├── Presentation.pptx
├── Data
    └── customer_booking.csv
├── completion_certificate.pdf
├── Predicitve Model Results.pptx
├── Predictive Modeling
    ├── customer_booking.csv
    └── .ipynb_checkpoints
    │   └── Explore-customer-data-checkpoint.ipynb
├── README.md
├── Data Collection
    ├── getting_started.ipynb
    ├── .ipynb_checkpoints
    │   ├── getting_started-checkpoint.ipynb
    │   └── Data-collection-checkpoint.ipynb
    └── Data-collection.ipynb
└── Data Cleaning
    ├── Data Cleaning.ipynb
    └── .ipynb_checkpoints
        └── Data Cleaning-checkpoint.ipynb


/Presentation.pptx:
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https://raw.githubusercontent.com/hseju/British-Airways-Good-or-Bad/HEAD/Presentation.pptx


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/Data/customer_booking.csv:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/hseju/British-Airways-Good-or-Bad/HEAD/Data/customer_booking.csv


--------------------------------------------------------------------------------
/completion_certificate.pdf:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/hseju/British-Airways-Good-or-Bad/HEAD/completion_certificate.pdf


--------------------------------------------------------------------------------
/Predicitve Model Results.pptx:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/hseju/British-Airways-Good-or-Bad/HEAD/Predicitve Model Results.pptx


--------------------------------------------------------------------------------
/Predictive Modeling/customer_booking.csv:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/hseju/British-Airways-Good-or-Bad/HEAD/Predictive Modeling/customer_booking.csv


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # British Airways Good or Bad?
 2 | 
 3 | This project is a part of the Data Science virtual internship program offered by Forage with British Airways. 
 4 | 
 5 | ## The virtual Internship is divided into two main tasks 
 6 | 
 7 | 1. Web scraping to gain company insights
 8 | 2. Predicting customer buying behaviour
 9 | 
10 | <hr>
11 | 
12 | ### Task 1 - Web scraping to gain company insights
13 | 
14 | Customers who book a flight with BA will experience many interaction points with the BA brand. Understanding a customer's feelings, needs, and feedback is crucial for any business, including BA.
15 | 
16 | This first task is focused on scraping and collecting customer feedback and reviewing data from a third-party source and analysing this data to present any insights you may uncover.
17 | 
18 | Customer review data for Britis Airways was collected from [Skytrax](https://www.airlinequality.com/airline-reviews/british-airways).
19 | 
20 | Following insights were uncovered as they are summed up in the one slide presentation.
21 | 
22 | ![image](https://user-images.githubusercontent.com/89634505/201470985-159e17d2-605d-46c1-a9f1-8d0cdd147245.png)
23 | 
24 | ### Task 2 - Predicting customer buying behaviour
25 | 
26 | Customers are more empowered than ever because they have access to a wealth of information at their fingertips. This is one of the reasons the buying cycle is very different to what it used to be. Today, if you’re hoping that a customer purchases your flights or holidays as they come into the airport, you’ve already lost! Being reactive in this situation is not ideal; airlines must be proactive in order to acquire customers before they embark on their holiday.
27 | 
28 | This task involves building a high quality predictive to predict the successful bookings using customer bookings data.
29 | 
30 | ![image](https://user-images.githubusercontent.com/89634505/201471191-cdd85024-1691-4136-b9f8-b4b1d8d42f72.png)
31 | 


--------------------------------------------------------------------------------
/Data Collection/getting_started.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Task 1\n",
  8 |     "\n",
  9 |     "---\n",
 10 |     "\n",
 11 |     "## Web scraping and analysis\n",
 12 |     "\n",
 13 |     "This Jupyter notebook includes some code to get you started with web scraping. We will use a package called `BeautifulSoup` to collect the data from the web. Once you've collected your data and saved it into a local `.csv` file you should start with your analysis.\n",
 14 |     "\n",
 15 |     "### Scraping data from Skytrax\n",
 16 |     "\n",
 17 |     "If you visit [https://www.airlinequality.com] you can see that there is a lot of data there. For this task, we are only interested in reviews related to British Airways and the Airline itself.\n",
 18 |     "\n",
 19 |     "If you navigate to this link: [https://www.airlinequality.com/airline-reviews/british-airways] you will see this data. Now, we can use `Python` and `BeautifulSoup` to collect all the links to the reviews and then to collect the text data on each of the individual review links."
 20 |    ]
 21 |   },
 22 |   {
 23 |    "cell_type": "code",
 24 |    "execution_count": 16,
 25 |    "metadata": {},
 26 |    "outputs": [],
 27 |    "source": [
 28 |     "import requests\n",
 29 |     "from bs4 import BeautifulSoup\n",
 30 |     "import pandas as pd"
 31 |    ]
 32 |   },
 33 |   {
 34 |    "cell_type": "code",
 35 |    "execution_count": 14,
 36 |    "metadata": {},
 37 |    "outputs": [
 38 |     {
 39 |      "name": "stdout",
 40 |      "output_type": "stream",
 41 |      "text": [
 42 |       "Scraping page 1\n",
 43 |       "   ---> 100 total reviews\n",
 44 |       "Scraping page 2\n",
 45 |       "   ---> 200 total reviews\n",
 46 |       "Scraping page 3\n",
 47 |       "   ---> 300 total reviews\n",
 48 |       "Scraping page 4\n",
 49 |       "   ---> 400 total reviews\n",
 50 |       "Scraping page 5\n",
 51 |       "   ---> 500 total reviews\n",
 52 |       "Scraping page 6\n",
 53 |       "   ---> 600 total reviews\n",
 54 |       "Scraping page 7\n",
 55 |       "   ---> 700 total reviews\n",
 56 |       "Scraping page 8\n",
 57 |       "   ---> 800 total reviews\n",
 58 |       "Scraping page 9\n",
 59 |       "   ---> 900 total reviews\n",
 60 |       "Scraping page 10\n",
 61 |       "   ---> 1000 total reviews\n"
 62 |      ]
 63 |     }
 64 |    ],
 65 |    "source": [
 66 |     "base_url = \"https://www.airlinequality.com/airline-reviews/british-airways\"\n",
 67 |     "pages = 10\n",
 68 |     "page_size = 100\n",
 69 |     "\n",
 70 |     "reviews = []\n",
 71 |     "\n",
 72 |     "# for i in range(1, pages + 1):\n",
 73 |     "for i in range(1, pages + 1):\n",
 74 |     "\n",
 75 |     "    print(f\"Scraping page {i}\")\n",
 76 |     "\n",
 77 |     "    # Create URL to collect links from paginated data\n",
 78 |     "    url = f\"{base_url}/page/{i}/?sortby=post_date%3ADesc&pagesize={page_size}\"\n",
 79 |     "\n",
 80 |     "    # Collect HTML data from this page\n",
 81 |     "    response = requests.get(url)\n",
 82 |     "\n",
 83 |     "    # Parse content\n",
 84 |     "    content = response.content\n",
 85 |     "    parsed_content = BeautifulSoup(content, 'html.parser')\n",
 86 |     "    for para in parsed_content.find_all(\"div\", {\"class\": \"text_content\"}):\n",
 87 |     "        reviews.append(para.get_text())\n",
 88 |     "    \n",
 89 |     "    print(f\"   ---> {len(reviews)} total reviews\")"
 90 |    ]
 91 |   },
 92 |   {
 93 |    "cell_type": "code",
 94 |    "execution_count": 17,
 95 |    "metadata": {},
 96 |    "outputs": [
 97 |     {
 98 |      "data": {
 99 |       "text/html": [
100 |        "<div>\n",
101 |        "<style scoped>\n",
102 |        "    .dataframe tbody tr th:only-of-type {\n",
103 |        "        vertical-align: middle;\n",
104 |        "    }\n",
105 |        "\n",
106 |        "    .dataframe tbody tr th {\n",
107 |        "        vertical-align: top;\n",
108 |        "    }\n",
109 |        "\n",
110 |        "    .dataframe thead th {\n",
111 |        "        text-align: right;\n",
112 |        "    }\n",
113 |        "</style>\n",
114 |        "<table border=\"1\" class=\"dataframe\">\n",
115 |        "  <thead>\n",
116 |        "    <tr style=\"text-align: right;\">\n",
117 |        "      <th></th>\n",
118 |        "      <th>reviews</th>\n",
119 |        "    </tr>\n",
120 |        "  </thead>\n",
121 |        "  <tbody>\n",
122 |        "    <tr>\n",
123 |        "      <th>0</th>\n",
124 |        "      <td>Not Verified |  LHR-LAX. I prefer the Boeing 7...</td>\n",
125 |        "    </tr>\n",
126 |        "    <tr>\n",
127 |        "      <th>1</th>\n",
128 |        "      <td>✅ Trip Verified |  Flew back to UK from Miami ...</td>\n",
129 |        "    </tr>\n",
130 |        "    <tr>\n",
131 |        "      <th>2</th>\n",
132 |        "      <td>✅ Trip Verified |  I flew with hand baggage bu...</td>\n",
133 |        "    </tr>\n",
134 |        "    <tr>\n",
135 |        "      <th>3</th>\n",
136 |        "      <td>Not Verified | London to Cairo. First, on this...</td>\n",
137 |        "    </tr>\n",
138 |        "    <tr>\n",
139 |        "      <th>4</th>\n",
140 |        "      <td>✅ Trip Verified |  This review is specifically...</td>\n",
141 |        "    </tr>\n",
142 |        "  </tbody>\n",
143 |        "</table>\n",
144 |        "</div>"
145 |       ],
146 |       "text/plain": [
147 |        "                                             reviews\n",
148 |        "0  Not Verified |  LHR-LAX. I prefer the Boeing 7...\n",
149 |        "1  ✅ Trip Verified |  Flew back to UK from Miami ...\n",
150 |        "2  ✅ Trip Verified |  I flew with hand baggage bu...\n",
151 |        "3  Not Verified | London to Cairo. First, on this...\n",
152 |        "4  ✅ Trip Verified |  This review is specifically..."
153 |       ]
154 |      },
155 |      "execution_count": 17,
156 |      "metadata": {},
157 |      "output_type": "execute_result"
158 |     }
159 |    ],
160 |    "source": [
161 |     "df = pd.DataFrame()\n",
162 |     "df[\"reviews\"] = reviews\n",
163 |     "df.head()"
164 |    ]
165 |   },
166 |   {
167 |    "cell_type": "code",
168 |    "execution_count": 18,
169 |    "metadata": {},
170 |    "outputs": [],
171 |    "source": [
172 |     "df.to_csv(\"data/BA_reviews.csv\")"
173 |    ]
174 |   },
175 |   {
176 |    "cell_type": "markdown",
177 |    "metadata": {},
178 |    "source": [
179 |     "Congratulations! Now you have your dataset for this task! The loops above collected 1000 reviews by iterating through the paginated pages on the website. However, if you want to collect more data, try increasing the number of pages!\n",
180 |     "\n",
181 |     " The next thing that you should do is clean this data to remove any unnecessary text from each of the rows. For example, \"✅ Trip Verified\" can be removed from each row if it exists, as it's not relevant to what we want to investigate."
182 |    ]
183 |   },
184 |   {
185 |    "cell_type": "code",
186 |    "execution_count": null,
187 |    "metadata": {},
188 |    "outputs": [],
189 |    "source": []
190 |   }
191 |  ],
192 |  "metadata": {
193 |   "kernelspec": {
194 |    "display_name": "Python 3 (ipykernel)",
195 |    "language": "python",
196 |    "name": "python3"
197 |   },
198 |   "language_info": {
199 |    "codemirror_mode": {
200 |     "name": "ipython",
201 |     "version": 3
202 |    },
203 |    "file_extension": ".py",
204 |    "mimetype": "text/x-python",
205 |    "name": "python",
206 |    "nbconvert_exporter": "python",
207 |    "pygments_lexer": "ipython3",
208 |    "version": "3.9.7"
209 |   },
210 |   "vscode": {
211 |    "interpreter": {
212 |     "hash": "4f7924c4c56b083e0e50eadfe7ef592a7a8ef70df33a0047f82280e6be1afe15"
213 |    }
214 |   }
215 |  },
216 |  "nbformat": 4,
217 |  "nbformat_minor": 4
218 | }
219 | 


--------------------------------------------------------------------------------
/Data Collection/.ipynb_checkpoints/getting_started-checkpoint.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Task 1\n",
  8 |     "\n",
  9 |     "---\n",
 10 |     "\n",
 11 |     "## Web scraping and analysis\n",
 12 |     "\n",
 13 |     "This Jupyter notebook includes some code to get you started with web scraping. We will use a package called `BeautifulSoup` to collect the data from the web. Once you've collected your data and saved it into a local `.csv` file you should start with your analysis.\n",
 14 |     "\n",
 15 |     "### Scraping data from Skytrax\n",
 16 |     "\n",
 17 |     "If you visit [https://www.airlinequality.com] you can see that there is a lot of data there. For this task, we are only interested in reviews related to British Airways and the Airline itself.\n",
 18 |     "\n",
 19 |     "If you navigate to this link: [https://www.airlinequality.com/airline-reviews/british-airways] you will see this data. Now, we can use `Python` and `BeautifulSoup` to collect all the links to the reviews and then to collect the text data on each of the individual review links."
 20 |    ]
 21 |   },
 22 |   {
 23 |    "cell_type": "code",
 24 |    "execution_count": 16,
 25 |    "metadata": {},
 26 |    "outputs": [],
 27 |    "source": [
 28 |     "import requests\n",
 29 |     "from bs4 import BeautifulSoup\n",
 30 |     "import pandas as pd"
 31 |    ]
 32 |   },
 33 |   {
 34 |    "cell_type": "code",
 35 |    "execution_count": 14,
 36 |    "metadata": {},
 37 |    "outputs": [
 38 |     {
 39 |      "name": "stdout",
 40 |      "output_type": "stream",
 41 |      "text": [
 42 |       "Scraping page 1\n",
 43 |       "   ---> 100 total reviews\n",
 44 |       "Scraping page 2\n",
 45 |       "   ---> 200 total reviews\n",
 46 |       "Scraping page 3\n",
 47 |       "   ---> 300 total reviews\n",
 48 |       "Scraping page 4\n",
 49 |       "   ---> 400 total reviews\n",
 50 |       "Scraping page 5\n",
 51 |       "   ---> 500 total reviews\n",
 52 |       "Scraping page 6\n",
 53 |       "   ---> 600 total reviews\n",
 54 |       "Scraping page 7\n",
 55 |       "   ---> 700 total reviews\n",
 56 |       "Scraping page 8\n",
 57 |       "   ---> 800 total reviews\n",
 58 |       "Scraping page 9\n",
 59 |       "   ---> 900 total reviews\n",
 60 |       "Scraping page 10\n",
 61 |       "   ---> 1000 total reviews\n"
 62 |      ]
 63 |     }
 64 |    ],
 65 |    "source": [
 66 |     "base_url = \"https://www.airlinequality.com/airline-reviews/british-airways\"\n",
 67 |     "pages = 10\n",
 68 |     "page_size = 100\n",
 69 |     "\n",
 70 |     "reviews = []\n",
 71 |     "\n",
 72 |     "# for i in range(1, pages + 1):\n",
 73 |     "for i in range(1, pages + 1):\n",
 74 |     "\n",
 75 |     "    print(f\"Scraping page {i}\")\n",
 76 |     "\n",
 77 |     "    # Create URL to collect links from paginated data\n",
 78 |     "    url = f\"{base_url}/page/{i}/?sortby=post_date%3ADesc&pagesize={page_size}\"\n",
 79 |     "\n",
 80 |     "    # Collect HTML data from this page\n",
 81 |     "    response = requests.get(url)\n",
 82 |     "\n",
 83 |     "    # Parse content\n",
 84 |     "    content = response.content\n",
 85 |     "    parsed_content = BeautifulSoup(content, 'html.parser')\n",
 86 |     "    for para in parsed_content.find_all(\"div\", {\"class\": \"text_content\"}):\n",
 87 |     "        reviews.append(para.get_text())\n",
 88 |     "    \n",
 89 |     "    print(f\"   ---> {len(reviews)} total reviews\")"
 90 |    ]
 91 |   },
 92 |   {
 93 |    "cell_type": "code",
 94 |    "execution_count": 17,
 95 |    "metadata": {},
 96 |    "outputs": [
 97 |     {
 98 |      "data": {
 99 |       "text/html": [
100 |        "<div>\n",
101 |        "<style scoped>\n",
102 |        "    .dataframe tbody tr th:only-of-type {\n",
103 |        "        vertical-align: middle;\n",
104 |        "    }\n",
105 |        "\n",
106 |        "    .dataframe tbody tr th {\n",
107 |        "        vertical-align: top;\n",
108 |        "    }\n",
109 |        "\n",
110 |        "    .dataframe thead th {\n",
111 |        "        text-align: right;\n",
112 |        "    }\n",
113 |        "</style>\n",
114 |        "<table border=\"1\" class=\"dataframe\">\n",
115 |        "  <thead>\n",
116 |        "    <tr style=\"text-align: right;\">\n",
117 |        "      <th></th>\n",
118 |        "      <th>reviews</th>\n",
119 |        "    </tr>\n",
120 |        "  </thead>\n",
121 |        "  <tbody>\n",
122 |        "    <tr>\n",
123 |        "      <th>0</th>\n",
124 |        "      <td>Not Verified |  LHR-LAX. I prefer the Boeing 7...</td>\n",
125 |        "    </tr>\n",
126 |        "    <tr>\n",
127 |        "      <th>1</th>\n",
128 |        "      <td>✅ Trip Verified |  Flew back to UK from Miami ...</td>\n",
129 |        "    </tr>\n",
130 |        "    <tr>\n",
131 |        "      <th>2</th>\n",
132 |        "      <td>✅ Trip Verified |  I flew with hand baggage bu...</td>\n",
133 |        "    </tr>\n",
134 |        "    <tr>\n",
135 |        "      <th>3</th>\n",
136 |        "      <td>Not Verified | London to Cairo. First, on this...</td>\n",
137 |        "    </tr>\n",
138 |        "    <tr>\n",
139 |        "      <th>4</th>\n",
140 |        "      <td>✅ Trip Verified |  This review is specifically...</td>\n",
141 |        "    </tr>\n",
142 |        "  </tbody>\n",
143 |        "</table>\n",
144 |        "</div>"
145 |       ],
146 |       "text/plain": [
147 |        "                                             reviews\n",
148 |        "0  Not Verified |  LHR-LAX. I prefer the Boeing 7...\n",
149 |        "1  ✅ Trip Verified |  Flew back to UK from Miami ...\n",
150 |        "2  ✅ Trip Verified |  I flew with hand baggage bu...\n",
151 |        "3  Not Verified | London to Cairo. First, on this...\n",
152 |        "4  ✅ Trip Verified |  This review is specifically..."
153 |       ]
154 |      },
155 |      "execution_count": 17,
156 |      "metadata": {},
157 |      "output_type": "execute_result"
158 |     }
159 |    ],
160 |    "source": [
161 |     "df = pd.DataFrame()\n",
162 |     "df[\"reviews\"] = reviews\n",
163 |     "df.head()"
164 |    ]
165 |   },
166 |   {
167 |    "cell_type": "code",
168 |    "execution_count": 18,
169 |    "metadata": {},
170 |    "outputs": [],
171 |    "source": [
172 |     "df.to_csv(\"data/BA_reviews.csv\")"
173 |    ]
174 |   },
175 |   {
176 |    "cell_type": "markdown",
177 |    "metadata": {},
178 |    "source": [
179 |     "Congratulations! Now you have your dataset for this task! The loops above collected 1000 reviews by iterating through the paginated pages on the website. However, if you want to collect more data, try increasing the number of pages!\n",
180 |     "\n",
181 |     " The next thing that you should do is clean this data to remove any unnecessary text from each of the rows. For example, \"✅ Trip Verified\" can be removed from each row if it exists, as it's not relevant to what we want to investigate."
