n",
      "('book_ref', 'character(6)')\n",
      "('book_date', 'timestamp with time zone')\n",
      "('total_amount', 'numeric(10,2)')\n",
      "\n",
      "\n",
      "flights:\n",
      "\n",
      "('flight_id', 'INTEGER')\n",
      "('flight_no', 'character(6)')\n",
      "('scheduled_departure', 'timestamp with time zone')\n",
      "('scheduled_arrival', 'timestamp with time zone')\n",
      "('departure_airport', 'character(3)')\n",
      "('arrival_airport', 'character(3)')\n",
      "('status', 'character varying(20)')\n",
      "('aircraft_code', 'character(3)')\n",
      "('actual_departure', 'timestamp with time zone')\n",
      "('actual_arrival', 'timestamp with time zone')\n",
      "\n",
      "\n",
      "seats:\n",
      "\n",
      "('aircraft_code', 'character(3)')\n",
      "('seat_no', 'character varying(4)')\n",
      "('fare_conditions', 'character varying(10)')\n",
      "\n",
      "\n",
      "ticket_flights:\n",
      "\n",
      "('ticket_no', 'character(13)')\n",
      "('flight_id', 'INTEGER')\n",
      "('fare_conditions', 'character varying(10)')\n",
      "('amount', 'numeric(10,2)')\n",
      "\n",
      "\n",
      "tickets:\n",
      "\n",
      "('ticket_no', 'character(13)')\n",
      "('book_ref', 'character(6)')\n",
      "('passenger_id', 'character varying(20)')\n"
     ]
    }
   ],
   "source": [
    "# getting the datatypes of the columns\n",
    "for table in table_list['name']:\n",
    "    print(f'\\n\\n{table}:\\n')\n",
    "    column_info = connection.execute(f\"PRAGMA table_info({table})\")\n",
    "    for column in column_info.fetchall():\n",
    "        print(column[1:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a26c1ee5-02a2-45dd-8c5a-2232ae7521e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "aircrafts_data:\n",
      "aircraft_code    0\n",
      "model            0\n",
      "range            0\n",
      "dtype: int64\n",
      "\n",
      "airports_data:\n",
      "airport_code    0\n",
      "airport_name    0\n",
      "city            0\n",
      "coordinates     0\n",
      "timezone        0\n",
      "dtype: int64\n",
      "\n",
      "boarding_passes:\n",
      "ticket_no      0\n",
      "flight_id      0\n",
      "boarding_no    0\n",
      "seat_no        0\n",
      "dtype: int64\n",
      "\n",
      "bookings:\n",
      "book_ref        0\n",
      "book_date       0\n",
      "total_amount    0\n",
      "dtype: int64\n",
      "\n",
      "flights:\n",
      "flight_id              0\n",
      "flight_no              0\n",
      "scheduled_departure    0\n",
      "scheduled_arrival      0\n",
      "departure_airport      0\n",
      "arrival_airport        0\n",
      "status                 0\n",
      "aircraft_code          0\n",
      "actual_departure       0\n",
      "actual_arrival         0\n",
      "dtype: int64\n",
      "\n",
      "seats:\n",
      "aircraft_code      0\n",
      "seat_no            0\n",
      "fare_conditions    0\n",
      "dtype: int64\n",
      "\n",
      "ticket_flights:\n",
      "ticket_no          0\n",
      "flight_id          0\n",
      "fare_conditions    0\n",
      "amount             0\n",
      "dtype: int64\n",
      "\n",
      "tickets:\n",
      "ticket_no       0\n",
      "book_ref        0\n",
      "passenger_id    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# checking for missing values\n",
    "for table in table_list['name']:\n",
    "    print(f'\\n{table}:')\n",
    "    df = pd.read_sql_query(f'select * from {table}',connection)\n",
    "    print(df.isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28fc7a34-d352-49ce-a0f0-b03431637905",
   "metadata": {},
   "source": [
    "# Basic Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b02ebeb-bdb2-4459-b746-ba9ea5b64567",
   "metadata": {},
   "source": [
    "#### Q1. How many planes have more than 100 seats?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "11f227df-5fc3-4428-a621-02877dbb3f5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aircraft_code</th>\n",
       "      <th>seats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>319</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>320</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>321</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>733</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>763</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>773</td>\n",
       "      <td>402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  aircraft_code  seats\n",
       "0           319    116\n",
       "1           320    140\n",
       "2           321    170\n",
       "3           733    130\n",
       "4           763    222\n",
       "5           773    402"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query(\"\"\"select aircraft_code, count(seat_no) as seats \n",