182 |    ]
183 |   },
184 |   {
185 |    "cell_type": "code",
186 |    "execution_count": null,
187 |    "metadata": {},
188 |    "outputs": [],
189 |    "source": []
190 |   }
191 |  ],
192 |  "metadata": {
193 |   "kernelspec": {
194 |    "display_name": "Python 3.9.13 ('venv': venv)",
195 |    "language": "python",
196 |    "name": "python3"
197 |   },
198 |   "language_info": {
199 |    "codemirror_mode": {
200 |     "name": "ipython",
201 |     "version": 3
202 |    },
203 |    "file_extension": ".py",
204 |    "mimetype": "text/x-python",
205 |    "name": "python",
206 |    "nbconvert_exporter": "python",
207 |    "pygments_lexer": "ipython3",
208 |    "version": "3.9.13"
209 |   },
210 |   "orig_nbformat": 4,
211 |   "vscode": {
212 |    "interpreter": {
213 |     "hash": "4f7924c4c56b083e0e50eadfe7ef592a7a8ef70df33a0047f82280e6be1afe15"
214 |    }
215 |   }
216 |  },
217 |  "nbformat": 4,
218 |  "nbformat_minor": 2
219 | }
220 | 


--------------------------------------------------------------------------------
/Data Collection/.ipynb_checkpoints/Data-collection-checkpoint.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "8d47a312-9fd2-44a8-b43e-aaaddaf670c4",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "## Data Collection\n",
  9 |     "\n",
 10 |     "In this phase we will collect the customer ratings data from the airline quality website called [Skytrax](https://www.airlinequality.com/airline-reviews/british-airways). We will collect data about airline ratings, seat ratings and lounge experience ratings from this website. "
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 199,
 16 |    "id": "5f43dc5f-ffc2-4ba0-8a73-3b70e9ab0fcf",
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "#imports\n",
 21 |     "\n",
 22 |     "import pandas as pd\n",
 23 |     "import numpy as np\n",
 24 |     "from bs4 import BeautifulSoup\n",
 25 |     "import requests "
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "code",
 30 |    "execution_count": 200,
 31 |    "id": "d5c2c023-7500-4e89-94f7-ddff4864d83c",
 32 |    "metadata": {},
 33 |    "outputs": [],
 34 |    "source": [
 35 |     "#create an empty list to collect all reviews\n",
 36 |     "reviews  = []\n",
 37 |     "\n",
 38 |     "#create an empty list to collect rating stars\n",
 39 |     "stars = []\n",
 40 |     "\n",
 41 |     "#create an empty list to collect date\n",
 42 |     "date = []\n",
 43 |     "\n",
 44 |     "#create an empty list to collect country the reviewer is from\n",
 45 |     "country = []"
 46 |    ]
 47 |   },
 48 |   {
 49 |    "cell_type": "code",
 50 |    "execution_count": 201,
 51 |    "id": "2894e93a-cbd4-45b5-b262-95f88994d83a",
 52 |    "metadata": {},
 53 |    "outputs": [
 54 |     {
 55 |      "name": "stdout",
 56 |      "output_type": "stream",
 57 |      "text": [
 58 |       "Error on page 29\n",
 59 |       "Error on page 30\n",
 60 |       "Error on page 30\n",
 61 |       "Error on page 33\n",
 62 |       "Error on page 33\n"
 63 |      ]
 64 |     }
 65 |    ],
 66 |    "source": [
 67 |     "for i in range(1, 36):\n",
 68 |     "    page = requests.get(f\"https://www.airlinequality.com/airline-reviews/british-airways/page/{i}/?sortby=post_date%3ADesc&pagesize=100\")\n",
 69 |     "    \n",
 70 |     "    soup = BeautifulSoup(page.content, \"html5\")\n",
 71 |     "    \n",
 72 |     "    for item in soup.find_all(\"div\", class_=\"text_content\"):\n",
 73 |     "        reviews.append(item.text)\n",
 74 |     "    \n",
 75 |     "    for item in soup.find_all(\"div\", class_ = \"rating-10\"):\n",
 76 |     "        try:\n",
 77 |     "            stars.append(item.span.text)\n",
 78 |     "        except:\n",
 79 |     "            print(f\"Error on page {i}\")\n",
 80 |     "            stars.append(\"None\")\n",
 81 |     "            \n",
 82 |     "    #date\n",
 83 |     "    for item in soup.find_all(\"time\"):\n",
 84 |     "        date.append(item.text)\n",
 85 |     "        \n",
 86 |     "    #country\n",
 87 |     "    for item in soup.find_all(\"h3\"):\n",
 88 |     "        country.append(item.span.next_sibling.text.strip(\" ()\"))"
 89 |    ]
 90 |   },
 91 |   {
 92 |    "cell_type": "code",
 93 |    "execution_count": 202,
 94 |    "id": "0982838a-78be-4c65-82c1-351c2075fa6d",
 95 |    "metadata": {},
 96 |    "outputs": [
 97 |     {
 98 |      "data": {
 99 |       "text/plain": [
100 |        "3418"
101 |       ]
102 |      },
103 |      "execution_count": 202,
104 |      "metadata": {},
105 |      "output_type": "execute_result"
106 |     }
107 |    ],
108 |    "source": [
109 |     "#check the length of total reviews extracted\n",
110 |     "len(reviews)"
111 |    ]
112 |   },
113 |   {
114 |    "cell_type": "code",
115 |    "execution_count": 203,
116 |    "id": "82fb4e85-e73e-493d-a0db-b84da71b616d",
117 |    "metadata": {},
118 |    "outputs": [
119 |     {
120 |      "data": {
121 |       "text/plain": [
122 |        "3418"
123 |       ]
124 |      },
125 |      "execution_count": 203,
126 |      "metadata": {},
127 |      "output_type": "execute_result"
128 |     }
129 |    ],
130 |    "source": [
131 |     "len(country)"
132 |    ]
133 |   },
134 |   {
135 |    "cell_type": "code",
136 |    "execution_count": 204,
137 |    "id": "df8251db-2d1d-40da-b6e6-a383d09808be",
138 |    "metadata": {},
139 |    "outputs": [],
140 |    "source": [
141 |     "#check the length \n",
142 |     "stars = stars[:3418]"
143 |    ]
144 |   },
145 |   {
146 |    "cell_type": "code",
147 |    "execution_count": 205,
148 |    "id": "79213047-71dd-4907-8caf-d9a9d157f37a",
149 |    "metadata": {},
150 |    "outputs": [],
151 |    "source": [
152 |     "#create  a dataframe from these collected lists of data\n",
153 |     "\n",
154 |     "df = pd.DataFrame({\"reviews\":reviews,\"stars\": stars, \"date\":date, \"country\": country})"
155 |    ]
156 |   },
157 |   {
158 |    "cell_type": "code",
159 |    "execution_count": 206,
160 |    "id": "c6532f95-e13b-4fb9-83f3-300c35c20cf6",
161 |    "metadata": {},
162 |    "outputs": [
163 |     {
164 |      "data": {
165 |       "text/html": [
166 |        "<div>\n",
167 |        "<style scoped>\n",
168 |        "    .dataframe tbody tr th:only-of-type {\n",
169 |        "        vertical-align: middle;\n",
170 |        "    }\n",
171 |        "\n",
172 |        "    .dataframe tbody tr th {\n",
173 |        "        vertical-align: top;\n",
174 |        "    }\n",
175 |        "\n",
176 |        "    .dataframe thead th {\n",
177 |        "        text-align: right;\n",
178 |        "    }\n",
179 |        "</style>\n",
180 |        "<table border=\"1\" class=\"dataframe\">\n",
181 |        "  <thead>\n",
182 |        "    <tr style=\"text-align: right;\">\n",
183 |        "      <th></th>\n",
184 |        "      <th>reviews</th>\n",
185 |        "      <th>stars</th>\n",
186 |        "      <th>date</th>\n",
187 |        "      <th>country</th>\n",
188 |        "    </tr>\n",
189 |        "  </thead>\n",
190 |        "  <tbody>\n",
191 |        "    <tr>\n",
192 |        "      <th>0</th>\n",
193 |        "      <td>Not Verified | Worst experience ever. Outbound...</td>\n",
194 |        "      <td>\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t5</td>\n",
195 |        "      <td>7th November 2022</td>\n",
196 |        "      <td>Italy</td>\n",
197 |        "    </tr>\n",
198 |        "    <tr>\n",
199 |        "      <th>1</th>\n",
200 |        "      <td>✅ Trip Verified |  Check in was a shambles at ...</td>\n",
201 |        "      <td>1</td>\n",
202 |        "      <td>7th November 2022</td>\n",
203 |        "      <td>Malaysia</td>\n",
204 |        "    </tr>\n",
205 |        "    <tr>\n",
206 |        "      <th>2</th>\n",
207 |        "      <td>✅ Trip Verified | Beyond disgusted with the fa...</td>\n",
208 |        "      <td>5</td>\n",
209 |        "      <td>5th November 2022</td>\n",
210 |        "      <td>United Arab Emirates</td>\n",
211 |        "    </tr>\n",
212 |        "    <tr>\n",
213 |        "      <th>3</th>\n",
214 |        "      <td>✅ Trip Verified | On July 19th 2022 I had subm...</td>\n",
215 |        "      <td>1</td>\n",
216 |        "      <td>31st October 2022</td>\n",
217 |        "      <td>United States</td>\n",
218 |        "    </tr>\n",
219 |        "    <tr>\n",
220 |        "      <th>4</th>\n",
221 |        "      <td>✅ Trip Verified |  I booked the flight on Oct ...</td>\n",
222 |        "      <td>1</td>\n",
223 |        "      <td>31st October 2022</td>\n",
224 |        "      <td>United States</td>\n",
225 |        "    </tr>\n",
226 |        "  </tbody>\n",
227 |        "</table>\n",
228 |        "</div>"
229 |       ],
230 |       "text/plain": [
231 |        "                                             reviews  \\\n",
232 |        "0  Not Verified | Worst experience ever. Outbound...   \n",
233 |        "1  ✅ Trip Verified |  Check in was a shambles at ...   \n",
234 |        "2  ✅ Trip Verified | Beyond disgusted with the fa...   \n",
235 |        "3  ✅ Trip Verified | On July 19th 2022 I had subm...   \n",
236 |        "4  ✅ Trip Verified |  I booked the flight on Oct ...   \n",
237 |        "\n",
238 |        "                           stars               date               country  \n",
239 |        "0  \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t5  7th November 2022                 Italy  \n",
240 |        "1                              1  7th November 2022              Malaysia  \n",
241 |        "2                              5  5th November 2022  United Arab Emirates  \n",
242 |        "3                              1  31st October 2022         United States  \n",
243 |        "4                              1  31st October 2022         United States  "
244 |       ]
245 |      },
246 |      "execution_count": 206,
247 |      "metadata": {},
248 |      "output_type": "execute_result"
249 |     }
250 |    ],
251 |    "source": [
252 |     "df.head()"
253 |    ]
254 |   },
255 |   {
256 |    "cell_type": "code",
257 |    "execution_count": 207,
258 |    "id": "366096e8-d1f2-4033-a831-5447e52861c2",
259 |    "metadata": {},
260 |    "outputs": [
261 |     {
262 |      "data": {
263 |       "text/plain": [
264 |        "(3418, 4)"
265 |       ]
266 |      },
267 |      "execution_count": 207,
268 |      "metadata": {},
269 |      "output_type": "execute_result"
270 |     }
271 |    ],
272 |    "source": [
273 |     "df.shape"
274 |    ]
275 |   },
276 |   {
277 |    "cell_type": "markdown",
278 |    "id": "fc219e55-fdc8-4b10-a3e9-8dda764ee275",
279 |    "metadata": {},
280 |    "source": [
281 |     "### Export the data into a csv format"
282 |    ]
283 |   },
284 |   {
285 |    "cell_type": "code",
286 |    "execution_count": 208,
287 |    "id": "b286e642-59a2-4da3-9120-e9db119ebea6",
288 |    "metadata": {},
289 |    "outputs": [],
290 |    "source": [
291 |     "import os\n",
292 |     "\n",
293 |     "cwd = os.getcwd()\n",
294 |     "df.to_csv(cwd+ \"/BA_reviews.csv\")"
295 |    ]
296 |   },
297 |   {
298 |    "cell_type": "code",
299 |    "execution_count": null,
300 |    "id": "eb1d69e4-3f4c-4b21-8b04-ebccfb7d6332",
301 |    "metadata": {},
302 |    "outputs": [],
303 |    "source": []
304 |   },
305 |   {
306 |    "cell_type": "code",
307 |    "execution_count": null,
308 |    "id": "591157d3-bd0c-4305-9975-c1b09cb680d3",
309 |    "metadata": {},
310 |    "outputs": [],
311 |    "source": []
312 |   }
313 |  ],
314 |  "metadata": {
315 |   "kernelspec": {
316 |    "display_name": "Python 3 (ipykernel)",
317 |    "language": "python",
318 |    "name": "python3"
319 |   },
320 |   "language_info": {
321 |    "codemirror_mode": {
322 |     "name": "ipython",
323 |     "version": 3
324 |    },
325 |    "file_extension": ".py",
326 |    "mimetype": "text/x-python",
327 |    "name": "python",
328 |    "nbconvert_exporter": "python",
329 |    "pygments_lexer": "ipython3",
330 |    "version": "3.9.7"
331 |   }
332 |  },
333 |  "nbformat": 4,
334 |  "nbformat_minor": 5
335 | }
336 | 


--------------------------------------------------------------------------------
/Data Collection/Data-collection.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "8d47a312-9fd2-44a8-b43e-aaaddaf670c4",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "## Data Collection\n",
  9 |     "\n",
 10 |     "In this phase we will collect the customer ratings data from the airline quality website called [Skytrax](https://www.airlinequality.com/airline-reviews/british-airways). We will collect data about airline ratings, seat ratings and lounge experience ratings from this website. "
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 199,
 16 |    "id": "5f43dc5f-ffc2-4ba0-8a73-3b70e9ab0fcf",
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "#imports\n",
 21 |     "\n",
 22 |     "import pandas as pd\n",
 23 |     "import numpy as np\n",
 24 |     "from bs4 import BeautifulSoup\n",
 25 |     "import requests "
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "code",
 30 |    "execution_count": 200,
 31 |    "id": "d5c2c023-7500-4e89-94f7-ddff4864d83c",
 32 |    "metadata": {},
 33 |    "outputs": [],
 34 |    "source": [
 35 |     "#create an empty list to collect all reviews\n",
 36 |     "reviews  = []\n",
 37 |     "\n",
 38 |     "#create an empty list to collect rating stars\n",
 39 |     "stars = []\n",
 40 |     "\n",
 41 |     "#create an empty list to collect date\n",
 42 |     "date = []\n",
 43 |     "\n",
 44 |     "#create an empty list to collect country the reviewer is from\n",
 45 |     "country = []"
 46 |    ]
 47 |   },
 48 |   {
 49 |    "cell_type": "code",
 50 |    "execution_count": 201,
 51 |    "id": "2894e93a-cbd4-45b5-b262-95f88994d83a",
 52 |    "metadata": {},
 53 |    "outputs": [
 54 |     {
 55 |      "name": "stdout",
 56 |      "output_type": "stream",
 57 |      "text": [
 58 |       "Error on page 29\n",
 59 |       "Error on page 30\n",
 60 |       "Error on page 30\n",
 61 |       "Error on page 33\n",
 62 |       "Error on page 33\n"
 63 |      ]
 64 |     }
 65 |    ],
 66 |    "source": [
 67 |     "for i in range(1, 36):\n",
 68 |     "    page = requests.get(f\"https://www.airlinequality.com/airline-reviews/british-airways/page/{i}/?sortby=post_date%3ADesc&pagesize=100\")\n",
 69 |     "    \n",
 70 |     "    soup = BeautifulSoup(page.content, \"html5\")\n",
 71 |     "    \n",
 72 |     "    for item in soup.find_all(\"div\", class_=\"text_content\"):\n",
 73 |     "        reviews.append(item.text)\n",
 74 |     "    \n",
 75 |     "    for item in soup.find_all(\"div\", class_ = \"rating-10\"):\n",
 76 |     "        try:\n",
 77 |     "            stars.append(item.span.text)\n",
 78 |     "        except:\n",
 79 |     "            print(f\"Error on page {i}\")\n",
 80 |     "            stars.append(\"None\")\n",
 81 |     "            \n",
 82 |     "    #date\n",
 83 |     "    for item in soup.find_all(\"time\"):\n",
 84 |     "        date.append(item.text)\n",
 85 |     "        \n",
 86 |     "    #country\n",
 87 |     "    for item in soup.find_all(\"h3\"):\n",
 88 |     "        country.append(item.span.next_sibling.text.strip(\" ()\"))"
 89 |    ]
 90 |   },
 91 |   {
 92 |    "cell_type": "code",
 93 |    "execution_count": 202,
 94 |    "id": "0982838a-78be-4c65-82c1-351c2075fa6d",
 95 |    "metadata": {},
 96 |    "outputs": [
 97 |     {
 98 |      "data": {
 99 |       "text/plain": [
100 |        "3418"
101 |       ]
102 |      },
103 |      "execution_count": 202,
104 |      "metadata": {},
105 |      "output_type": "execute_result"
106 |     }
107 |    ],
108 |    "source": [
109 |     "#check the length of total reviews extracted\n",
110 |     "len(reviews)"
111 |    ]
112 |   },
113 |   {
114 |    "cell_type": "code",
115 |    "execution_count": 203,
116 |    "id": "82fb4e85-e73e-493d-a0db-b84da71b616d",
117 |    "metadata": {},
118 |    "outputs": [
119 |     {
120 |      "data": {
121 |       "text/plain": [
122 |        "3418"
123 |       ]
124 |      },
125 |      "execution_count": 203,
126 |      "metadata": {},
127 |      "output_type": "execute_result"
128 |     }
129 |    ],
130 |    "source": [
131 |     "len(country)"
132 |    ]
133 |   },
134 |   {
135 |    "cell_type": "code",
136 |    "execution_count": 204,
137 |    "id": "df8251db-2d1d-40da-b6e6-a383d09808be",
138 |    "metadata": {},
139 |    "outputs": [],
140 |    "source": [
141 |     "#check the length \n",
142 |     "stars = stars[:3418]"
143 |    ]
144 |   },
145 |   {
146 |    "cell_type": "code",
147 |    "execution_count": 205,
148 |    "id": "79213047-71dd-4907-8caf-d9a9d157f37a",
149 |    "metadata": {},
150 |    "outputs": [],
151 |    "source": [
152 |     "#create  a dataframe from these collected lists of data\n",
153 |     "\n",
154 |     "df = pd.DataFrame({\"reviews\":reviews,\"stars\": stars, \"date\":date, \"country\": country})"
155 |    ]
156 |   },
157 |   {
158 |    "cell_type": "code",
159 |    "execution_count": 206,
160 |    "id": "c6532f95-e13b-4fb9-83f3-300c35c20cf6",
161 |    "metadata": {},
162 |    "outputs": [
163 |     {
164 |      "data": {
165 |       "text/html": [
166 |        "<div>\n",
167 |        "<style scoped>\n",
168 |        "    .dataframe tbody tr th:only-of-type {\n",
169 |        "        vertical-align: middle;\n",
170 |        "    }\n",
171 |        "\n",
172 |        "    .dataframe tbody tr th {\n",
173 |        "        vertical-align: top;\n",
174 |        "    }\n",
175 |        "\n",
176 |        "    .dataframe thead th {\n",
177 |        "        text-align: right;\n",
178 |        "    }\n",
179 |        "</style>\n",
180 |        "<table border=\"1\" class=\"dataframe\">\n",
181 |        "  <thead>\n",
182 |        "    <tr style=\"text-align: right;\">\n",
183 |        "      <th></th>\n",
184 |        "      <th>reviews</th>\n",
185 |        "      <th>stars</th>\n",
186 |        "      <th>date</th>\n",
187 |        "      <th>country</th>\n",
188 |        "    </tr>\n",
189 |        "  </thead>\n",
190 |        "  <tbody>\n",
191 |        "    <tr>\n",
192 |        "      <th>0</th>\n",
193 |        "      <td>Not Verified | Worst experience ever. Outbound...</td>\n",
194 |        "      <td>\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t5</td>\n",
195 |        "      <td>7th November 2022</td>\n",
196 |        "      <td>Italy</td>\n",
197 |        "    </tr>\n",
198 |        "    <tr>\n",
199 |        "      <th>1</th>\n",
200 |        "      <td>✅ Trip Verified |  Check in was a shambles at ...</td>\n",
201 |        "      <td>1</td>\n",
202 |        "      <td>7th November 2022</td>\n",
203 |        "      <td>Malaysia</td>\n",
204 |        "    </tr>\n",
205 |        "    <tr>\n",
206 |        "      <th>2</th>\n",
207 |        "      <td>✅ Trip Verified | Beyond disgusted with the fa...</td>\n",
208 |        "      <td>5</td>\n",
209 |        "      <td>5th November 2022</td>\n",
210 |        "      <td>United Arab Emirates</td>\n",
211 |        "    </tr>\n",
212 |        "    <tr>\n",
213 |        "      <th>3</th>\n",
214 |        "      <td>✅ Trip Verified | On July 19th 2022 I had subm...</td>\n",
215 |        "      <td>1</td>\n",
216 |        "      <td>31st October 2022</td>\n",
217 |        "      <td>United States</td>\n",
218 |        "    </tr>\n",
219 |        "    <tr>\n",
220 |        "      <th>4</th>\n",
221 |        "      <td>✅ Trip Verified |  I booked the flight on Oct ...</td>\n",
222 |        "      <td>1</td>\n",
223 |        "      <td>31st October 2022</td>\n",
224 |        "      <td>United States</td>\n",
225 |        "    </tr>\n",
226 |        "  </tbody>\n",
227 |        "</table>\n",
228 |        "</div>"
229 |       ],
230 |       "text/plain": [
231 |        "                                             reviews  \\\n",
232 |        "0  Not Verified | Worst experience ever. Outbound...   \n",
233 |        "1  ✅ Trip Verified |  Check in was a shambles at ...   \n",
234 |        "2  ✅ Trip Verified | Beyond disgusted with the fa...   \n",
235 |        "3  ✅ Trip Verified | On July 19th 2022 I had subm...   \n",
236 |        "4  ✅ Trip Verified |  I booked the flight on Oct ...   \n",
237 |        "\n",
238 |        "                           stars               date               country  \n",
239 |        "0  \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t5  7th November 2022                 Italy  \n",
240 |        "1                              1  7th November 2022              Malaysia  \n",
241 |        "2                              5  5th November 2022  United Arab Emirates  \n",
242 |        "3                              1  31st October 2022         United States  \n",
243 |        "4                              1  31st October 2022         United States  "
244 |       ]
245 |      },
246 |      "execution_count": 206,
247 |      "metadata": {},
248 |      "output_type": "execute_result"
249 |     }
250 |    ],
251 |    "source": [
252 |     "df.head()"
253 |    ]
254 |   },
255 |   {
256 |    "cell_type": "code",
257 |    "execution_count": 207,
258 |    "id": "366096e8-d1f2-4033-a831-5447e52861c2",
259 |    "metadata": {},
260 |    "outputs": [
261 |     {
262 |      "data": {
263 |       "text/plain": [
264 |        "(3418, 4)"
265 |       ]
266 |      },
267 |      "execution_count": 207,
268 |      "metadata": {},
269 |      "output_type": "execute_result"
270 |     }
271 |    ],
272 |    "source": [
273 |     "df.shape"
274 |    ]
275 |   },
276 |   {
277 |    "cell_type": "markdown",
278 |    "id": "fc219e55-fdc8-4b10-a3e9-8dda764ee275",
279 |    "metadata": {},
280 |    "source": [
281 |     "### Export the data into a csv format"
282 |    ]
283 |   },
284 |   {
285 |    "cell_type": "code",
286 |    "execution_count": 208,
287 |    "id": "b286e642-59a2-4da3-9120-e9db119ebea6",
288 |    "metadata": {},
289 |    "outputs": [],
290 |    "source": [
291 |     "import os\n",
292 |     "\n",
293 |     "cwd = os.getcwd()\n",
294 |     "df.to_csv(cwd+ \"/BA_reviews.csv\")"
295 |    ]
296 |   },
297 |   {
298 |    "cell_type": "code",
299 |    "execution_count": null,
300 |    "id": "eb1d69e4-3f4c-4b21-8b04-ebccfb7d6332",
301 |    "metadata": {},
302 |    "outputs": [],
303 |    "source": []
304 |   },
305 |   {
306 |    "cell_type": "code",
307 |    "execution_count": null,
308 |    "id": "591157d3-bd0c-4305-9975-c1b09cb680d3",
309 |    "metadata": {},
310 |    "outputs": [],
311 |    "source": []
312 |   },
313 |   {
314 |    "cell_type": "code",
315 |    "execution_count": null,
316 |    "id": "360952f1-decb-4f41-abbf-54c5ac7b9e59",
317 |    "metadata": {},
318 |    "outputs": [],
319 |    "source": []
320 |   }
321 |  ],
322 |  "metadata": {
323 |   "kernelspec": {
324 |    "display_name": "Python 3 (ipykernel)",
325 |    "language": "python",
326 |    "name": "python3"
327 |   },
328 |   "language_info": {
329 |    "codemirror_mode": {
330 |     "name": "ipython",
331 |     "version": 3
332 |    },
333 |    "file_extension": ".py",
334 |    "mimetype": "text/x-python",
335 |    "name": "python",
336 |    "nbconvert_exporter": "python",
337 |    "pygments_lexer": "ipython3",
338 |    "version": "3.9.7"
339 |   }
340 |  },
341 |  "nbformat": 4,
342 |  "nbformat_minor": 5
343 | }
344 | 


--------------------------------------------------------------------------------
/Data Cleaning/Data Cleaning.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "e00b5383-b915-4a16-8ed0-476f979052b7",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "## Data Cleaning\n",
  9 |     "\n",
 10 |     "Now since we have extracted data from the website, it is not cleaned and ready to be analyzed yet. The reviews section will need to be cleaned for punctuations, spellings and other characters. "
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 83,
 16 |    "id": "69c14a0d-d637-4b7e-8714-31fb531b4954",
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "#imports\n",
 21 |     "\n",
 22 |     "import pandas as pd\n",
 23 |     "import matplotlib.pyplot as plt\n",
 24 |     "import seaborn as sns\n",
 25 |     "import os\n",
 26 |     "\n",
 27 |     "#regex\n",
 28 |     "import re"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "code",
 33 |    "execution_count": 84,
 34 |    "id": "c46b06dc-b9f5-4b9f-b52b-7f9b01ff7200",
 35 |    "metadata": {},
 36 |    "outputs": [],
 37 |    "source": [
 38 |     "#create a dataframe from csv file\n",
 39 |     "\n",
 40 |     "cwd = os.getcwd()\n",
 41 |     "\n",
 42 |     "df = pd.read_csv(cwd+\"/BA_reviews.csv\", index_col=0)"
 43 |    ]
 44 |   },
 45 |   {
 46 |    "cell_type": "code",
 47 |    "execution_count": 85,
 48 |    "id": "888d1113-bafd-4e6c-a020-c0e7b721ce50",
 49 |    "metadata": {},
 50 |    "outputs": [
 51 |     {
 52 |      "data": {
 53 |       "text/html": [
 54 |        "<div>\n",
 55 |        "<style scoped>\n",
 56 |        "    .dataframe tbody tr th:only-of-type {\n",
 57 |        "        vertical-align: middle;\n",
 58 |        "    }\n",
 59 |        "\n",
 60 |        "    .dataframe tbody tr th {\n",
 61 |        "        vertical-align: top;\n",
 62 |        "    }\n",
 63 |        "\n",
 64 |        "    .dataframe thead th {\n",
 65 |        "        text-align: right;\n",
 66 |        "    }\n",
 67 |        "</style>\n",
 68 |        "<table border=\"1\" class=\"dataframe\">\n",
 69 |        "  <thead>\n",
 70 |        "    <tr style=\"text-align: right;\">\n",
 71 |        "      <th></th>\n",
 72 |        "      <th>reviews</th>\n",
 73 |        "      <th>stars</th>\n",
 74 |        "      <th>date</th>\n",
 75 |        "      <th>country</th>\n",
 76 |        "    </tr>\n",
 77 |        "  </thead>\n",
 78 |        "  <tbody>\n",
 79 |        "    <tr>\n",
 80 |        "      <th>0</th>\n",
 81 |        "      <td>Not Verified | Worst experience ever. Outbound...</td>\n",
 82 |        "      <td>\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t5</td>\n",
 83 |        "      <td>7th November 2022</td>\n",
 84 |        "      <td>Italy</td>\n",
 85 |        "    </tr>\n",
 86 |        "    <tr>\n",
 87 |        "      <th>1</th>\n",
 88 |        "      <td>✅ Trip Verified |  Check in was a shambles at ...</td>\n",
 89 |        "      <td>1</td>\n",
 90 |        "      <td>7th November 2022</td>\n",
 91 |        "      <td>Malaysia</td>\n",
 92 |        "    </tr>\n",
 93 |        "    <tr>\n",
 94 |        "      <th>2</th>\n",
 95 |        "      <td>✅ Trip Verified | Beyond disgusted with the fa...</td>\n",
 96 |        "      <td>5</td>\n",
 97 |        "      <td>5th November 2022</td>\n",
 98 |        "      <td>United Arab Emirates</td>\n",
 99 |        "    </tr>\n",
100 |        "    <tr>\n",
101 |        "      <th>3</th>\n",
102 |        "      <td>✅ Trip Verified | On July 19th 2022 I had subm...</td>\n",
103 |        "      <td>1</td>\n",
104 |        "      <td>31st October 2022</td>\n",
105 |        "      <td>United States</td>\n",
106 |        "    </tr>\n",
107 |        "    <tr>\n",
108 |        "      <th>4</th>\n",
109 |        "      <td>✅ Trip Verified |  I booked the flight on Oct ...</td>\n",
110 |        "      <td>1</td>\n",
111 |        "      <td>31st October 2022</td>\n",
112 |        "      <td>United States</td>\n",
113 |        "    </tr>\n",
114 |        "  </tbody>\n",
115 |        "</table>\n",
116 |        "</div>"
117 |       ],
118 |       "text/plain": [
119 |        "                                             reviews  \\\n",
120 |        "0  Not Verified | Worst experience ever. Outbound...   \n",
121 |        "1  ✅ Trip Verified |  Check in was a shambles at ...   \n",
122 |        "2  ✅ Trip Verified | Beyond disgusted with the fa...   \n",
123 |        "3  ✅ Trip Verified | On July 19th 2022 I had subm...   \n",
124 |        "4  ✅ Trip Verified |  I booked the flight on Oct ...   \n",
125 |        "\n",
126 |        "                           stars               date               country  \n",
127 |        "0  \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t5  7th November 2022                 Italy  \n",
128 |        "1                              1  7th November 2022              Malaysia  \n",
129 |        "2                              5  5th November 2022  United Arab Emirates  \n",
130 |        "3                              1  31st October 2022         United States  \n",
131 |        "4                              1  31st October 2022         United States  "
132 |       ]
133 |      },
134 |      "execution_count": 85,
135 |      "metadata": {},
136 |      "output_type": "execute_result"
137 |     }
138 |    ],
139 |    "source": [
140 |     "df.head()"
141 |    ]
142 |   },
143 |   {
144 |    "cell_type": "markdown",
145 |    "id": "7aeb0b06-23f8-48bb-b2d4-b91455040038",
146 |    "metadata": {},
147 |    "source": [
148 |     "We will also create a column which mentions if the user is verified or not. "
149 |    ]
150 |   },
151 |   {
152 |    "cell_type": "code",
153 |    "execution_count": 86,
154 |    "id": "45908798-e042-4d71-8e01-395f53f2005d",
155 |    "metadata": {},
156 |    "outputs": [],
157 |    "source": [
158 |     "df['verified'] = df.reviews.str.contains(\"Trip Verified\")"
159 |    ]
160 |   },
161 |   {
162 |    "cell_type": "code",
163 |    "execution_count": 87,
164 |    "id": "ae4e6e23-4ecc-4c94-8526-5f645ff2f6a2",
165 |    "metadata": {},
166 |    "outputs": [
167 |     {
168 |      "data": {
169 |       "text/plain": [
170 |        "0       False\n",
171 |        "1        True\n",
172 |        "2        True\n",
173 |        "3        True\n",
174 |        "4        True\n",
175 |        "        ...  \n",
176 |        "3413    False\n",
177 |        "3414    False\n",
178 |        "3415    False\n",
179 |        "3416    False\n",
180 |        "3417    False\n",
181 |        "Name: verified, Length: 3418, dtype: bool"
182 |       ]
183 |      },
184 |      "execution_count": 87,
185 |      "metadata": {},
186 |      "output_type": "execute_result"
187 |     }
188 |    ],
189 |    "source": [
190 |     "df['verified']"
191 |    ]
192 |   },
193 |   {
194 |    "cell_type": "markdown",
195 |    "id": "cf266fe0-0c33-477a-9f1e-529a214e91c5",
196 |    "metadata": {},
197 |    "source": [
198 |     "### Cleaning Reviews"
199 |    ]
200 |   },
201 |   {
202 |    "cell_type": "markdown",
203 |    "id": "ac2bc6a6-8783-4873-b5e7-1719f997cdf2",
204 |    "metadata": {},
205 |    "source": [
206 |     "We will extract the column of reviews into a separate dataframe and clean it for semantic analysis"
207 |    ]
208 |   },
209 |   {
210 |    "cell_type": "code",
211 |    "execution_count": 88,
212 |    "id": "e4319089-b957-4b60-8f07-fc8cd3487f9e",
213 |    "metadata": {},
214 |    "outputs": [],
215 |    "source": [
216 |     "#for lemmatization of words we will use nltk library\n",
217 |     "from nltk.stem import WordNetLemmatizer\n",
218 |     "from nltk.corpus import stopwords\n",
219 |     "lemma = WordNetLemmatizer()\n",
220 |     "\n",
221 |     "\n",
222 |     "reviews_data = df.reviews.str.strip(\"✅ Trip Verified |\")\n",
223 |     "\n",
224 |     "#create an empty list to collect cleaned data corpus\n",
225 |     "corpus =[]\n",
226 |     "\n",
227 |     "#loop through each review, remove punctuations, small case it, join it and add it to corpus\n",
228 |     "for rev in reviews_data:\n",
229 |     "    rev = re.sub('[^a-zA-Z]',' ', rev)\n",
230 |     "    rev = rev.lower()\n",
231 |     "    rev = rev.split()\n",
232 |     "    rev = [lemma.lemmatize(word) for word in rev if word not in set(stopwords.words(\"english\"))]\n",
233 |     "    rev = \" \".join(rev)\n",
234 |     "    corpus.append(rev)"
235 |    ]
236 |   },
237 |   {
238 |    "cell_type": "code",
239 |    "execution_count": 89,
240 |    "id": "835b9930-20e0-4d79-8a88-a5737309b4ce",
241 |    "metadata": {},
242 |    "outputs": [],
243 |    "source": [
244 |     "# add the corpus to the original dataframe\n",
245 |     "\n",
246 |     "df['corpus'] = corpus"
247 |    ]
248 |   },
249 |   {
250 |    "cell_type": "code",
251 |    "execution_count": 90,
252 |    "id": "baa16beb-4ac5-4850-a46a-0ae11d64cf3d",
253 |    "metadata": {},
254 |    "outputs": [
255 |     {
256 |      "data": {
257 |       "text/html": [
258 |        "<div>\n",
259 |        "<style scoped>\n",
260 |        "    .dataframe tbody tr th:only-of-type {\n",
261 |        "        vertical-align: middle;\n",
262 |        "    }\n",
263 |        "\n",
264 |        "    .dataframe tbody tr th {\n",
265 |        "        vertical-align: top;\n",
266 |        "    }\n",
267 |        "\n",
268 |        "    .dataframe thead th {\n",
269 |        "        text-align: right;\n",
270 |        "    }\n",
271 |        "</style>\n",
272 |        "<table border=\"1\" class=\"dataframe\">\n",
273 |        "  <thead>\n",
274 |        "    <tr style=\"text-align: right;\">\n",
275 |        "      <th></th>\n",
276 |        "      <th>reviews</th>\n",
277 |        "      <th>stars</th>\n",
278 |        "      <th>date</th>\n",
279 |        "      <th>country</th>\n",
280 |        "      <th>verified</th>\n",
281 |        "      <th>corpus</th>\n",
282 |        "    </tr>\n",
283 |        "  </thead>\n",
284 |        "  <tbody>\n",
285 |        "    <tr>\n",
286 |        "      <th>0</th>\n",
287 |        "      <td>Not Verified | Worst experience ever. Outbound...</td>\n",
288 |        "      <td>\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t5</td>\n",
289 |        "      <td>7th November 2022</td>\n",
290 |        "      <td>Italy</td>\n",
291 |        "      <td>False</td>\n",
292 |        "      <td>verified worst experience ever outbound flight...</td>\n",
293 |        "    </tr>\n",
294 |        "    <tr>\n",
295 |        "      <th>1</th>\n",
296 |        "      <td>✅ Trip Verified |  Check in was a shambles at ...</td>\n",
297 |        "      <td>1</td>\n",
298 |        "      <td>7th November 2022</td>\n",
299 |        "      <td>Malaysia</td>\n",
300 |        "      <td>True</td>\n",
301 |        "      <td>check shamble bwi counter open full flight bag...</td>\n",
302 |        "    </tr>\n",
303 |        "    <tr>\n",
304 |        "      <th>2</th>\n",
305 |        "      <td>✅ Trip Verified | Beyond disgusted with the fa...</td>\n",
306 |        "      <td>5</td>\n",
307 |        "      <td>5th November 2022</td>\n",
308 |        "      <td>United Arab Emirates</td>\n",
309 |        "      <td>True</td>\n",
310 |        "      <td>beyond disgusted fact baggage yet delivered we...</td>\n",
311 |        "    </tr>\n",
312 |        "    <tr>\n",
313 |        "      <th>3</th>\n",
314 |        "      <td>✅ Trip Verified | On July 19th 2022 I had subm...</td>\n",
315 |        "      <td>1</td>\n",
316 |        "      <td>31st October 2022</td>\n",
317 |        "      <td>United States</td>\n",
318 |        "      <td>True</td>\n",
319 |        "      <td>july th submitted complaint form regard fact b...</td>\n",
320 |        "    </tr>\n",
321 |        "    <tr>\n",
322 |        "      <th>4</th>\n",
323 |        "      <td>✅ Trip Verified |  I booked the flight on Oct ...</td>\n",
324 |        "      <td>1</td>\n",
325 |        "      <td>31st October 2022</td>\n",
326 |        "      <td>United States</td>\n",
327 |        "      <td>True</td>\n",
328 |        "      <td>booked flight oct cancel flight day learning g...</td>\n",
329 |        "    </tr>\n",
330 |        "  </tbody>\n",
331 |        "</table>\n",
332 |        "</div>"
333 |       ],
334 |       "text/plain": [
335 |        "                                             reviews  \\\n",
336 |        "0  Not Verified | Worst experience ever. Outbound...   \n",
337 |        "1  ✅ Trip Verified |  Check in was a shambles at ...   \n",
338 |        "2  ✅ Trip Verified | Beyond disgusted with the fa...   \n",
339 |        "3  ✅ Trip Verified | On July 19th 2022 I had subm...   \n",
340 |        "4  ✅ Trip Verified |  I booked the flight on Oct ...   \n",
341 |        "\n",
342 |        "                           stars               date               country  \\\n",
343 |        "0  \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t5  7th November 2022                 Italy   \n",
344 |        "1                              1  7th November 2022              Malaysia   \n",
345 |        "2                              5  5th November 2022  United Arab Emirates   \n",