    "                    from seats group by aircraft_code having seats>100\"\"\", connection)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "833b496a-1e15-416f-b099-2573fda27c17",
   "metadata": {},
   "source": [
    "Hence, there are 6 planes having more than 100 seats."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ee715f6-8180-4a65-a66a-2fe41f8ca2cd",
   "metadata": {},
   "source": [
    "#### Q2. How the number of tickets booked and total amount earned changed with the time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "333f12e0-d125-452a-8925-aa5c2e9d7412",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tickets = pd.read_sql_query(\"\"\"select * from bookings b inner join tickets t on b.book_ref=t.book_ref\"\"\",connection)\n",
    "tickets['book_date'] = pd.to_datetime(tickets['book_date'])\n",
    "tickets['date']=tickets['book_date'].dt.date\n",
    "x = tickets.groupby('date')[['ticket_no']].count()\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(x.index,x['ticket_no'],marker='o')\n",
    "plt.xlabel('Date',fontsize=15)\n",
    "plt.ylabel('No. of Tickets',fontsize=15)\n",
    "plt.grid('b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d4e73204-bf71-47d0-887e-1d26409f2f3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bookings = pd.read_sql_query(\"\"\"select * from bookings\"\"\",connection)\n",
    "bookings['book_date'] = pd.to_datetime(bookings['book_date'])\n",
    "bookings['date']=bookings['book_date'].dt.date\n",
    "x = bookings.groupby('date')[['total_amount']].sum()\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(x.index,x['total_amount'],marker='o')\n",
    "plt.xlabel('Date',fontsize=15)\n",
    "plt.ylabel('Total Amount',fontsize=15)\n",
    "plt.grid('b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbc47e80-6b18-4717-b25a-887c1515aa9e",
   "metadata": {},
   "source": [
    "- Upon analysis of the line chart, we observe that the no. of tickets booked exhibits a gradual increase from June 22nd to July 7th with a small dip on July 8 followed by a relatively stable pattern from July 8th until August, with a noticeable peak  where the highest no. of tickets were booked on a single day. \n",
    "- We can see a similar trend in total revenue earned by the company throughout the analysed time period.\n",
    "- These findings suggest that further exploration of the factors contributing to the peak in ticket bookings may be beneficial for increasing overall revenue and optimizing operational strategies."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52376327-ab6c-48eb-a059-d58a5cc2f510",
   "metadata": {},
   "source": [
    "#### Q3. Calculate the average charges for each aircraft with different fare conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "17ab3d5d-1305-4227-b3dd-6be9d5d8b78a",
   "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>aircraft_code</th>\n",
       "      <th>fare_conditions</th>\n",
       "      <th>avg_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>319</td>\n",
       "      <td>Business</td>\n",
       "      <td>113550.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>319</td>\n",
       "      <td>Economy</td>\n",
       "      <td>38311.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>321</td>\n",
       "      <td>Business</td>\n",
       "      <td>34435.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>321</td>\n",
       "      <td>Economy</td>\n",
       "      <td>11534.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>733</td>\n",
       "      <td>Business</td>\n",
       "      <td>41865.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>733</td>\n",
       "      <td>Economy</td>\n",
       "      <td>13985.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>763</td>\n",
       "      <td>Business</td>\n",
       "      <td>82839.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>763</td>\n",
       "      <td>Economy</td>\n",
       "      <td>27594.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>773</td>\n",
       "      <td>Business</td>\n",
       "      <td>57779.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>773</td>\n",
       "      <td>Comfort</td>\n",
       "      <td>32740.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>773</td>\n",
       "      <td>Economy</td>\n",
       "      <td>19265.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CN1</td>\n",
       "      <td>Economy</td>\n",
       "      <td>6568.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CR2</td>\n",
       "      <td>Economy</td>\n",
       "      <td>13207.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>SU9</td>\n",
       "      <td>Business</td>\n",
       "      <td>33487.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SU9</td>\n",
       "      <td>Economy</td>\n",
       "      <td>11220.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   aircraft_code fare_conditions  avg_amount\n",