346 |        "3                              1  31st October 2022         United States   \n",
347 |        "4                              1  31st October 2022         United States   \n",
348 |        "\n",
349 |        "   verified                                             corpus  \n",
350 |        "0     False  verified worst experience ever outbound flight...  \n",
351 |        "1      True  check shamble bwi counter open full flight bag...  \n",
352 |        "2      True  beyond disgusted fact baggage yet delivered we...  \n",
353 |        "3      True  july th submitted complaint form regard fact b...  \n",
354 |        "4      True  booked flight oct cancel flight day learning g...  "
355 |       ]
356 |      },
357 |      "execution_count": 90,
358 |      "metadata": {},
359 |      "output_type": "execute_result"
360 |     }
361 |    ],
362 |    "source": [
363 |     "df.head()"
364 |    ]
365 |   },
366 |   {
367 |    "cell_type": "markdown",
368 |    "id": "63424d81-bab2-4e74-aa97-6094bd035940",
369 |    "metadata": {},
370 |    "source": [
371 |     "### Cleaning/Fromat date"
372 |    ]
373 |   },
374 |   {
375 |    "cell_type": "code",
376 |    "execution_count": 91,
377 |    "id": "33f1a718-af12-4fa4-85ae-f5511e8986ae",
378 |    "metadata": {},
379 |    "outputs": [
380 |     {
381 |      "data": {
382 |       "text/plain": [
383 |        "reviews     object\n",
384 |        "stars       object\n",
385 |        "date        object\n",
386 |        "country     object\n",
387 |        "verified      bool\n",
388 |        "corpus      object\n",
389 |        "dtype: object"
390 |       ]
391 |      },
392 |      "execution_count": 91,
393 |      "metadata": {},
394 |      "output_type": "execute_result"
395 |     }
396 |    ],
397 |    "source": [
398 |     "df.dtypes"
399 |    ]
400 |   },
401 |   {
402 |    "cell_type": "code",
403 |    "execution_count": 92,
404 |    "id": "c773fca4-23ee-4f7d-a1db-8da648ddb161",
405 |    "metadata": {},
406 |    "outputs": [],
407 |    "source": [
408 |     "# convert the date to datetime format\n",
409 |     "\n",
410 |     "df.date = pd.to_datetime(df.date)"
411 |    ]
412 |   },
413 |   {
414 |    "cell_type": "code",
415 |    "execution_count": 93,
416 |    "id": "0440c89e-9a89-4ef4-888f-eb25ad833afb",
417 |    "metadata": {},
418 |    "outputs": [
419 |     {
420 |      "data": {
421 |       "text/plain": [
422 |        "0   2022-11-07\n",
423 |        "1   2022-11-07\n",
424 |        "2   2022-11-05\n",
425 |        "3   2022-10-31\n",
426 |        "4   2022-10-31\n",
427 |        "Name: date, dtype: datetime64[ns]"
428 |       ]
429 |      },
430 |      "execution_count": 93,
431 |      "metadata": {},
432 |      "output_type": "execute_result"
433 |     }
434 |    ],
435 |    "source": [
436 |     "df.date.head()"
437 |    ]
438 |   },
439 |   {
440 |    "cell_type": "markdown",
441 |    "id": "a7973868-80c4-46b9-9449-6761fac91f64",
442 |    "metadata": {},
443 |    "source": [
444 |     "### Cleaning ratings with stars"
445 |    ]
446 |   },
447 |   {
448 |    "cell_type": "code",
449 |    "execution_count": 100,
450 |    "id": "4f535799-1f5a-43d2-a858-5639f35b073c",
451 |    "metadata": {},
452 |    "outputs": [
453 |     {
454 |      "data": {
455 |       "text/plain": [
456 |        "array(['\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t5', '1', '5', '6', '9', '3', '2', '8',\n",
457 |        "       '7', '10', '4', 'None'], dtype=object)"
458 |       ]
459 |      },
460 |      "execution_count": 100,
461 |      "metadata": {},
462 |      "output_type": "execute_result"
463 |     }
464 |    ],
465 |    "source": [
466 |     "#check for unique values\n",
467 |     "df.stars.unique()"
468 |    ]
469 |   },
470 |   {
471 |    "cell_type": "code",
472 |    "execution_count": 102,
473 |    "id": "5664b1c3-17d3-4f54-8799-388443a6242d",
474 |    "metadata": {},
475 |    "outputs": [],
476 |    "source": [
477 |     "# remove the \\t and \\n from the ratings\n",
478 |     "df.stars = df.stars.str.strip(\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\")"
479 |    ]
480 |   },
481 |   {
482 |    "cell_type": "code",
483 |    "execution_count": 105,
484 |    "id": "9cbfaa06-b519-4ce1-bb50-420692a923d6",
485 |    "metadata": {},
486 |    "outputs": [
487 |     {
488 |      "data": {
489 |       "text/plain": [
490 |        "1       735\n",
491 |        "2       382\n",
492 |        "3       379\n",
493 |        "8       349\n",
494 |        "10      306\n",
495 |        "7       299\n",
496 |        "9       293\n",
497 |        "5       259\n",
498 |        "4       227\n",
499 |        "6       182\n",
500 |        "None      5\n",
501 |        "Name: stars, dtype: int64"
502 |       ]
503 |      },
504 |      "execution_count": 105,
505 |      "metadata": {},
506 |      "output_type": "execute_result"
507 |     }
508 |    ],
509 |    "source": [
510 |     "df.stars.value_counts()"
511 |    ]
512 |   },
513 |   {
514 |    "cell_type": "markdown",
515 |    "id": "23941023-6f4d-44a3-9ae5-34ba7270a646",
516 |    "metadata": {},
517 |    "source": [
518 |     "There are 5 rows having values \"None\" in the ratings. We will drop all these 5 rows. "
519 |    ]
520 |   },
521 |   {
522 |    "cell_type": "code",
523 |    "execution_count": 110,
524 |    "id": "e5d3e147-b454-4515-8b14-54e4ec756b1c",
525 |    "metadata": {},
526 |    "outputs": [],
527 |    "source": [
528 |     "# drop the rows where the value of ratings is None\n",
529 |     "df.drop(df[df.stars == \"None\"].index, axis=0, inplace=True)"
530 |    ]
531 |   },
532 |   {
533 |    "cell_type": "code",
534 |    "execution_count": 111,
535 |    "id": "e152aed3-2da4-4ee9-90dc-d2bd3553d870",
536 |    "metadata": {},
537 |    "outputs": [
538 |     {
539 |      "data": {
540 |       "text/plain": [
541 |        "array(['5', '1', '6', '9', '3', '2', '8', '7', '10', '4'], dtype=object)"
542 |       ]
543 |      },
544 |      "execution_count": 111,
545 |      "metadata": {},
546 |      "output_type": "execute_result"
547 |     }
548 |    ],
549 |    "source": [
550 |     "#check the unique values again\n",
551 |     "df.stars.unique()"
552 |    ]
553 |   },
554 |   {
555 |    "cell_type": "markdown",
556 |    "id": "c29e569e-bab0-4d73-8496-f60aa375be38",
557 |    "metadata": {},
558 |    "source": [
559 |     "## Check for null Values"
560 |    ]
561 |   },
562 |   {
563 |    "cell_type": "code",
564 |    "execution_count": 112,
565 |    "id": "e81950f2-0cb3-4c1a-9859-233f4c3304d7",
566 |    "metadata": {},
567 |    "outputs": [
568 |     {
569 |      "data": {
570 |       "text/plain": [
571 |        "reviews  stars  date   country  verified  corpus\n",
572 |        "False    False  False  False    False     False     3411\n",
573 |        "dtype: int64"
574 |       ]
575 |      },
576 |      "execution_count": 112,
577 |      "metadata": {},
578 |      "output_type": "execute_result"
579 |     }
580 |    ],
581 |    "source": [
582 |     "df.isnull().value_counts()"
583 |    ]
584 |   },
585 |   {
586 |    "cell_type": "code",
587 |    "execution_count": 113,
588 |    "id": "7c53f6a8-3f14-4711-a11a-a99f72015431",
589 |    "metadata": {},
590 |    "outputs": [
591 |     {
592 |      "data": {
593 |       "text/plain": [
594 |        "False    3411\n",
595 |        "Name: country, dtype: int64"
596 |       ]
597 |      },
598 |      "execution_count": 113,
599 |      "metadata": {},
600 |      "output_type": "execute_result"
601 |     }
602 |    ],
603 |    "source": [
604 |     "df.country.isnull().value_counts()"
605 |    ]
606 |   },
607 |   {
608 |    "cell_type": "markdown",
609 |    "id": "3d9dbc68-9826-49e0-a003-d1c0c5e5d4f6",
610 |    "metadata": {},
611 |    "source": [
612 |     "We have two missing values for country. For this we can just remove those two reviews (rows) from the dataframe. "
613 |    ]
614 |   },
615 |   {
616 |    "cell_type": "code",
617 |    "execution_count": 114,
618 |    "id": "98b5b16a-c25d-475b-8894-e680323195eb",
619 |    "metadata": {},
620 |    "outputs": [],
621 |    "source": [
622 |     "#drop the rows using index where the country value is null\n",
623 |     "df.drop(df[df.country.isnull() == True].index, axis=0, inplace=True)"
624 |    ]
625 |   },
626 |   {
627 |    "cell_type": "code",
628 |    "execution_count": 115,
629 |    "id": "116656f9-0f7a-4d79-8f69-7f4fbf711eca",
630 |    "metadata": {},
631 |    "outputs": [
632 |     {
633 |      "data": {
634 |       "text/plain": [
635 |        "(3411, 6)"
636 |       ]
637 |      },
638 |      "execution_count": 115,
639 |      "metadata": {},
640 |      "output_type": "execute_result"
641 |     }
642 |    ],
643 |    "source": [
644 |     "df.shape"
645 |    ]
646 |   },
647 |   {
648 |    "cell_type": "code",
649 |    "execution_count": 116,
650 |    "id": "54a2b64d-4730-44f3-b40c-409d098f78f9",
651 |    "metadata": {},
652 |    "outputs": [
653 |     {
654 |      "data": {
655 |       "text/html": [
656 |        "<div>\n",
657 |        "<style scoped>\n",
658 |        "    .dataframe tbody tr th:only-of-type {\n",
659 |        "        vertical-align: middle;\n",
660 |        "    }\n",
661 |        "\n",
662 |        "    .dataframe tbody tr th {\n",
663 |        "        vertical-align: top;\n",
664 |        "    }\n",
665 |        "\n",
666 |        "    .dataframe thead th {\n",
667 |        "        text-align: right;\n",
668 |        "    }\n",
669 |        "</style>\n",
670 |        "<table border=\"1\" class=\"dataframe\">\n",
671 |        "  <thead>\n",
672 |        "    <tr style=\"text-align: right;\">\n",
673 |        "      <th></th>\n",
674 |        "      <th>reviews</th>\n",
675 |        "      <th>stars</th>\n",
676 |        "      <th>date</th>\n",
677 |        "      <th>country</th>\n",
678 |        "      <th>verified</th>\n",
679 |        "      <th>corpus</th>\n",
680 |        "    </tr>\n",
681 |        "  </thead>\n",
682 |        "  <tbody>\n",
683 |        "    <tr>\n",
684 |        "      <th>0</th>\n",
685 |        "      <td>Not Verified | Worst experience ever. Outbound...</td>\n",
686 |        "      <td>5</td>\n",
687 |        "      <td>2022-11-07</td>\n",
688 |        "      <td>Italy</td>\n",
689 |        "      <td>False</td>\n",
690 |        "      <td>verified worst experience ever outbound flight...</td>\n",
691 |        "    </tr>\n",
692 |        "    <tr>\n",
693 |        "      <th>1</th>\n",
694 |        "      <td>✅ Trip Verified |  Check in was a shambles at ...</td>\n",
695 |        "      <td>1</td>\n",
696 |        "      <td>2022-11-07</td>\n",
697 |        "      <td>Malaysia</td>\n",
698 |        "      <td>True</td>\n",
699 |        "      <td>check shamble bwi counter open full flight bag...</td>\n",
700 |        "    </tr>\n",
701 |        "    <tr>\n",
702 |        "      <th>2</th>\n",
703 |        "      <td>✅ Trip Verified | Beyond disgusted with the fa...</td>\n",
704 |        "      <td>5</td>\n",
705 |        "      <td>2022-11-05</td>\n",
706 |        "      <td>United Arab Emirates</td>\n",
707 |        "      <td>True</td>\n",
708 |        "      <td>beyond disgusted fact baggage yet delivered we...</td>\n",
709 |        "    </tr>\n",
710 |        "    <tr>\n",
711 |        "      <th>3</th>\n",
712 |        "      <td>✅ Trip Verified | On July 19th 2022 I had subm...</td>\n",
713 |        "      <td>1</td>\n",
714 |        "      <td>2022-10-31</td>\n",
715 |        "      <td>United States</td>\n",
716 |        "      <td>True</td>\n",
717 |        "      <td>july th submitted complaint form regard fact b...</td>\n",
718 |        "    </tr>\n",
719 |        "    <tr>\n",
720 |        "      <th>4</th>\n",
721 |        "      <td>✅ Trip Verified |  I booked the flight on Oct ...</td>\n",
722 |        "      <td>1</td>\n",
723 |        "      <td>2022-10-31</td>\n",
724 |        "      <td>United States</td>\n",
725 |        "      <td>True</td>\n",
726 |        "      <td>booked flight oct cancel flight day learning g...</td>\n",
727 |        "    </tr>\n",
728 |        "    <tr>\n",
729 |        "      <th>...</th>\n",
730 |        "      <td>...</td>\n",
731 |        "      <td>...</td>\n",
732 |        "      <td>...</td>\n",
733 |        "      <td>...</td>\n",
734 |        "      <td>...</td>\n",
735 |        "      <td>...</td>\n",
736 |        "    </tr>\n",
737 |        "    <tr>\n",
738 |        "      <th>3406</th>\n",
739 |        "      <td>This was a bmi Regional operated flight on a R...</td>\n",
740 |        "      <td>1</td>\n",
741 |        "      <td>2012-08-29</td>\n",
742 |        "      <td>United Kingdom</td>\n",
743 |        "      <td>False</td>\n",
744 |        "      <td>bmi regional operated flight rj manchester hea...</td>\n",
745 |        "    </tr>\n",
746 |        "    <tr>\n",
747 |        "      <th>3407</th>\n",
748 |        "      <td>LHR to HAM. Purser addresses all club passenge...</td>\n",
749 |        "      <td>10</td>\n",
750 |        "      <td>2012-08-28</td>\n",
751 |        "      <td>United Kingdom</td>\n",
752 |        "      <td>False</td>\n",
753 |        "      <td>lhr ham purser address club passenger name boa...</td>\n",
754 |        "    </tr>\n",
755 |        "    <tr>\n",
756 |        "      <th>3408</th>\n",
757 |        "      <td>My son who had worked for British Airways urge...</td>\n",
758 |        "      <td>10</td>\n",
759 |        "      <td>2011-10-12</td>\n",
760 |        "      <td>United Kingdom</td>\n",
761 |        "      <td>False</td>\n",
762 |        "      <td>son worked british airway urged fly british ai...</td>\n",
763 |        "    </tr>\n",
764 |        "    <tr>\n",
765 |        "      <th>3409</th>\n",
766 |        "      <td>London City-New York JFK via Shannon on A318 b...</td>\n",
767 |        "      <td>8</td>\n",
768 |        "      <td>2011-10-11</td>\n",
769 |        "      <td>United States</td>\n",
770 |        "      <td>False</td>\n",
771 |        "      <td>london city new york jfk via shannon really ni...</td>\n",
772 |        "    </tr>\n",
773 |        "    <tr>\n",
774 |        "      <th>3410</th>\n",
775 |        "      <td>SIN-LHR BA12 B747-436 First Class. Old aircraf...</td>\n",
776 |        "      <td>9</td>\n",
777 |        "      <td>2011-10-09</td>\n",
778 |        "      <td>United Kingdom</td>\n",
779 |        "      <td>False</td>\n",
780 |        "      <td>sin lhr ba b first class old aircraft seat pri...</td>\n",
781 |        "    </tr>\n",
782 |        "  </tbody>\n",
783 |        "</table>\n",
784 |        "<p>3411 rows × 6 columns</p>\n",
785 |        "</div>"
786 |       ],
787 |       "text/plain": [
788 |        "                                                reviews stars       date  \\\n",
789 |        "0     Not Verified | Worst experience ever. Outbound...     5 2022-11-07   \n",
790 |        "1     ✅ Trip Verified |  Check in was a shambles at ...     1 2022-11-07   \n",
791 |        "2     ✅ Trip Verified | Beyond disgusted with the fa...     5 2022-11-05   \n",
792 |        "3     ✅ Trip Verified | On July 19th 2022 I had subm...     1 2022-10-31   \n",
793 |        "4     ✅ Trip Verified |  I booked the flight on Oct ...     1 2022-10-31   \n",
794 |        "...                                                 ...   ...        ...   \n",
795 |        "3406  This was a bmi Regional operated flight on a R...     1 2012-08-29   \n",
796 |        "3407  LHR to HAM. Purser addresses all club passenge...    10 2012-08-28   \n",
797 |        "3408  My son who had worked for British Airways urge...    10 2011-10-12   \n",
798 |        "3409  London City-New York JFK via Shannon on A318 b...     8 2011-10-11   \n",
799 |        "3410  SIN-LHR BA12 B747-436 First Class. Old aircraf...     9 2011-10-09   \n",
800 |        "\n",
801 |        "                   country  verified  \\\n",
802 |        "0                    Italy     False   \n",
803 |        "1                 Malaysia      True   \n",
804 |        "2     United Arab Emirates      True   \n",
805 |        "3            United States      True   \n",
806 |        "4            United States      True   \n",
807 |        "...                    ...       ...   \n",
808 |        "3406        United Kingdom     False   \n",
809 |        "3407        United Kingdom     False   \n",
810 |        "3408        United Kingdom     False   \n",
811 |        "3409         United States     False   \n",
812 |        "3410        United Kingdom     False   \n",
813 |        "\n",
814 |        "                                                 corpus  \n",
815 |        "0     verified worst experience ever outbound flight...  \n",
816 |        "1     check shamble bwi counter open full flight bag...  \n",
817 |        "2     beyond disgusted fact baggage yet delivered we...  \n",
818 |        "3     july th submitted complaint form regard fact b...  \n",
819 |        "4     booked flight oct cancel flight day learning g...  \n",
820 |        "...                                                 ...  \n",
821 |        "3406  bmi regional operated flight rj manchester hea...  \n",
822 |        "3407  lhr ham purser address club passenger name boa...  \n",
823 |        "3408  son worked british airway urged fly british ai...  \n",
824 |        "3409  london city new york jfk via shannon really ni...  \n",
825 |        "3410  sin lhr ba b first class old aircraft seat pri...  \n",
826 |        "\n",
827 |        "[3411 rows x 6 columns]"
828 |       ]
829 |      },
830 |      "execution_count": 116,
831 |      "metadata": {},
832 |      "output_type": "execute_result"
833 |     }
834 |    ],
835 |    "source": [
836 |     "#resetting the index\n",
837 |     "df.reset_index(drop=True)"
838 |    ]
839 |   },
840 |   {
841 |    "cell_type": "markdown",
842 |    "id": "9fa4efa3-5d41-4fa7-a5ac-a959e322fcc9",
843 |    "metadata": {},
844 |    "source": [
845 |     "*****"
846 |    ]
847 |   },
848 |   {
849 |    "cell_type": "markdown",
850 |    "id": "cbb39abb-e09a-4bad-9fa6-65bba66ed631",
851 |    "metadata": {},
852 |    "source": [
853 |     "Now our data is all cleaned and ready for data visualization and data analysis."
854 |    ]
855 |   },
856 |   {
857 |    "cell_type": "code",
858 |    "execution_count": 117,
859 |    "id": "d9f0183c-e72a-4028-9c9a-1f3dee87c342",
860 |    "metadata": {},
861 |    "outputs": [],
862 |    "source": [
863 |     "# export the cleaned data\n",
864 |     "\n",
865 |     "df.to_csv(cwd + \"/cleaned-BA-reviews.csv\")"
866 |    ]
867 |   },
868 |   {
869 |    "cell_type": "code",
870 |    "execution_count": null,
871 |    "id": "4f9bad2f-721b-445a-86f9-bc9e16c177ce",
872 |    "metadata": {},
873 |    "outputs": [],
874 |    "source": []
875 |   }
876 |  ],
877 |  "metadata": {
878 |   "kernelspec": {
879 |    "display_name": "Python 3 (ipykernel)",
880 |    "language": "python",
881 |    "name": "python3"
882 |   },
883 |   "language_info": {
884 |    "codemirror_mode": {
885 |     "name": "ipython",
886 |     "version": 3
887 |    },
888 |    "file_extension": ".py",
889 |    "mimetype": "text/x-python",
890 |    "name": "python",
891 |    "nbconvert_exporter": "python",
892 |    "pygments_lexer": "ipython3",
893 |    "version": "3.9.7"
894 |   }
895 |  },
896 |  "nbformat": 4,
897 |  "nbformat_minor": 5
898 | }
899 | 


--------------------------------------------------------------------------------
/Data Cleaning/.ipynb_checkpoints/Data Cleaning-checkpoint.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "e00b5383-b915-4a16-8ed0-476f979052b7",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "## Data Cleaning\n",
  9 |     "\n",
 10 |     "Now since we have extracted data from the website, it is not cleaned and ready to be analyzed yet. The reviews section will need to be cleaned for punctuations, spellings and other characters. "
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 83,
 16 |    "id": "69c14a0d-d637-4b7e-8714-31fb531b4954",
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "#imports\n",
 21 |     "\n",
 22 |     "import pandas as pd\n",
 23 |     "import matplotlib.pyplot as plt\n",
 24 |     "import seaborn as sns\n",
 25 |     "import os\n",
 26 |     "\n",
 27 |     "#regex\n",
 28 |     "import re"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "code",
 33 |    "execution_count": 84,
 34 |    "id": "c46b06dc-b9f5-4b9f-b52b-7f9b01ff7200",
 35 |    "metadata": {},
 36 |    "outputs": [],
 37 |    "source": [
 38 |     "#create a dataframe from csv file\n",
 39 |     "\n",
 40 |     "cwd = os.getcwd()\n",
 41 |     "\n",
 42 |     "df = pd.read_csv(cwd+\"/BA_reviews.csv\", index_col=0)"
 43 |    ]
 44 |   },
 45 |   {
 46 |    "cell_type": "code",
 47 |    "execution_count": 85,
 48 |    "id": "888d1113-bafd-4e6c-a020-c0e7b721ce50",
 49 |    "metadata": {},
 50 |    "outputs": [
 51 |     {
 52 |      "data": {
 53 |       "text/html": [
 54 |        "<div>\n",
 55 |        "<style scoped>\n",
 56 |        "    .dataframe tbody tr th:only-of-type {\n",
 57 |        "        vertical-align: middle;\n",
 58 |        "    }\n",
 59 |        "\n",
 60 |        "    .dataframe tbody tr th {\n",
 61 |        "        vertical-align: top;\n",
 62 |        "    }\n",
 63 |        "\n",
 64 |        "    .dataframe thead th {\n",
 65 |        "        text-align: right;\n",
 66 |        "    }\n",
 67 |        "</style>\n",
 68 |        "<table border=\"1\" class=\"dataframe\">\n",
 69 |        "  <thead>\n",
 70 |        "    <tr style=\"text-align: right;\">\n",
 71 |        "      <th></th>\n",
 72 |        "      <th>reviews</th>\n",
 73 |        "      <th>stars</th>\n",
 74 |        "      <th>date</th>\n",
 75 |        "      <th>country</th>\n",
 76 |        "    </tr>\n",
 77 |        "  </thead>\n",
 78 |        "  <tbody>\n",
 79 |        "    <tr>\n",
 80 |        "      <th>0</th>\n",
 81 |        "      <td>Not Verified | Worst experience ever. Outbound...</td>\n",
 82 |        "      <td>\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t5</td>\n",
 83 |        "      <td>7th November 2022</td>\n",
 84 |        "      <td>Italy</td>\n",
 85 |        "    </tr>\n",
 86 |        "    <tr>\n",
 87 |        "      <th>1</th>\n",
 88 |        "      <td>✅ Trip Verified |  Check in was a shambles at ...</td>\n",
 89 |        "      <td>1</td>\n",
 90 |        "      <td>7th November 2022</td>\n",
 91 |        "      <td>Malaysia</td>\n",
 92 |        "    </tr>\n",
 93 |        "    <tr>\n",
 94 |        "      <th>2</th>\n",
 95 |        "      <td>✅ Trip Verified | Beyond disgusted with the fa...</td>\n",
 96 |        "      <td>5</td>\n",
 97 |        "      <td>5th November 2022</td>\n",
 98 |        "      <td>United Arab Emirates</td>\n",
 99 |        "    </tr>\n",
100 |        "    <tr>\n",
101 |        "      <th>3</th>\n",
102 |        "      <td>✅ Trip Verified | On July 19th 2022 I had subm...</td>\n",
103 |        "      <td>1</td>\n",
104 |        "      <td>31st October 2022</td>\n",
105 |        "      <td>United States</td>\n",
106 |        "    </tr>\n",
107 |        "    <tr>\n",
108 |        "      <th>4</th>\n",
109 |        "      <td>✅ Trip Verified |  I booked the flight on Oct ...</td>\n",
110 |        "      <td>1</td>\n",
111 |        "      <td>31st October 2022</td>\n",
112 |        "      <td>United States</td>\n",
113 |        "    </tr>\n",
114 |        "  </tbody>\n",
115 |        "</table>\n",
116 |        "</div>"
117 |       ],
118 |       "text/plain": [
119 |        "                                             reviews  \\\n",
120 |        "0  Not Verified | Worst experience ever. Outbound...   \n",
121 |        "1  ✅ Trip Verified |  Check in was a shambles at ...   \n",
122 |        "2  ✅ Trip Verified | Beyond disgusted with the fa...   \n",
123 |        "3  ✅ Trip Verified | On July 19th 2022 I had subm...   \n",
124 |        "4  ✅ Trip Verified |  I booked the flight on Oct ...   \n",
125 |        "\n",
126 |        "                           stars               date               country  \n",
127 |        "0  \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t5  7th November 2022                 Italy  \n",
128 |        "1                              1  7th November 2022              Malaysia  \n",
129 |        "2                              5  5th November 2022  United Arab Emirates  \n",
130 |        "3                              1  31st October 2022         United States  \n",
131 |        "4                              1  31st October 2022         United States  "
132 |       ]
133 |      },
134 |      "execution_count": 85,
135 |      "metadata": {},
136 |      "output_type": "execute_result"
137 |     }
138 |    ],
139 |    "source": [
140 |     "df.head()"
141 |    ]
142 |   },
143 |   {
144 |    "cell_type": "markdown",
145 |    "id": "7aeb0b06-23f8-48bb-b2d4-b91455040038",
146 |    "metadata": {},
147 |    "source": [
148 |     "We will also create a column which mentions if the user is verified or not. "
149 |    ]
150 |   },
151 |   {
152 |    "cell_type": "code",
153 |    "execution_count": 86,
154 |    "id": "45908798-e042-4d71-8e01-395f53f2005d",
155 |    "metadata": {},
156 |    "outputs": [],
157 |    "source": [
158 |     "df['verified'] = df.reviews.str.contains(\"Trip Verified\")"
159 |    ]
160 |   },
161 |   {
162 |    "cell_type": "code",
163 |    "execution_count": 87,
164 |    "id": "ae4e6e23-4ecc-4c94-8526-5f645ff2f6a2",
165 |    "metadata": {},
166 |    "outputs": [
167 |     {
168 |      "data": {
169 |       "text/plain": [
170 |        "0       False\n",
171 |        "1        True\n",
172 |        "2        True\n",
173 |        "3        True\n",
174 |        "4        True\n",
175 |        "        ...  \n",
176 |        "3413    False\n",
177 |        "3414    False\n",
178 |        "3415    False\n",
179 |        "3416    False\n",
180 |        "3417    False\n",
181 |        "Name: verified, Length: 3418, dtype: bool"
182 |       ]
183 |      },
184 |      "execution_count": 87,
185 |      "metadata": {},
186 |      "output_type": "execute_result"
187 |     }
188 |    ],
189 |    "source": [
190 |     "df['verified']"
191 |    ]
192 |   },
193 |   {
194 |    "cell_type": "markdown",
195 |    "id": "cf266fe0-0c33-477a-9f1e-529a214e91c5",
196 |    "metadata": {},
197 |    "source": [
198 |     "### Cleaning Reviews"
199 |    ]
200 |   },
201 |   {
202 |    "cell_type": "markdown",
203 |    "id": "ac2bc6a6-8783-4873-b5e7-1719f997cdf2",
204 |    "metadata": {},
205 |    "source": [
206 |     "We will extract the column of reviews into a separate dataframe and clean it for semantic analysis"
207 |    ]
208 |   },
209 |   {
210 |    "cell_type": "code",
211 |    "execution_count": 88,
212 |    "id": "e4319089-b957-4b60-8f07-fc8cd3487f9e",
213 |    "metadata": {},
214 |    "outputs": [],
215 |    "source": [
216 |     "#for lemmatization of words we will use nltk library\n",
217 |     "from nltk.stem import WordNetLemmatizer\n",
218 |     "from nltk.corpus import stopwords\n",
219 |     "lemma = WordNetLemmatizer()\n",
220 |     "\n",
221 |     "\n",
222 |     "reviews_data = df.reviews.str.strip(\"✅ Trip Verified |\")\n",
223 |     "\n",
224 |     "#create an empty list to collect cleaned data corpus\n",
225 |     "corpus =[]\n",
226 |     "\n",
227 |     "#loop through each review, remove punctuations, small case it, join it and add it to corpus\n",
228 |     "for rev in reviews_data:\n",
229 |     "    rev = re.sub('[^a-zA-Z]',' ', rev)\n",
230 |     "    rev = rev.lower()\n",
231 |     "    rev = rev.split()\n",
232 |     "    rev = [lemma.lemmatize(word) for word in rev if word not in set(stopwords.words(\"english\"))]\n",
233 |     "    rev = \" \".join(rev)\n",
234 |     "    corpus.append(rev)"
235 |    ]
236 |   },
237 |   {
238 |    "cell_type": "code",
239 |    "execution_count": 89,
240 |    "id": "835b9930-20e0-4d79-8a88-a5737309b4ce",
241 |    "metadata": {},
242 |    "outputs": [],
243 |    "source": [
244 |     "# add the corpus to the original dataframe\n",
245 |     "\n",
246 |     "df['corpus'] = corpus"
247 |    ]
248 |   },
249 |   {
250 |    "cell_type": "code",
251 |    "execution_count": 90,
252 |    "id": "baa16beb-4ac5-4850-a46a-0ae11d64cf3d",
253 |    "metadata": {},
254 |    "outputs": [
255 |     {
256 |      "data": {
257 |       "text/html": [
258 |        "<div>\n",
259 |        "<style scoped>\n",
260 |        "    .dataframe tbody tr th:only-of-type {\n",
261 |        "        vertical-align: middle;\n",
262 |        "    }\n",
263 |        "\n",
264 |        "    .dataframe tbody tr th {\n",
265 |        "        vertical-align: top;\n",
266 |        "    }\n",
267 |        "\n",
268 |        "    .dataframe thead th {\n",
269 |        "        text-align: right;\n",
270 |        "    }\n",
271 |        "</style>\n",
272 |        "<table border=\"1\" class=\"dataframe\">\n",
273 |        "  <thead>\n",
274 |        "    <tr style=\"text-align: right;\">\n",
275 |        "      <th></th>\n",
276 |        "      <th>reviews</th>\n",
277 |        "      <th>stars</th>\n",
278 |        "      <th>date</th>\n",
279 |        "      <th>country</th>\n",
280 |        "      <th>verified</th>\n",
281 |        "      <th>corpus</th>\n",
282 |        "    </tr>\n",
283 |        "  </thead>\n",
284 |        "  <tbody>\n",
285 |        "    <tr>\n",
286 |        "      <th>0</th>\n",
287 |        "      <td>Not Verified | Worst experience ever. Outbound...</td>\n",
288 |        "      <td>\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t5</td>\n",
289 |        "      <td>7th November 2022</td>\n",
290 |        "      <td>Italy</td>\n",
291 |        "      <td>False</td>\n",
292 |        "      <td>verified worst experience ever outbound flight...</td>\n",
293 |        "    </tr>\n",
294 |        "    <tr>\n",
295 |        "      <th>1</th>\n",
296 |        "      <td>✅ Trip Verified |  Check in was a shambles at ...</td>\n",
297 |        "      <td>1</td>\n",
298 |        "      <td>7th November 2022</td>\n",
299 |        "      <td>Malaysia</td>\n",
300 |        "      <td>True</td>\n",
301 |        "      <td>check shamble bwi counter open full flight bag...</td>\n",
302 |        "    </tr>\n",
303 |        "    <tr>\n",
304 |        "      <th>2</th>\n",
305 |        "      <td>✅ Trip Verified | Beyond disgusted with the fa...</td>\n",
306 |        "      <td>5</td>\n",
307 |        "      <td>5th November 2022</td>\n",
308 |        "      <td>United Arab Emirates</td>\n",
309 |        "      <td>True</td>\n",
310 |        "      <td>beyond disgusted fact baggage yet delivered we...</td>\n",
311 |        "    </tr>\n",
312 |        "    <tr>\n",
313 |        "      <th>3</th>\n",
314 |        "      <td>✅ Trip Verified | On July 19th 2022 I had subm...</td>\n",
315 |        "      <td>1</td>\n",
316 |        "      <td>31st October 2022</td>\n",
317 |        "      <td>United States</td>\n",
318 |        "      <td>True</td>\n",
319 |        "      <td>july th submitted complaint form regard fact b...</td>\n",
320 |        "    </tr>\n",
321 |        "    <tr>\n",
322 |        "      <th>4</th>\n",
323 |        "      <td>✅ Trip Verified |  I booked the flight on Oct ...</td>\n",
324 |        "      <td>1</td>\n",
325 |        "      <td>31st October 2022</td>\n",
326 |        "      <td>United States</td>\n",
327 |        "      <td>True</td>\n",
328 |        "      <td>booked flight oct cancel flight day learning g...</td>\n",
329 |        "    </tr>\n",
330 |        "  </tbody>\n",
331 |        "</table>\n",
332 |        "</div>"
333 |       ],
334 |       "text/plain": [
335 |        "                                             reviews  \\\n",
336 |        "0  Not Verified | Worst experience ever. Outbound...   \n",
337 |        "1  ✅ Trip Verified |  Check in was a shambles at ...   \n",
338 |        "2  ✅ Trip Verified | Beyond disgusted with the fa...   \n",
339 |        "3  ✅ Trip Verified | On July 19th 2022 I had subm...   \n",
340 |        "4  ✅ Trip Verified |  I booked the flight on Oct ...   \n",
341 |        "\n",
342 |        "                           stars               date               country  \\\n",
343 |        "0  \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t5  7th November 2022                 Italy   \n",
344 |        "1                              1  7th November 2022              Malaysia   \n",
345 |        "2                              5  5th November 2022  United Arab Emirates   \n",
346 |        "3                              1  31st October 2022         United States   \n",
347 |        "4                              1  31st October 2022         United States   \n",
348 |        "\n",
349 |        "   verified                                             corpus  \n",
350 |        "0     False  verified worst experience ever outbound flight...  \n",
351 |        "1      True  check shamble bwi counter open full flight bag...  \n",
352 |        "2      True  beyond disgusted fact baggage yet delivered we...  \n",
353 |        "3      True  july th submitted complaint form regard fact b...  \n",
354 |        "4      True  booked flight oct cancel flight day learning g...  "
355 |       ]
356 |      },
357 |      "execution_count": 90,
358 |      "metadata": {},
359 |      "output_type": "execute_result"
360 |     }
361 |    ],
362 |    "source": [
363 |     "df.head()"
364 |    ]
365 |   },
366 |   {
367 |    "cell_type": "markdown",
368 |    "id": "63424d81-bab2-4e74-aa97-6094bd035940",
369 |    "metadata": {},
370 |    "source": [
371 |     "### Cleaning/Fromat date"
372 |    ]
373 |   },
374 |   {
375 |    "cell_type": "code",
376 |    "execution_count": 91,
377 |    "id": "33f1a718-af12-4fa4-85ae-f5511e8986ae",
378 |    "metadata": {},
379 |    "outputs": [
380 |     {
381 |      "data": {
382 |       "text/plain": [
383 |        "reviews     object\n",
384 |        "stars       object\n",
385 |        "date        object\n",
386 |        "country     object\n",
387 |        "verified      bool\n",
388 |        "corpus      object\n",
389 |        "dtype: object"
390 |       ]
391 |      },
392 |      "execution_count": 91,
393 |      "metadata": {},
394 |      "output_type": "execute_result"
395 |     }
396 |    ],
397 |    "source": [
398 |     "df.dtypes"
399 |    ]
400 |   },
401 |   {
402 |    "cell_type": "code",
403 |    "execution_count": 92,