       "0            319        Business   113550.56\n",
       "1            319         Economy    38311.40\n",
       "2            321        Business    34435.66\n",
       "3            321         Economy    11534.97\n",
       "4            733        Business    41865.63\n",
       "5            733         Economy    13985.15\n",
       "6            763        Business    82839.84\n",
       "7            763         Economy    27594.72\n",
       "8            773        Business    57779.91\n",
       "9            773         Comfort    32740.55\n",
       "10           773         Economy    19265.23\n",
       "11           CN1         Economy     6568.55\n",
       "12           CR2         Economy    13207.66\n",
       "13           SU9        Business    33487.85\n",
       "14           SU9         Economy    11220.18"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_sql_query(\"\"\"select aircraft_code,fare_conditions,round(avg(amount),2) as avg_amount from ticket_flights tf inner join flights f on tf.flight_id=f.flight_id group by aircraft_code, fare_conditions\"\"\",connection)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bbfbed30-ae2c-477f-851e-09a4afcb3dd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='aircraft_code', ylabel='avg_amount'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df, x='aircraft_code',y='avg_amount',hue='fare_conditions')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "757f0306-ff75-4e17-babd-1fb4d244e2b8",
   "metadata": {},
   "source": [
    "- From the above bar plot, we observe that the comfort class is only available on aircraft with code 773.\n",
    "- Aircrafts with codes CN1 and CR2 only provide economy class.\n",
    "- The charges for business class is consistently greater than those for economy class across all the planes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9594019f-74ea-4975-bbcb-910b8f71552b",
   "metadata": {},
   "source": [
    "The basic analysis of data provides insights into the no. of planes with more than 100 seats, how the no. of tickets booked and total amount earned changed over time, and the average fare for each aircraft with different fare conditions. These findings will be helpful in developing strategies to increase occupancy rates and optimize pricing for each aircraft."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0cedd79-ae7c-4786-b05c-8aeb040fe903",
   "metadata": {},
   "source": [
    "# Analyzing occupancy rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dad81a1-fa1a-413e-b133-24179bec3310",
   "metadata": {},
   "source": [
    "#### Q4. For each aircraft, calculate the total revenue per year and the average revenue per ticket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "16b71e14-dc04-4b2a-832c-29596f3bf467",
   "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>aircraft_code</th>\n",
       "      <th>total_revenue</th>\n",
       "      <th>avg_revenue_per_ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>319</td>\n",
       "      <td>2706163100</td>\n",
       "      <td>51201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>321</td>\n",
       "      <td>1638164100</td>\n",
       "      <td>15291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>733</td>\n",
       "      <td>1426552100</td>\n",
       "      <td>16568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>763</td>\n",
       "      <td>4371277100</td>\n",
       "      <td>35033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>773</td>\n",
       "      <td>3431205500</td>\n",
       "      <td>23765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CN1</td>\n",
       "      <td>96373800</td>\n",
       "      <td>6568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CR2</td>\n",
       "      <td>1982760500</td>\n",
       "      <td>13207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SU9</td>\n",
       "      <td>5114484700</td>\n",
       "      <td>13985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  aircraft_code  total_revenue  avg_revenue_per_ticket\n",
       "0           319     2706163100                   51201\n",
       "1           321     1638164100                   15291\n",
       "2           733     1426552100                   16568\n",
       "3           763     4371277100                   35033\n",
       "4           773     3431205500                   23765\n",
       "5           CN1       96373800                    6568\n",
       "6           CR2     1982760500                   13207\n",
       "7           SU9     5114484700                   13985"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query(\"\"\"select aircraft_code, sum(amount) as total_revenue, (sum(amount)/count(ticket_no)) as avg_revenue_per_ticket\n",
    "from flights f inner join ticket_flights tf on f.flight_id=tf.flight_id group by aircraft_code\"\"\",connection)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1bb0b02-187a-4288-95f3-90d98bab6705",
   "metadata": {},
   "source": [
    "- From the above table we observe that the aircraft with the highest total revenue is SU9 but average revenue per ticket is low. It might be because the price of business class and economy class is lowest in this aircraft which can be seen from the above bar plot. So, most people bought this aircraft ticket as it is less costly as compared to others.\n",
    "- On the other hand CN1 aircraft has lowest total_revenue. It could be because it only offers economy class with the lowest price and it might be because of its poor conditions and facilities."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf5f85f9-3ea4-4aec-bb49-a2c756497c5f",
   "metadata": {},
   "source": [
    "The average occupancy per aircraft is another critical metric to consider. Higher occupancy rates can help airlines increase revenue and profitability while lowering operational expenses associated with vacant seats. Pricing strategy, airline schedules, and customer satisfaction are all factors that might influence occupancy rates.\n",
    "\n",
    "The occupancy rate is calculated by dividing the average of no. of booked seats for all the flights of an aircraft by total no. of seats in that aircraft."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e9e285d-aaeb-4b6c-be38-7add1f65893c",
   "metadata": {},
   "source": [
    "#### Q5. Calculate the average occupancy per aircraft."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39d57e2c-6aeb-4793-9e63-311884816a1c",
   "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>aircraft_code</th>\n",
       "      <th>occupancy_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>319</td>\n",
       "      <td>46.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>321</td>\n",
       "      <td>52.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>733</td>\n",
       "      <td>61.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>763</td>\n",
       "      <td>51.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>773</td>\n",
       "      <td>65.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CN1</td>\n",
       "      <td>50.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CR2</td>\n",
       "      <td>42.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SU9</td>\n",
       "      <td>58.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  aircraft_code  occupancy_rate\n",
       "0           319           46.19\n",
       "1           321           52.24\n",
       "2           733           61.73\n",
       "3           763           51.32\n",
       "4           773           65.90\n",
       "5           CN1           50.04\n",
       "6           CR2           42.97\n",
       "7           SU9           58.57"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occupancy_rate = pd.read_sql_query(\"\"\"select a.aircraft_code, round((avg(booked_seats)/seat_count)*100,2) as occupancy_rate\n",
    "from (select s.aircraft_code, count(seat_no) as seat_count\n",
    "from seats s group by s.aircraft_code) as a inner join \n",
    "(select f.aircraft_code, f.flight_id, count(bp.seat_no) as booked_seats\n",
    "from boarding_passes bp inner join flights f \n",
    "on bp.flight_id=f.flight_id group by f.aircraft_code,f.flight_id) as b\n",
    "on a.aircraft_code=b.aircraft_code group by a.aircraft_code\"\"\",connection)\n",
    "occupancy_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10534394-146b-435d-876b-0c7fc6edaed6",
   "metadata": {},
   "source": [
    "- Airlines can access how much theirtotal annual turnover could improve by increasing the occupancy rate by 10% for all the aircrafts to further examine the possible benifits of raising occupancy rates.\n",
    "- Airlines may enhance occupancy rates and revenue while delivering greater value and service to the customers by optimizing pricing strategies and other operational considerations. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8128bb5e-b7ed-4238-82b0-eac0bb80ed53",
   "metadata": {},
   "source": [
    "#### Q6. Calculate by how much the total annual turnover could increase by giving all aircraft a 10% higher occupancy rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "94499733-edf3-4f79-95aa-27d965110a23",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.float_format',str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c25a0923-f3b7-4391-8ca4-352417510307",
   "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>aircraft_code</th>\n",
       "      <th>occupancy_rate</th>\n",
       "      <th>Inc_occupancy_rate</th>\n",
       "      <th>Inc_in_total_annual_turnover</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>319</td>\n",