404 |    "id": "c773fca4-23ee-4f7d-a1db-8da648ddb161",
405 |    "metadata": {},
406 |    "outputs": [],
407 |    "source": [
408 |     "# convert the date to datetime format\n",
409 |     "\n",
410 |     "df.date = pd.to_datetime(df.date)"
411 |    ]
412 |   },
413 |   {
414 |    "cell_type": "code",
415 |    "execution_count": 93,
416 |    "id": "0440c89e-9a89-4ef4-888f-eb25ad833afb",
417 |    "metadata": {},
418 |    "outputs": [
419 |     {
420 |      "data": {
421 |       "text/plain": [
422 |        "0   2022-11-07\n",
423 |        "1   2022-11-07\n",
424 |        "2   2022-11-05\n",
425 |        "3   2022-10-31\n",
426 |        "4   2022-10-31\n",
427 |        "Name: date, dtype: datetime64[ns]"
428 |       ]
429 |      },
430 |      "execution_count": 93,
431 |      "metadata": {},
432 |      "output_type": "execute_result"
433 |     }
434 |    ],
435 |    "source": [
436 |     "df.date.head()"
437 |    ]
438 |   },
439 |   {
440 |    "cell_type": "markdown",
441 |    "id": "a7973868-80c4-46b9-9449-6761fac91f64",
442 |    "metadata": {},
443 |    "source": [
444 |     "### Cleaning ratings with stars"
445 |    ]
446 |   },
447 |   {
448 |    "cell_type": "code",
449 |    "execution_count": 100,
450 |    "id": "4f535799-1f5a-43d2-a858-5639f35b073c",
451 |    "metadata": {},
452 |    "outputs": [
453 |     {
454 |      "data": {
455 |       "text/plain": [
456 |        "array(['\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t5', '1', '5', '6', '9', '3', '2', '8',\n",
457 |        "       '7', '10', '4', 'None'], dtype=object)"
458 |       ]
459 |      },
460 |      "execution_count": 100,
461 |      "metadata": {},
462 |      "output_type": "execute_result"
463 |     }
464 |    ],
465 |    "source": [
466 |     "#check for unique values\n",
467 |     "df.stars.unique()"
468 |    ]
469 |   },
470 |   {
471 |    "cell_type": "code",
472 |    "execution_count": 102,
473 |    "id": "5664b1c3-17d3-4f54-8799-388443a6242d",
474 |    "metadata": {},
475 |    "outputs": [],
476 |    "source": [
477 |     "# remove the \\t and \\n from the ratings\n",
478 |     "df.stars = df.stars.str.strip(\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\")"
479 |    ]
480 |   },
481 |   {
482 |    "cell_type": "code",
483 |    "execution_count": 105,
484 |    "id": "9cbfaa06-b519-4ce1-bb50-420692a923d6",
485 |    "metadata": {},
486 |    "outputs": [
487 |     {
488 |      "data": {
489 |       "text/plain": [
490 |        "1       735\n",
491 |        "2       382\n",
492 |        "3       379\n",
493 |        "8       349\n",
494 |        "10      306\n",
495 |        "7       299\n",
496 |        "9       293\n",
497 |        "5       259\n",
498 |        "4       227\n",
499 |        "6       182\n",
500 |        "None      5\n",
501 |        "Name: stars, dtype: int64"
502 |       ]
503 |      },
504 |      "execution_count": 105,
505 |      "metadata": {},
506 |      "output_type": "execute_result"
507 |     }
508 |    ],
509 |    "source": [
510 |     "df.stars.value_counts()"
511 |    ]
512 |   },
513 |   {
514 |    "cell_type": "markdown",
515 |    "id": "23941023-6f4d-44a3-9ae5-34ba7270a646",
516 |    "metadata": {},
517 |    "source": [
518 |     "There are 5 rows having values \"None\" in the ratings. We will drop all these 5 rows. "
519 |    ]
520 |   },
521 |   {
522 |    "cell_type": "code",
523 |    "execution_count": 110,
524 |    "id": "e5d3e147-b454-4515-8b14-54e4ec756b1c",
525 |    "metadata": {},
526 |    "outputs": [],
527 |    "source": [
528 |     "# drop the rows where the value of ratings is None\n",
529 |     "df.drop(df[df.stars == \"None\"].index, axis=0, inplace=True)"
530 |    ]
531 |   },
532 |   {
533 |    "cell_type": "code",
534 |    "execution_count": 111,
535 |    "id": "e152aed3-2da4-4ee9-90dc-d2bd3553d870",
536 |    "metadata": {},
537 |    "outputs": [
538 |     {
539 |      "data": {
540 |       "text/plain": [
541 |        "array(['5', '1', '6', '9', '3', '2', '8', '7', '10', '4'], dtype=object)"
542 |       ]
543 |      },
544 |      "execution_count": 111,
545 |      "metadata": {},
546 |      "output_type": "execute_result"
547 |     }
548 |    ],
549 |    "source": [
550 |     "#check the unique values again\n",
551 |     "df.stars.unique()"
552 |    ]
553 |   },
554 |   {
555 |    "cell_type": "markdown",
556 |    "id": "c29e569e-bab0-4d73-8496-f60aa375be38",
557 |    "metadata": {},
558 |    "source": [
559 |     "## Check for null Values"
560 |    ]
561 |   },
562 |   {
563 |    "cell_type": "code",
564 |    "execution_count": 112,
565 |    "id": "e81950f2-0cb3-4c1a-9859-233f4c3304d7",
566 |    "metadata": {},
567 |    "outputs": [
568 |     {
569 |      "data": {
570 |       "text/plain": [
571 |        "reviews  stars  date   country  verified  corpus\n",
572 |        "False    False  False  False    False     False     3411\n",
573 |        "dtype: int64"
574 |       ]
575 |      },
576 |      "execution_count": 112,
577 |      "metadata": {},
578 |      "output_type": "execute_result"
579 |     }
580 |    ],
581 |    "source": [
582 |     "df.isnull().value_counts()"
583 |    ]
584 |   },
585 |   {
586 |    "cell_type": "code",
587 |    "execution_count": 113,
588 |    "id": "7c53f6a8-3f14-4711-a11a-a99f72015431",
589 |    "metadata": {},
590 |    "outputs": [
591 |     {
592 |      "data": {
593 |       "text/plain": [
594 |        "False    3411\n",
595 |        "Name: country, dtype: int64"
596 |       ]
597 |      },
598 |      "execution_count": 113,
599 |      "metadata": {},
600 |      "output_type": "execute_result"
601 |     }
602 |    ],
603 |    "source": [
604 |     "df.country.isnull().value_counts()"
605 |    ]
606 |   },
607 |   {
608 |    "cell_type": "markdown",
609 |    "id": "3d9dbc68-9826-49e0-a003-d1c0c5e5d4f6",
610 |    "metadata": {},
611 |    "source": [
612 |     "We have two missing values for country. For this we can just remove those two reviews (rows) from the dataframe. "
613 |    ]
614 |   },
615 |   {
616 |    "cell_type": "code",
617 |    "execution_count": 114,
618 |    "id": "98b5b16a-c25d-475b-8894-e680323195eb",
619 |    "metadata": {},
620 |    "outputs": [],
621 |    "source": [
622 |     "#drop the rows using index where the country value is null\n",
623 |     "df.drop(df[df.country.isnull() == True].index, axis=0, inplace=True)"
624 |    ]
625 |   },
626 |   {
627 |    "cell_type": "code",
628 |    "execution_count": 115,
629 |    "id": "116656f9-0f7a-4d79-8f69-7f4fbf711eca",
630 |    "metadata": {},
631 |    "outputs": [
632 |     {
633 |      "data": {
634 |       "text/plain": [
635 |        "(3411, 6)"
636 |       ]
637 |      },
638 |      "execution_count": 115,
639 |      "metadata": {},
640 |      "output_type": "execute_result"
641 |     }
642 |    ],
643 |    "source": [
644 |     "df.shape"
645 |    ]
646 |   },
647 |   {
648 |    "cell_type": "code",
649 |    "execution_count": 116,
650 |    "id": "54a2b64d-4730-44f3-b40c-409d098f78f9",
651 |    "metadata": {},
652 |    "outputs": [
653 |     {
654 |      "data": {
655 |       "text/html": [
656 |        "<div>\n",
657 |        "<style scoped>\n",
658 |        "    .dataframe tbody tr th:only-of-type {\n",
659 |        "        vertical-align: middle;\n",
660 |        "    }\n",
661 |        "\n",
662 |        "    .dataframe tbody tr th {\n",
663 |        "        vertical-align: top;\n",
664 |        "    }\n",
665 |        "\n",
666 |        "    .dataframe thead th {\n",
667 |        "        text-align: right;\n",
668 |        "    }\n",
669 |        "</style>\n",
670 |        "<table border=\"1\" class=\"dataframe\">\n",
671 |        "  <thead>\n",
672 |        "    <tr style=\"text-align: right;\">\n",
673 |        "      <th></th>\n",
674 |        "      <th>reviews</th>\n",
675 |        "      <th>stars</th>\n",
676 |        "      <th>date</th>\n",
677 |        "      <th>country</th>\n",
678 |        "      <th>verified</th>\n",
679 |        "      <th>corpus</th>\n",
680 |        "    </tr>\n",
681 |        "  </thead>\n",
682 |        "  <tbody>\n",
683 |        "    <tr>\n",
684 |        "      <th>0</th>\n",
685 |        "      <td>Not Verified | Worst experience ever. Outbound...</td>\n",
686 |        "      <td>5</td>\n",
687 |        "      <td>2022-11-07</td>\n",
688 |        "      <td>Italy</td>\n",
689 |        "      <td>False</td>\n",
690 |        "      <td>verified worst experience ever outbound flight...</td>\n",
691 |        "    </tr>\n",
692 |        "    <tr>\n",
693 |        "      <th>1</th>\n",
694 |        "      <td>✅ Trip Verified |  Check in was a shambles at ...</td>\n",
695 |        "      <td>1</td>\n",
696 |        "      <td>2022-11-07</td>\n",
697 |        "      <td>Malaysia</td>\n",
698 |        "      <td>True</td>\n",
699 |        "      <td>check shamble bwi counter open full flight bag...</td>\n",
700 |        "    </tr>\n",
701 |        "    <tr>\n",
702 |        "      <th>2</th>\n",
703 |        "      <td>✅ Trip Verified | Beyond disgusted with the fa...</td>\n",
704 |        "      <td>5</td>\n",
705 |        "      <td>2022-11-05</td>\n",
706 |        "      <td>United Arab Emirates</td>\n",
707 |        "      <td>True</td>\n",
708 |        "      <td>beyond disgusted fact baggage yet delivered we...</td>\n",
709 |        "    </tr>\n",
710 |        "    <tr>\n",
711 |        "      <th>3</th>\n",
712 |        "      <td>✅ Trip Verified | On July 19th 2022 I had subm...</td>\n",
713 |        "      <td>1</td>\n",
714 |        "      <td>2022-10-31</td>\n",
715 |        "      <td>United States</td>\n",
716 |        "      <td>True</td>\n",
717 |        "      <td>july th submitted complaint form regard fact b...</td>\n",
718 |        "    </tr>\n",
719 |        "    <tr>\n",
720 |        "      <th>4</th>\n",
721 |        "      <td>✅ Trip Verified |  I booked the flight on Oct ...</td>\n",
722 |        "      <td>1</td>\n",
723 |        "      <td>2022-10-31</td>\n",
724 |        "      <td>United States</td>\n",
725 |        "      <td>True</td>\n",
726 |        "      <td>booked flight oct cancel flight day learning g...</td>\n",
727 |        "    </tr>\n",
728 |        "    <tr>\n",
729 |        "      <th>...</th>\n",
730 |        "      <td>...</td>\n",
731 |        "      <td>...</td>\n",
732 |        "      <td>...</td>\n",
733 |        "      <td>...</td>\n",
734 |        "      <td>...</td>\n",
735 |        "      <td>...</td>\n",
736 |        "    </tr>\n",
737 |        "    <tr>\n",
738 |        "      <th>3406</th>\n",
739 |        "      <td>This was a bmi Regional operated flight on a R...</td>\n",
740 |        "      <td>1</td>\n",
741 |        "      <td>2012-08-29</td>\n",
742 |        "      <td>United Kingdom</td>\n",
743 |        "      <td>False</td>\n",
744 |        "      <td>bmi regional operated flight rj manchester hea...</td>\n",
745 |        "    </tr>\n",
746 |        "    <tr>\n",
747 |        "      <th>3407</th>\n",
748 |        "      <td>LHR to HAM. Purser addresses all club passenge...</td>\n",
749 |        "      <td>10</td>\n",
750 |        "      <td>2012-08-28</td>\n",
751 |        "      <td>United Kingdom</td>\n",
752 |        "      <td>False</td>\n",
753 |        "      <td>lhr ham purser address club passenger name boa...</td>\n",
754 |        "    </tr>\n",
755 |        "    <tr>\n",
756 |        "      <th>3408</th>\n",
757 |        "      <td>My son who had worked for British Airways urge...</td>\n",
758 |        "      <td>10</td>\n",
759 |        "      <td>2011-10-12</td>\n",
760 |        "      <td>United Kingdom</td>\n",
761 |        "      <td>False</td>\n",
762 |        "      <td>son worked british airway urged fly british ai...</td>\n",
763 |        "    </tr>\n",
764 |        "    <tr>\n",
765 |        "      <th>3409</th>\n",
766 |        "      <td>London City-New York JFK via Shannon on A318 b...</td>\n",
767 |        "      <td>8</td>\n",
768 |        "      <td>2011-10-11</td>\n",
769 |        "      <td>United States</td>\n",
770 |        "      <td>False</td>\n",
771 |        "      <td>london city new york jfk via shannon really ni...</td>\n",
772 |        "    </tr>\n",
773 |        "    <tr>\n",
774 |        "      <th>3410</th>\n",
775 |        "      <td>SIN-LHR BA12 B747-436 First Class. Old aircraf...</td>\n",
776 |        "      <td>9</td>\n",
777 |        "      <td>2011-10-09</td>\n",
778 |        "      <td>United Kingdom</td>\n",
779 |        "      <td>False</td>\n",
780 |        "      <td>sin lhr ba b first class old aircraft seat pri...</td>\n",
781 |        "    </tr>\n",
782 |        "  </tbody>\n",
783 |        "</table>\n",
784 |        "<p>3411 rows × 6 columns</p>\n",
785 |        "</div>"
786 |       ],
787 |       "text/plain": [
788 |        "                                                reviews stars       date  \\\n",
789 |        "0     Not Verified | Worst experience ever. Outbound...     5 2022-11-07   \n",
790 |        "1     ✅ Trip Verified |  Check in was a shambles at ...     1 2022-11-07   \n",
791 |        "2     ✅ Trip Verified | Beyond disgusted with the fa...     5 2022-11-05   \n",
792 |        "3     ✅ Trip Verified | On July 19th 2022 I had subm...     1 2022-10-31   \n",
793 |        "4     ✅ Trip Verified |  I booked the flight on Oct ...     1 2022-10-31   \n",
794 |        "...                                                 ...   ...        ...   \n",
795 |        "3406  This was a bmi Regional operated flight on a R...     1 2012-08-29   \n",
796 |        "3407  LHR to HAM. Purser addresses all club passenge...    10 2012-08-28   \n",
797 |        "3408  My son who had worked for British Airways urge...    10 2011-10-12   \n",
798 |        "3409  London City-New York JFK via Shannon on A318 b...     8 2011-10-11   \n",
799 |        "3410  SIN-LHR BA12 B747-436 First Class. Old aircraf...     9 2011-10-09   \n",
800 |        "\n",
801 |        "                   country  verified  \\\n",
802 |        "0                    Italy     False   \n",
803 |        "1                 Malaysia      True   \n",
804 |        "2     United Arab Emirates      True   \n",
805 |        "3            United States      True   \n",
806 |        "4            United States      True   \n",
807 |        "...                    ...       ...   \n",
808 |        "3406        United Kingdom     False   \n",
809 |        "3407        United Kingdom     False   \n",
810 |        "3408        United Kingdom     False   \n",
811 |        "3409         United States     False   \n",
812 |        "3410        United Kingdom     False   \n",
813 |        "\n",
814 |        "                                                 corpus  \n",
815 |        "0     verified worst experience ever outbound flight...  \n",
816 |        "1     check shamble bwi counter open full flight bag...  \n",
817 |        "2     beyond disgusted fact baggage yet delivered we...  \n",
818 |        "3     july th submitted complaint form regard fact b...  \n",
819 |        "4     booked flight oct cancel flight day learning g...  \n",
820 |        "...                                                 ...  \n",
821 |        "3406  bmi regional operated flight rj manchester hea...  \n",
822 |        "3407  lhr ham purser address club passenger name boa...  \n",
823 |        "3408  son worked british airway urged fly british ai...  \n",
824 |        "3409  london city new york jfk via shannon really ni...  \n",
825 |        "3410  sin lhr ba b first class old aircraft seat pri...  \n",
826 |        "\n",
827 |        "[3411 rows x 6 columns]"
828 |       ]
829 |      },
830 |      "execution_count": 116,
831 |      "metadata": {},
832 |      "output_type": "execute_result"
833 |     }
834 |    ],
835 |    "source": [
836 |     "#resetting the index\n",
837 |     "df.reset_index(drop=True)"
838 |    ]
839 |   },
840 |   {
841 |    "cell_type": "markdown",
842 |    "id": "9fa4efa3-5d41-4fa7-a5ac-a959e322fcc9",
843 |    "metadata": {},
844 |    "source": [
845 |     "*****"
846 |    ]
847 |   },
848 |   {
849 |    "cell_type": "markdown",
850 |    "id": "cbb39abb-e09a-4bad-9fa6-65bba66ed631",
851 |    "metadata": {},
852 |    "source": [
853 |     "Now our data is all cleaned and ready for data visualization and data analysis."
854 |    ]
855 |   },
856 |   {
857 |    "cell_type": "code",
858 |    "execution_count": 117,
859 |    "id": "d9f0183c-e72a-4028-9c9a-1f3dee87c342",
860 |    "metadata": {},
861 |    "outputs": [],
862 |    "source": [
863 |     "# export the cleaned data\n",
864 |     "\n",
865 |     "df.to_csv(cwd + \"/cleaned-BA-reviews.csv\")"
866 |    ]
867 |   },
868 |   {
869 |    "cell_type": "code",
870 |    "execution_count": null,
871 |    "id": "4f9bad2f-721b-445a-86f9-bc9e16c177ce",
872 |    "metadata": {},
873 |    "outputs": [],
874 |    "source": []
875 |   }
876 |  ],
877 |  "metadata": {
878 |   "kernelspec": {
879 |    "display_name": "Python 3 (ipykernel)",
880 |    "language": "python",
881 |    "name": "python3"
882 |   },
883 |   "language_info": {
884 |    "codemirror_mode": {
885 |     "name": "ipython",
886 |     "version": 3
887 |    },
888 |    "file_extension": ".py",
889 |    "mimetype": "text/x-python",
890 |    "name": "python",
891 |    "nbconvert_exporter": "python",
892 |    "pygments_lexer": "ipython3",
893 |    "version": "3.9.7"
894 |   }
895 |  },
896 |  "nbformat": 4,
897 |  "nbformat_minor": 5
898 | }
899 | 


--------------------------------------------------------------------------------
/Predictive Modeling/.ipynb_checkpoints/Explore-customer-data-checkpoint.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "id": "e356b851-f7a9-4f12-ae70-93d26dee453b",
   6 |    "metadata": {},
   7 |    "source": [
   8 |     "## Exploratory Data Analysis on Customer Bookings data for British Airways"
   9 |    ]
  10 |   },
  11 |   {
  12 |    "cell_type": "markdown",
  13 |    "id": "d7d67fea-8f6b-4e3c-a414-2e821ca8b442",
  14 |    "metadata": {},
  15 |    "source": [
  16 |     "We will explore the customer data first to get to know it better in depth.\n"
  17 |    ]
  18 |   },
  19 |   {
  20 |    "cell_type": "code",
  21 |    "execution_count": 37,
  22 |    "id": "f8340639-d648-4236-8a50-b27bf8157449",
  23 |    "metadata": {},
  24 |    "outputs": [],
  25 |    "source": [
  26 |     "#imports\n",
  27 |     "\n",
  28 |     "import pandas as pd\n",
  29 |     "import numpy as np\n",
  30 |     "import os\n",
  31 |     "\n",
  32 |     "import matplotlib.pyplot as plt\n",
  33 |     "import seaborn as sns"
  34 |    ]
  35 |   },
  36 |   {
  37 |    "cell_type": "code",
  38 |    "execution_count": 6,
  39 |    "id": "553a9c80-8691-4b5e-9a51-4cb0a72d3737",
  40 |    "metadata": {},
  41 |    "outputs": [],
  42 |    "source": [
  43 |     "#get current working directory\n",
  44 |     "\n",
  45 |     "cwd = os.getcwd()\n",
  46 |     "\n",
  47 |     "#read the csv\n",
  48 |     "\n",
  49 |     "df = pd.read_csv(cwd + \"/customer_booking.csv\",  encoding=\"ISO-8859-1\")"
  50 |    ]
  51 |   },
  52 |   {
  53 |    "cell_type": "code",
  54 |    "execution_count": 8,
  55 |    "id": "1f195839-980d-4dfa-adf2-533bfeef3f94",
  56 |    "metadata": {},
  57 |    "outputs": [
  58 |     {
  59 |      "data": {
  60 |       "text/html": [
  61 |        "<div>\n",
  62 |        "<style scoped>\n",
  63 |        "    .dataframe tbody tr th:only-of-type {\n",
  64 |        "        vertical-align: middle;\n",
  65 |        "    }\n",
  66 |        "\n",
  67 |        "    .dataframe tbody tr th {\n",
  68 |        "        vertical-align: top;\n",
  69 |        "    }\n",
  70 |        "\n",
  71 |        "    .dataframe thead th {\n",
  72 |        "        text-align: right;\n",
  73 |        "    }\n",
  74 |        "</style>\n",
  75 |        "<table border=\"1\" class=\"dataframe\">\n",
  76 |        "  <thead>\n",
  77 |        "    <tr style=\"text-align: right;\">\n",
  78 |        "      <th></th>\n",
  79 |        "      <th>num_passengers</th>\n",
  80 |        "      <th>sales_channel</th>\n",
  81 |        "      <th>trip_type</th>\n",
  82 |        "      <th>purchase_lead</th>\n",
  83 |        "      <th>length_of_stay</th>\n",
  84 |        "      <th>flight_hour</th>\n",
  85 |        "      <th>flight_day</th>\n",
  86 |        "      <th>route</th>\n",
  87 |        "      <th>booking_origin</th>\n",
  88 |        "      <th>wants_extra_baggage</th>\n",
  89 |        "      <th>wants_preferred_seat</th>\n",
  90 |        "      <th>wants_in_flight_meals</th>\n",
  91 |        "      <th>flight_duration</th>\n",
  92 |        "      <th>booking_complete</th>\n",
  93 |        "    </tr>\n",
  94 |        "  </thead>\n",
  95 |        "  <tbody>\n",
  96 |        "    <tr>\n",
  97 |        "      <th>0</th>\n",
  98 |        "      <td>2</td>\n",
  99 |        "      <td>Internet</td>\n",
 100 |        "      <td>RoundTrip</td>\n",
 101 |        "      <td>262</td>\n",
 102 |        "      <td>19</td>\n",
 103 |        "      <td>7</td>\n",
 104 |        "      <td>Sat</td>\n",
 105 |        "      <td>AKLDEL</td>\n",
 106 |        "      <td>New Zealand</td>\n",
 107 |        "      <td>1</td>\n",
 108 |        "      <td>0</td>\n",
 109 |        "      <td>0</td>\n",
 110 |        "      <td>5.52</td>\n",
 111 |        "      <td>0</td>\n",
 112 |        "    </tr>\n",
 113 |        "    <tr>\n",
 114 |        "      <th>1</th>\n",
 115 |        "      <td>1</td>\n",
 116 |        "      <td>Internet</td>\n",
 117 |        "      <td>RoundTrip</td>\n",
 118 |        "      <td>112</td>\n",
 119 |        "      <td>20</td>\n",
 120 |        "      <td>3</td>\n",
 121 |        "      <td>Sat</td>\n",
 122 |        "      <td>AKLDEL</td>\n",
 123 |        "      <td>New Zealand</td>\n",
 124 |        "      <td>0</td>\n",
 125 |        "      <td>0</td>\n",
 126 |        "      <td>0</td>\n",
 127 |        "      <td>5.52</td>\n",
 128 |        "      <td>0</td>\n",
 129 |        "    </tr>\n",
 130 |        "    <tr>\n",
 131 |        "      <th>2</th>\n",
 132 |        "      <td>2</td>\n",
 133 |        "      <td>Internet</td>\n",
 134 |        "      <td>RoundTrip</td>\n",
 135 |        "      <td>243</td>\n",
 136 |        "      <td>22</td>\n",
 137 |        "      <td>17</td>\n",
 138 |        "      <td>Wed</td>\n",
 139 |        "      <td>AKLDEL</td>\n",
 140 |        "      <td>India</td>\n",
 141 |        "      <td>1</td>\n",
 142 |        "      <td>1</td>\n",
 143 |        "      <td>0</td>\n",
 144 |        "      <td>5.52</td>\n",
 145 |        "      <td>0</td>\n",
 146 |        "    </tr>\n",
 147 |        "    <tr>\n",
 148 |        "      <th>3</th>\n",
 149 |        "      <td>1</td>\n",
 150 |        "      <td>Internet</td>\n",
 151 |        "      <td>RoundTrip</td>\n",
 152 |        "      <td>96</td>\n",
 153 |        "      <td>31</td>\n",
 154 |        "      <td>4</td>\n",
 155 |        "      <td>Sat</td>\n",
 156 |        "      <td>AKLDEL</td>\n",
 157 |        "      <td>New Zealand</td>\n",
 158 |        "      <td>0</td>\n",
 159 |        "      <td>0</td>\n",
 160 |        "      <td>1</td>\n",
 161 |        "      <td>5.52</td>\n",
 162 |        "      <td>0</td>\n",
 163 |        "    </tr>\n",
 164 |        "    <tr>\n",
 165 |        "      <th>4</th>\n",
 166 |        "      <td>2</td>\n",
 167 |        "      <td>Internet</td>\n",
 168 |        "      <td>RoundTrip</td>\n",
 169 |        "      <td>68</td>\n",
 170 |        "      <td>22</td>\n",
 171 |        "      <td>15</td>\n",
 172 |        "      <td>Wed</td>\n",
 173 |        "      <td>AKLDEL</td>\n",
 174 |        "      <td>India</td>\n",
 175 |        "      <td>1</td>\n",
 176 |        "      <td>0</td>\n",
 177 |        "      <td>1</td>\n",
 178 |        "      <td>5.52</td>\n",
 179 |        "      <td>0</td>\n",
 180 |        "    </tr>\n",
 181 |        "  </tbody>\n",
 182 |        "</table>\n",
 183 |        "</div>"
 184 |       ],
 185 |       "text/plain": [
 186 |        "   num_passengers sales_channel  trip_type  purchase_lead  length_of_stay  \\\n",
 187 |        "0               2      Internet  RoundTrip            262              19   \n",
 188 |        "1               1      Internet  RoundTrip            112              20   \n",
 189 |        "2               2      Internet  RoundTrip            243              22   \n",
 190 |        "3               1      Internet  RoundTrip             96              31   \n",
 191 |        "4               2      Internet  RoundTrip             68              22   \n",
 192 |        "\n",
 193 |        "   flight_hour flight_day   route booking_origin  wants_extra_baggage  \\\n",
 194 |        "0            7        Sat  AKLDEL    New Zealand                    1   \n",
 195 |        "1            3        Sat  AKLDEL    New Zealand                    0   \n",
 196 |        "2           17        Wed  AKLDEL          India                    1   \n",
 197 |        "3            4        Sat  AKLDEL    New Zealand                    0   \n",
 198 |        "4           15        Wed  AKLDEL          India                    1   \n",
 199 |        "\n",
 200 |        "   wants_preferred_seat  wants_in_flight_meals  flight_duration  \\\n",
 201 |        "0                     0                      0             5.52   \n",
 202 |        "1                     0                      0             5.52   \n",
 203 |        "2                     1                      0             5.52   \n",
 204 |        "3                     0                      1             5.52   \n",
 205 |        "4                     0                      1             5.52   \n",
 206 |        "\n",
 207 |        "   booking_complete  \n",
 208 |        "0                 0  \n",
 209 |        "1                 0  \n",
 210 |        "2                 0  \n",
 211 |        "3                 0  \n",
 212 |        "4                 0  "
 213 |       ]
 214 |      },
 215 |      "execution_count": 8,
 216 |      "metadata": {},
 217 |      "output_type": "execute_result"
 218 |     }
 219 |    ],
 220 |    "source": [
 221 |     "df.head()"
 222 |    ]
 223 |   },
 224 |   {
 225 |    "cell_type": "code",
 226 |    "execution_count": 10,
 227 |    "id": "127dc3df-583f-424a-892d-697c41410906",
 228 |    "metadata": {},
 229 |    "outputs": [
 230 |     {
 231 |      "data": {
 232 |       "text/plain": [
 233 |        "(50000, 14)"
 234 |       ]
 235 |      },
 236 |      "execution_count": 10,
 237 |      "metadata": {},
 238 |      "output_type": "execute_result"
 239 |     }
 240 |    ],
 241 |    "source": [
 242 |     "df.shape"
 243 |    ]
 244 |   },
 245 |   {
 246 |    "cell_type": "code",
 247 |    "execution_count": 11,
 248 |    "id": "c89996b9-621d-4235-88a6-53ed2822d697",
 249 |    "metadata": {},
 250 |    "outputs": [
 251 |     {
 252 |      "data": {
 253 |       "text/html": [
 254 |        "<div>\n",
 255 |        "<style scoped>\n",
 256 |        "    .dataframe tbody tr th:only-of-type {\n",
 257 |        "        vertical-align: middle;\n",
 258 |        "    }\n",
 259 |        "\n",
 260 |        "    .dataframe tbody tr th {\n",
 261 |        "        vertical-align: top;\n",
 262 |        "    }\n",
 263 |        "\n",
 264 |        "    .dataframe thead th {\n",
 265 |        "        text-align: right;\n",
 266 |        "    }\n",
 267 |        "</style>\n",
 268 |        "<table border=\"1\" class=\"dataframe\">\n",
 269 |        "  <thead>\n",
 270 |        "    <tr style=\"text-align: right;\">\n",
 271 |        "      <th></th>\n",
 272 |        "      <th>num_passengers</th>\n",
 273 |        "      <th>purchase_lead</th>\n",
 274 |        "      <th>length_of_stay</th>\n",
 275 |        "      <th>flight_hour</th>\n",
 276 |        "      <th>wants_extra_baggage</th>\n",
 277 |        "      <th>wants_preferred_seat</th>\n",
 278 |        "      <th>wants_in_flight_meals</th>\n",
 279 |        "      <th>flight_duration</th>\n",
 280 |        "      <th>booking_complete</th>\n",
 281 |        "    </tr>\n",
 282 |        "  </thead>\n",
 283 |        "  <tbody>\n",
 284 |        "    <tr>\n",
 285 |        "      <th>count</th>\n",
 286 |        "      <td>50000.000000</td>\n",
 287 |        "      <td>50000.000000</td>\n",
 288 |        "      <td>50000.00000</td>\n",
 289 |        "      <td>50000.00000</td>\n",
 290 |        "      <td>50000.000000</td>\n",
 291 |        "      <td>50000.000000</td>\n",
 292 |        "      <td>50000.000000</td>\n",
 293 |        "      <td>50000.000000</td>\n",
 294 |        "      <td>50000.000000</td>\n",
 295 |        "    </tr>\n",
 296 |        "    <tr>\n",
 297 |        "      <th>mean</th>\n",
 298 |        "      <td>1.591240</td>\n",
 299 |        "      <td>84.940480</td>\n",
 300 |        "      <td>23.04456</td>\n",
 301 |        "      <td>9.06634</td>\n",
 302 |        "      <td>0.668780</td>\n",
 303 |        "      <td>0.296960</td>\n",
 304 |        "      <td>0.427140</td>\n",
 305 |        "      <td>7.277561</td>\n",
 306 |        "      <td>0.149560</td>\n",
 307 |        "    </tr>\n",
 308 |        "    <tr>\n",
 309 |        "      <th>std</th>\n",
 310 |        "      <td>1.020165</td>\n",
 311 |        "      <td>90.451378</td>\n",
 312 |        "      <td>33.88767</td>\n",
 313 |        "      <td>5.41266</td>\n",
 314 |        "      <td>0.470657</td>\n",
 315 |        "      <td>0.456923</td>\n",
 316 |        "      <td>0.494668</td>\n",
 317 |        "      <td>1.496863</td>\n",
 318 |        "      <td>0.356643</td>\n",
 319 |        "    </tr>\n",
 320 |        "    <tr>\n",
 321 |        "      <th>min</th>\n",
 322 |        "      <td>1.000000</td>\n",
 323 |        "      <td>0.000000</td>\n",
 324 |        "      <td>0.00000</td>\n",
 325 |        "      <td>0.00000</td>\n",
 326 |        "      <td>0.000000</td>\n",
 327 |        "      <td>0.000000</td>\n",
 328 |        "      <td>0.000000</td>\n",
 329 |        "      <td>4.670000</td>\n",
 330 |        "      <td>0.000000</td>\n",
 331 |        "    </tr>\n",
 332 |        "    <tr>\n",
 333 |        "      <th>25%</th>\n",
 334 |        "      <td>1.000000</td>\n",
 335 |        "      <td>21.000000</td>\n",
 336 |        "      <td>5.00000</td>\n",
 337 |        "      <td>5.00000</td>\n",
 338 |        "      <td>0.000000</td>\n",
 339 |        "      <td>0.000000</td>\n",
 340 |        "      <td>0.000000</td>\n",
 341 |        "      <td>5.620000</td>\n",
 342 |        "      <td>0.000000</td>\n",
 343 |        "    </tr>\n",
 344 |        "    <tr>\n",
 345 |        "      <th>50%</th>\n",
 346 |        "      <td>1.000000</td>\n",
 347 |        "      <td>51.000000</td>\n",
 348 |        "      <td>17.00000</td>\n",
 349 |        "      <td>9.00000</td>\n",
 350 |        "      <td>1.000000</td>\n",
 351 |        "      <td>0.000000</td>\n",
 352 |        "      <td>0.000000</td>\n",
 353 |        "      <td>7.570000</td>\n",
 354 |        "      <td>0.000000</td>\n",
 355 |        "    </tr>\n",
 356 |        "    <tr>\n",
 357 |        "      <th>75%</th>\n",
 358 |        "      <td>2.000000</td>\n",
 359 |        "      <td>115.000000</td>\n",
 360 |        "      <td>28.00000</td>\n",
 361 |        "      <td>13.00000</td>\n",
 362 |        "      <td>1.000000</td>\n",
 363 |        "      <td>1.000000</td>\n",
 364 |        "      <td>1.000000</td>\n",
 365 |        "      <td>8.830000</td>\n",
 366 |        "      <td>0.000000</td>\n",
 367 |        "    </tr>\n",
 368 |        "    <tr>\n",
 369 |        "      <th>max</th>\n",
 370 |        "      <td>9.000000</td>\n",
 371 |        "      <td>867.000000</td>\n",
 372 |        "      <td>778.00000</td>\n",
 373 |        "      <td>23.00000</td>\n",
 374 |        "      <td>1.000000</td>\n",
 375 |        "      <td>1.000000</td>\n",
 376 |        "      <td>1.000000</td>\n",
 377 |        "      <td>9.500000</td>\n",
 378 |        "      <td>1.000000</td>\n",
 379 |        "    </tr>\n",
 380 |        "  </tbody>\n",
 381 |        "</table>\n",
 382 |        "</div>"
 383 |       ],
 384 |       "text/plain": [
 385 |        "       num_passengers  purchase_lead  length_of_stay  flight_hour  \\\n",
 386 |        "count    50000.000000   50000.000000     50000.00000  50000.00000   \n",
 387 |        "mean         1.591240      84.940480        23.04456      9.06634   \n",
 388 |        "std          1.020165      90.451378        33.88767      5.41266   \n",
 389 |        "min          1.000000       0.000000         0.00000      0.00000   \n",
 390 |        "25%          1.000000      21.000000         5.00000      5.00000   \n",
 391 |        "50%          1.000000      51.000000        17.00000      9.00000   \n",
 392 |        "75%          2.000000     115.000000        28.00000     13.00000   \n",
 393 |        "max          9.000000     867.000000       778.00000     23.00000   \n",
 394 |        "\n",
 395 |        "       wants_extra_baggage  wants_preferred_seat  wants_in_flight_meals  \\\n",
 396 |        "count         50000.000000          50000.000000           50000.000000   \n",
 397 |        "mean              0.668780              0.296960               0.427140   \n",
 398 |        "std               0.470657              0.456923               0.494668   \n",
 399 |        "min               0.000000              0.000000               0.000000   \n",
 400 |        "25%               0.000000              0.000000               0.000000   \n",
 401 |        "50%               1.000000              0.000000               0.000000   \n",
 402 |        "75%               1.000000              1.000000               1.000000   \n",
 403 |        "max               1.000000              1.000000               1.000000   \n",
 404 |        "\n",
 405 |        "       flight_duration  booking_complete  \n",
 406 |        "count     50000.000000      50000.000000  \n",
 407 |        "mean          7.277561          0.149560  \n",
 408 |        "std           1.496863          0.356643  \n",
 409 |        "min           4.670000          0.000000  \n",
 410 |        "25%           5.620000          0.000000  \n",
 411 |        "50%           7.570000          0.000000  \n",
 412 |        "75%           8.830000          0.000000  \n",
 413 |        "max           9.500000          1.000000  "
 414 |       ]
 415 |      },
 416 |      "execution_count": 11,
 417 |      "metadata": {},
 418 |      "output_type": "execute_result"
 419 |     }
 420 |    ],
 421 |    "source": [
 422 |     "df.describe()"
 423 |    ]
 424 |   },
 425 |   {
 426 |    "cell_type": "code",
 427 |    "execution_count": 12,
 428 |    "id": "5c9f1d4b-3a05-49b7-ace4-fc222e19cbcf",
 429 |    "metadata": {},
 430 |    "outputs": [
 431 |     {
 432 |      "name": "stdout",
 433 |      "output_type": "stream",
 434 |      "text": [
 435 |       "<class 'pandas.core.frame.DataFrame'>\n",
 436 |       "RangeIndex: 50000 entries, 0 to 49999\n",
 437 |       "Data columns (total 14 columns):\n",
 438 |       " #   Column                 Non-Null Count  Dtype  \n",
 439 |       "---  ------                 --------------  -----  \n",
 440 |       " 0   num_passengers         50000 non-null  int64  \n",
 441 |       " 1   sales_channel          50000 non-null  object \n",
 442 |       " 2   trip_type              50000 non-null  object \n",
 443 |       " 3   purchase_lead          50000 non-null  int64  \n",
 444 |       " 4   length_of_stay         50000 non-null  int64  \n",
 445 |       " 5   flight_hour            50000 non-null  int64  \n",
 446 |       " 6   flight_day             50000 non-null  object \n",
 447 |       " 7   route                  50000 non-null  object \n",
 448 |       " 8   booking_origin         50000 non-null  object \n",
 449 |       " 9   wants_extra_baggage    50000 non-null  int64  \n",
 450 |       " 10  wants_preferred_seat   50000 non-null  int64  \n",
 451 |       " 11  wants_in_flight_meals  50000 non-null  int64  \n",
 452 |       " 12  flight_duration        50000 non-null  float64\n",
 453 |       " 13  booking_complete       50000 non-null  int64  \n",
 454 |       "dtypes: float64(1), int64(8), object(5)\n",
 455 |       "memory usage: 5.3+ MB\n"
 456 |      ]
 457 |     }
 458 |    ],
 459 |    "source": [
 460 |     "df.info()"
 461 |    ]
 462 |   },
 463 |   {
 464 |    "cell_type": "markdown",
 465 |    "id": "edb28431-ab65-43c0-a49c-99fd0eb536bb",
 466 |    "metadata": {},
 467 |    "source": [
 468 |     "### Sales Channel"
 469 |    ]
 470 |   },
 471 |   {
 472 |    "cell_type": "code",
 473 |    "execution_count": 23,
 474 |    "id": "c003e099-f950-4817-82ef-4b73a9428b94",
 475 |    "metadata": {},
 476 |    "outputs": [],
 477 |    "source": [
 478 |     "per_internet = df.sales_channel.value_counts().values[0]  / df.sales_channel.count() *100\n",
 479 |     "per_mobile = df.sales_channel.value_counts().values[1]  / df.sales_channel.count() *100"
 480 |    ]
 481 |   },
 482 |   {
 483 |    "cell_type": "code",
 484 |    "execution_count": 25,
 485 |    "id": "bde01b1a-3142-4667-b389-2b9a6c52913f",
 486 |    "metadata": {},
 487 |    "outputs": [
 488 |     {
 489 |      "name": "stdout",
 490 |      "output_type": "stream",
 491 |      "text": [
 492 |       "Number of bookings done through internet: 88.764 %\n",
 493 |       "Number of bookings done through phone call: 11.236 %\n"
 494 |      ]
 495 |     }
 496 |    ],
 497 |    "source": [
 498 |     "print(f\"Number of bookings done through internet: {per_internet} %\")\n",
 499 |     "print(f\"Number of bookings done through phone call: {per_mobile} %\")"
 500 |    ]
 501 |   },
 502 |   {
 503 |    "cell_type": "markdown",
 504 |    "id": "9252a715-73db-4763-b969-85517922fd06",
 505 |    "metadata": {},
 506 |    "source": [
 507 |     "### Trip Type"
 508 |    ]
 509 |   },
 510 |   {
 511 |    "cell_type": "code",
 512 |    "execution_count": 28,
 513 |    "id": "7f04c4b2-e90f-408e-9163-09353a1d977b",
 514 |    "metadata": {},
 515 |    "outputs": [],
 516 |    "source": [
 517 |     "per_round = df.trip_type.value_counts().values[0]/ df.trip_type.count() *100\n",
 518 |     "per_oneway = df.trip_type.value_counts().values[1]/ df.trip_type.count() *100\n",
 519 |     "per_circle = df.trip_type.value_counts().values[2]/ df.trip_type.count() *100"
 520 |    ]
 521 |   },
 522 |   {
 523 |    "cell_type": "code",
 524 |    "execution_count": 29,
 525 |    "id": "27523ae3-0d2c-4320-b748-47f6e59fbffc",
 526 |    "metadata": {},
 527 |    "outputs": [
 528 |     {
 529 |      "name": "stdout",
 530 |      "output_type": "stream",
 531 |      "text": [
 532 |       "Percentage of round trips: 98.994 %\n",
 533 |       "Percentage of One way trips: 0.774 %\n",
 534 |       "Percentage of circle trips: 0.232 %\n"
 535 |      ]
 536 |     }
 537 |    ],
 538 |    "source": [
 539 |     "print(f\"Percentage of round trips: {per_round} %\")\n",
 540 |     "print(f\"Percentage of One way trips: {per_oneway} %\")\n",
 541 |     "print(f\"Percentage of circle trips: {per_circle} %\")\n"
 542 |    ]
 543 |   },
 544 |   {
 545 |    "cell_type": "markdown",
 546 |    "id": "03c17de4-2cdc-40c9-8bd0-9f5a9f39cba3",
 547 |    "metadata": {},
 548 |    "source": [
 549 |     "### Purchase Lead"
 550 |    ]
 551 |   },
 552 |   {
 553 |    "cell_type": "code",
 554 |    "execution_count": 54,
 555 |    "id": "5207bdf6-4389-47ae-82f7-91e6c02f13a6",
 556 |    "metadata": {},
 557 |    "outputs": [
 558 |     {
 559 |      "data": {
 560 |       "text/plain": [
 561 |        "<AxesSubplot:xlabel='purchase_lead', ylabel='Count'>"
 562 |       ]
 563 |      },
 564 |      "execution_count": 54,
 565 |      "metadata": {},
 566 |      "output_type": "execute_result"
 567 |     },
 568 |     {
 569 |      "data": {
 570 |       "image/png": 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\n",
 571 |       "text/plain": [
 572 |        "<Figure size 1080x360 with 1 Axes>"
 573 |       ]
 574 |      },
 575 |      "metadata": {
 576 |       "needs_background": "light"
 577 |      },
 578 |      "output_type": "display_data"
 579 |     }
 580 |    ],
 581 |    "source": [
 582 |     "plt.figure(figsize=(15,5))\n",
 583 |     "sns.histplot(data=df, x=\"purchase_lead\", binwidth=20,kde=True)"
 584 |    ]
 585 |   },
 586 |   {
 587 |    "cell_type": "markdown",
 588 |    "id": "67e9da17-7a80-4cee-9e66-f4f195222156",
 589 |    "metadata": {},
 590 |    "source": [
 591 |     "There are few bookings that were done more than 2 years before the travel date and it seems very unlikely that book that in advance. However, it might also be because of the cancellation and rebooking in a period of 6 months for twice. Generally airline keep the tickets for rebooking within a year. But at this point we will consider them as outliers which will effect the results of predictive model in a huge way. "
 592 |    ]
 593 |   },
 594 |   {
 595 |    "cell_type": "code",
 596 |    "execution_count": 68,
 597 |    "id": "51af85fc-b939-4f49-b1ef-43376018701c",
 598 |    "metadata": {},
 599 |    "outputs": [
 600 |     {
 601 |      "data": {
 602 |       "text/plain": [
 603 |        "False    49992\n",
 604 |        "True         8\n",
 605 |        "Name: purchase_lead, dtype: int64"
 606 |       ]
 607 |      },
 608 |      "execution_count": 68,
 609 |      "metadata": {},
 610 |      "output_type": "execute_result"
 611 |     }
 612 |    ],
 613 |    "source": [
 614 |     "(df.purchase_lead >600).value_counts()"
 615 |    ]
 616 |   },
 617 |   {
 618 |    "cell_type": "markdown",
 619 |    "id": "71a14c0c-fa30-4bc0-9f3e-214251579ac2",
 620 |    "metadata": {},
 621 |    "source": [
 622 |     "If we assume that no customer is booking in advance of more than 1 and half year we will remove all entries with purchase_lead more than 600 days."