       "      <td>46.19</td>\n",
       "      <td>50.809</td>\n",
       "      <td>270616310.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>321</td>\n",
       "      <td>52.24</td>\n",
       "      <td>57.464</td>\n",
       "      <td>163816410.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>733</td>\n",
       "      <td>61.73</td>\n",
       "      <td>67.90299999999999</td>\n",
       "      <td>142655210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>763</td>\n",
       "      <td>51.32</td>\n",
       "      <td>56.452</td>\n",
       "      <td>437127710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>773</td>\n",
       "      <td>65.9</td>\n",
       "      <td>72.49000000000001</td>\n",
       "      <td>343120550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CN1</td>\n",
       "      <td>50.04</td>\n",
       "      <td>55.044</td>\n",
       "      <td>9637379.999999985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CR2</td>\n",
       "      <td>42.97</td>\n",
       "      <td>47.266999999999996</td>\n",
       "      <td>198276050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SU9</td>\n",
       "      <td>58.57</td>\n",
       "      <td>64.427</td>\n",
       "      <td>511448470.00000095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  aircraft_code  occupancy_rate  Inc_occupancy_rate  \\\n",
       "0           319           46.19              50.809   \n",
       "1           321           52.24              57.464   \n",
       "2           733           61.73   67.90299999999999   \n",
       "3           763           51.32              56.452   \n",
       "4           773            65.9   72.49000000000001   \n",
       "5           CN1           50.04              55.044   \n",
       "6           CR2           42.97  47.266999999999996   \n",
       "7           SU9           58.57              64.427   \n",
       "\n",
       "   Inc_in_total_annual_turnover  \n",
       "0                   270616310.0  \n",
       "1                   163816410.0  \n",
       "2                   142655210.0  \n",
       "3                   437127710.0  \n",
       "4                   343120550.0  \n",
       "5             9637379.999999985  \n",
       "6                   198276050.0  \n",
       "7            511448470.00000095  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occupancy_rate['Inc_occupancy_rate'] = occupancy_rate['occupancy_rate']+occupancy_rate['occupancy_rate']*0.1\n",
    "total_revenue = pd.read_sql_query(\"\"\"select aircraft_code, sum(amount) as total_revenue\n",
    "from ticket_flights tf inner join flights f on tf.flight_id=f.flight_id group by aircraft_code\"\"\",connection)\n",
    "occupancy_rate['Inc_in_total_annual_turnover']=((total_revenue['total_revenue']/occupancy_rate['occupancy_rate'])*occupancy_rate['Inc_occupancy_rate'])-total_revenue['total_revenue']\n",
    "occupancy_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "debeb5e7-201d-4e0d-a1a5-a250e2d40c29",
   "metadata": {},
   "source": [
    "The above table shows how the total revenue increased after increasing occupancy rate by 10%."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eef8a4f9-a3bf-4907-9429-0ef4d3780ff4",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfa3cf3c-5de1-4faf-abee-0b30b09ed5d7",
   "metadata": {},
   "source": [
    "- To summarize, analyzing revenue data such as total revenue per year, average revenue per ticket, and average occupancy per aircraft is critical for airlines seeking to maximize profitability.\n",
    "- Airlines can find areas for improvement and modify their pricing strategies and route plans to increase their profitability.\n",
    "- A greater occupancy rate is one important feature that can enhance profitability since it allows airlines to maximize revenue while minimizing costs associated with vacant seats.\n",
    "- The airline should revise the price for each aircraft as very low price and very high price is also a factor that people are not purchasing tickets for some aircrafts. They shold decide a reasonable price according to the condition and facility the aircraft is providing so that it should not be very cheap or very costly.\n",
    "- Also, profit should not come at the price of consumer satisfaction and safety. So, airlines must strike a balance b/w the necessity for profit and the significance of delivering high-quality service and upholding safety regulations. Balancing these factors is key to long term success in the competitive airline industry.\n",
    "- Adopting a data-driven approach to revenue analysis and optimization can lead to sustainable growth and success."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e07a358-ff1c-4d80-99ea-aa6a3ef1b11d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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