 623 |    ]
 624 |   },
 625 |   {
 626 |    "cell_type": "code",
 627 |    "execution_count": 69,
 628 |    "id": "956345d4-716f-49a4-9f66-ca014256c9f5",
 629 |    "metadata": {},
 630 |    "outputs": [
 631 |     {
 632 |      "data": {
 633 |       "text/html": [
 634 |        "<div>\n",
 635 |        "<style scoped>\n",
 636 |        "    .dataframe tbody tr th:only-of-type {\n",
 637 |        "        vertical-align: middle;\n",
 638 |        "    }\n",
 639 |        "\n",
 640 |        "    .dataframe tbody tr th {\n",
 641 |        "        vertical-align: top;\n",
 642 |        "    }\n",
 643 |        "\n",
 644 |        "    .dataframe thead th {\n",
 645 |        "        text-align: right;\n",
 646 |        "    }\n",
 647 |        "</style>\n",
 648 |        "<table border=\"1\" class=\"dataframe\">\n",
 649 |        "  <thead>\n",
 650 |        "    <tr style=\"text-align: right;\">\n",
 651 |        "      <th></th>\n",
 652 |        "      <th>num_passengers</th>\n",
 653 |        "      <th>sales_channel</th>\n",
 654 |        "      <th>trip_type</th>\n",
 655 |        "      <th>purchase_lead</th>\n",
 656 |        "      <th>length_of_stay</th>\n",
 657 |        "      <th>flight_hour</th>\n",
 658 |        "      <th>flight_day</th>\n",
 659 |        "      <th>route</th>\n",
 660 |        "      <th>booking_origin</th>\n",
 661 |        "      <th>wants_extra_baggage</th>\n",
 662 |        "      <th>wants_preferred_seat</th>\n",
 663 |        "      <th>wants_in_flight_meals</th>\n",
 664 |        "      <th>flight_duration</th>\n",
 665 |        "      <th>booking_complete</th>\n",
 666 |        "    </tr>\n",
 667 |        "  </thead>\n",
 668 |        "  <tbody>\n",
 669 |        "    <tr>\n",
 670 |        "      <th>835</th>\n",
 671 |        "      <td>3</td>\n",
 672 |        "      <td>Internet</td>\n",
 673 |        "      <td>RoundTrip</td>\n",
 674 |        "      <td>641</td>\n",
 675 |        "      <td>46</td>\n",
 676 |        "      <td>6</td>\n",
 677 |        "      <td>Sun</td>\n",
 678 |        "      <td>AKLKUL</td>\n",
 679 |        "      <td>Malaysia</td>\n",
 680 |        "      <td>1</td>\n",
 681 |        "      <td>0</td>\n",
 682 |        "      <td>1</td>\n",
 683 |        "      <td>8.83</td>\n",
 684 |        "      <td>1</td>\n",
 685 |        "    </tr>\n",
 686 |        "    <tr>\n",
 687 |        "      <th>6148</th>\n",
 688 |        "      <td>1</td>\n",
 689 |        "      <td>Internet</td>\n",
 690 |        "      <td>RoundTrip</td>\n",
 691 |        "      <td>614</td>\n",
 692 |        "      <td>19</td>\n",
 693 |        "      <td>11</td>\n",
 694 |        "      <td>Wed</td>\n",
 695 |        "      <td>COKMEL</td>\n",
 696 |        "      <td>Australia</td>\n",
 697 |        "      <td>0</td>\n",
 698 |        "      <td>0</td>\n",
 699 |        "      <td>0</td>\n",
 700 |        "      <td>8.83</td>\n",
 701 |        "      <td>0</td>\n",
 702 |        "    </tr>\n",
 703 |        "    <tr>\n",
 704 |        "      <th>24119</th>\n",
 705 |        "      <td>1</td>\n",
 706 |        "      <td>Internet</td>\n",
 707 |        "      <td>RoundTrip</td>\n",
 708 |        "      <td>704</td>\n",
 709 |        "      <td>23</td>\n",
 710 |        "      <td>8</td>\n",
 711 |        "      <td>Tue</td>\n",
 712 |        "      <td>PNHSYD</td>\n",
 713 |        "      <td>Australia</td>\n",
 714 |        "      <td>0</td>\n",
 715 |        "      <td>0</td>\n",
 716 |        "      <td>0</td>\n",
 717 |        "      <td>8.58</td>\n",
 718 |        "      <td>0</td>\n",
 719 |        "    </tr>\n",
 720 |        "    <tr>\n",
 721 |        "      <th>38356</th>\n",
 722 |        "      <td>2</td>\n",
 723 |        "      <td>Internet</td>\n",
 724 |        "      <td>RoundTrip</td>\n",
 725 |        "      <td>633</td>\n",
 726 |        "      <td>5</td>\n",
 727 |        "      <td>10</td>\n",
 728 |        "      <td>Sat</td>\n",
 729 |        "      <td>HKTOOL</td>\n",
 730 |        "      <td>Australia</td>\n",
 731 |        "      <td>0</td>\n",
 732 |        "      <td>0</td>\n",
 733 |        "      <td>1</td>\n",
 734 |        "      <td>8.83</td>\n",
 735 |        "      <td>0</td>\n",
 736 |        "    </tr>\n",
 737 |        "    <tr>\n",
 738 |        "      <th>39417</th>\n",
 739 |        "      <td>1</td>\n",
 740 |        "      <td>Mobile</td>\n",
 741 |        "      <td>RoundTrip</td>\n",
 742 |        "      <td>625</td>\n",
 743 |        "      <td>5</td>\n",
 744 |        "      <td>15</td>\n",
 745 |        "      <td>Fri</td>\n",
 746 |        "      <td>ICNRGN</td>\n",
 747 |        "      <td>Myanmar (Burma)</td>\n",
 748 |        "      <td>0</td>\n",
 749 |        "      <td>0</td>\n",
 750 |        "      <td>0</td>\n",
 751 |        "      <td>6.62</td>\n",
 752 |        "      <td>0</td>\n",
 753 |        "    </tr>\n",
 754 |        "    <tr>\n",
 755 |        "      <th>42916</th>\n",
 756 |        "      <td>1</td>\n",
 757 |        "      <td>Mobile</td>\n",
 758 |        "      <td>RoundTrip</td>\n",
 759 |        "      <td>605</td>\n",
 760 |        "      <td>6</td>\n",
 761 |        "      <td>18</td>\n",
 762 |        "      <td>Thu</td>\n",
 763 |        "      <td>BLRMEL</td>\n",
 764 |        "      <td>India</td>\n",
 765 |        "      <td>0</td>\n",
 766 |        "      <td>0</td>\n",
 767 |        "      <td>0</td>\n",
 768 |        "      <td>8.83</td>\n",
 769 |        "      <td>0</td>\n",
 770 |        "    </tr>\n",
 771 |        "    <tr>\n",
 772 |        "      <th>46716</th>\n",
 773 |        "      <td>2</td>\n",
 774 |        "      <td>Internet</td>\n",
 775 |        "      <td>RoundTrip</td>\n",
 776 |        "      <td>606</td>\n",
 777 |        "      <td>6</td>\n",
 778 |        "      <td>6</td>\n",
 779 |        "      <td>Fri</td>\n",
 780 |        "      <td>HKTTPE</td>\n",
 781 |        "      <td>United States</td>\n",
 782 |        "      <td>0</td>\n",
 783 |        "      <td>0</td>\n",
 784 |        "      <td>1</td>\n",
 785 |        "      <td>4.67</td>\n",
 786 |        "      <td>0</td>\n",
 787 |        "    </tr>\n",
 788 |        "    <tr>\n",
 789 |        "      <th>48259</th>\n",
 790 |        "      <td>3</td>\n",
 791 |        "      <td>Internet</td>\n",
 792 |        "      <td>RoundTrip</td>\n",
 793 |        "      <td>867</td>\n",
 794 |        "      <td>6</td>\n",
 795 |        "      <td>7</td>\n",
 796 |        "      <td>Mon</td>\n",
 797 |        "      <td>KIXMLE</td>\n",
 798 |        "      <td>Japan</td>\n",
 799 |        "      <td>0</td>\n",
 800 |        "      <td>0</td>\n",
 801 |        "      <td>1</td>\n",
 802 |        "      <td>7.00</td>\n",
 803 |        "      <td>1</td>\n",
 804 |        "    </tr>\n",
 805 |        "  </tbody>\n",
 806 |        "</table>\n",
 807 |        "</div>"
 808 |       ],
 809 |       "text/plain": [
 810 |        "       num_passengers sales_channel  trip_type  purchase_lead  length_of_stay  \\\n",
 811 |        "835                 3      Internet  RoundTrip            641              46   \n",
 812 |        "6148                1      Internet  RoundTrip            614              19   \n",
 813 |        "24119               1      Internet  RoundTrip            704              23   \n",
 814 |        "38356               2      Internet  RoundTrip            633               5   \n",
 815 |        "39417               1        Mobile  RoundTrip            625               5   \n",
 816 |        "42916               1        Mobile  RoundTrip            605               6   \n",
 817 |        "46716               2      Internet  RoundTrip            606               6   \n",
 818 |        "48259               3      Internet  RoundTrip            867               6   \n",
 819 |        "\n",
 820 |        "       flight_hour flight_day   route   booking_origin  wants_extra_baggage  \\\n",
 821 |        "835              6        Sun  AKLKUL         Malaysia                    1   \n",
 822 |        "6148            11        Wed  COKMEL        Australia                    0   \n",
 823 |        "24119            8        Tue  PNHSYD        Australia                    0   \n",
 824 |        "38356           10        Sat  HKTOOL        Australia                    0   \n",
 825 |        "39417           15        Fri  ICNRGN  Myanmar (Burma)                    0   \n",
 826 |        "42916           18        Thu  BLRMEL            India                    0   \n",
 827 |        "46716            6        Fri  HKTTPE    United States                    0   \n",
 828 |        "48259            7        Mon  KIXMLE            Japan                    0   \n",
 829 |        "\n",
 830 |        "       wants_preferred_seat  wants_in_flight_meals  flight_duration  \\\n",
 831 |        "835                       0                      1             8.83   \n",
 832 |        "6148                      0                      0             8.83   \n",
 833 |        "24119                     0                      0             8.58   \n",
 834 |        "38356                     0                      1             8.83   \n",
 835 |        "39417                     0                      0             6.62   \n",
 836 |        "42916                     0                      0             8.83   \n",
 837 |        "46716                     0                      1             4.67   \n",
 838 |        "48259                     0                      1             7.00   \n",
 839 |        "\n",
 840 |        "       booking_complete  \n",
 841 |        "835                   1  \n",
 842 |        "6148                  0  \n",
 843 |        "24119                 0  \n",
 844 |        "38356                 0  \n",
 845 |        "39417                 0  \n",
 846 |        "42916                 0  \n",
 847 |        "46716                 0  \n",
 848 |        "48259                 1  "
 849 |       ]
 850 |      },
 851 |      "execution_count": 69,
 852 |      "metadata": {},
 853 |      "output_type": "execute_result"
 854 |     }
 855 |    ],
 856 |    "source": [
 857 |     "df[df.purchase_lead > 600]"
 858 |    ]
 859 |   },
 860 |   {
 861 |    "cell_type": "code",
 862 |    "execution_count": 71,
 863 |    "id": "12453b86-8ee1-4e41-9814-9938fd4abcbd",
 864 |    "metadata": {},
 865 |    "outputs": [],
 866 |    "source": [
 867 |     "#filtering the data to have only purchase lead days less than 600 days\n",
 868 |     "df = df[df.purchase_lead <600 ]"
 869 |    ]
 870 |   },
 871 |   {
 872 |    "cell_type": "markdown",
 873 |    "id": "5029fd70-7dc9-4748-85fb-f9f3541280a3",
 874 |    "metadata": {},
 875 |    "source": [
 876 |     "### Length Of Stay"
 877 |    ]
 878 |   },
 879 |   {
 880 |    "cell_type": "code",
 881 |    "execution_count": 74,
 882 |    "id": "53e701c9-5153-45b0-9e5c-bfdd887a30a5",
 883 |    "metadata": {},
 884 |    "outputs": [
 885 |     {
 886 |      "data": {
 887 |       "text/plain": [
 888 |        "<AxesSubplot:xlabel='length_of_stay', ylabel='Count'>"
 889 |       ]
 890 |      },
 891 |      "execution_count": 74,
 892 |      "metadata": {},
 893 |      "output_type": "execute_result"
 894 |     },
 895 |     {
 896 |      "data": {
 897 |       "image/png": 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\n",
 898 |       "text/plain": [
 899 |        "<Figure size 1080x360 with 1 Axes>"
 900 |       ]
 901 |      },
 902 |      "metadata": {
 903 |       "needs_background": "light"
 904 |      },
 905 |      "output_type": "display_data"
 906 |     }
 907 |    ],
 908 |    "source": [
 909 |     "plt.figure(figsize=(15,5))\n",
 910 |     "sns.histplot(data=df, x=\"length_of_stay\", binwidth=15,kde=True)"
 911 |    ]
 912 |   },
 913 |   {
 914 |    "cell_type": "markdown",
 915 |    "id": "1d4cdb69-f8e7-4254-a3e8-6d6dcf6dcd51",
 916 |    "metadata": {},
 917 |    "source": [
 918 |     "Let's see how many entries do we have that exceeds length of stay more than 100 days."
 919 |    ]
 920 |   },
 921 |   {
 922 |    "cell_type": "code",
 923 |    "execution_count": 76,
 924 |    "id": "f8f4c77f-e2c9-4ca3-ac9f-95563158db01",
 925 |    "metadata": {},
 926 |    "outputs": [
 927 |     {
 928 |      "data": {
 929 |       "text/plain": [
 930 |        "False    49713\n",
 931 |        "True       279\n",
 932 |        "Name: length_of_stay, dtype: int64"
 933 |       ]
 934 |      },
 935 |      "execution_count": 76,
 936 |      "metadata": {},
 937 |      "output_type": "execute_result"
 938 |     }
 939 |    ],
 940 |    "source": [
 941 |     "(df.length_of_stay> 200).value_counts()"
 942 |    ]
 943 |   },
 944 |   {
 945 |    "cell_type": "code",
 946 |    "execution_count": 90,
 947 |    "id": "865f0f4a-d6a7-40f5-99e4-f693eb0c1cfc",
 948 |    "metadata": {},
 949 |    "outputs": [
 950 |     {
 951 |      "data": {
 952 |       "text/plain": [
 953 |        "0    9\n",
 954 |        "1    1\n",
 955 |        "Name: booking_complete, dtype: int64"
 956 |       ]
 957 |      },
 958 |      "execution_count": 90,
 959 |      "metadata": {},
 960 |      "output_type": "execute_result"
 961 |     }
 962 |    ],
 963 |    "source": [
 964 |     "df[df.length_of_stay> 500].booking_complete.value_counts()"
 965 |    ]
 966 |   },
 967 |   {
 968 |    "cell_type": "markdown",
 969 |    "id": "9ad14f0a-8b73-4f49-879f-eb3bb45ae05d",
 970 |    "metadata": {},
 971 |    "source": [
 972 |     "We need to have more business knowledge to decide whether to remove these entries with more than 600 days of stay. There are could be many reasons for such bookings. But for now, we will just want to focus on bookings done for length of stay less than 500 days. "
 973 |    ]
 974 |   },
 975 |   {
 976 |    "cell_type": "code",
 977 |    "execution_count": 91,
 978 |    "id": "36c921c0-0efb-4054-9b4f-64331e142a3c",
 979 |    "metadata": {},
 980 |    "outputs": [],
 981 |    "source": [
 982 |     "#filtering the data to have only length of stay days less than 500 days\n",
 983 |     "df = df[df.purchase_lead <500 ]"
 984 |    ]
 985 |   },
 986 |   {
 987 |    "cell_type": "markdown",
 988 |    "id": "1f7b9aee-dcce-4616-8b39-5ffb0da18ed6",
 989 |    "metadata": {},
 990 |    "source": [
 991 |     "### Flight Day\n",
 992 |     "\n",
 993 |     "We will map the flight day with a number of a week. "
 994 |    ]
 995 |   },
 996 |   {
 997 |    "cell_type": "code",
 998 |    "execution_count": 93,
 999 |    "id": "24b9f199-1c20-403c-a8ba-2a126e05b2e6",
1000 |    "metadata": {},
1001 |    "outputs": [],
1002 |    "source": [
1003 |     "mapping = {\n",
1004 |     "    \"Mon\" : 1,\n",
1005 |     "    \"Tue\" : 2,\n",
1006 |     "    \"Wed\" : 3,\n",
1007 |     "    \"Thu\" : 4,\n",
1008 |     "    \"Fri\" : 5,\n",
1009 |     "    \"Sat\" : 6,\n",
1010 |     "    \"Sun\" : 7\n",
1011 |     "}\n",
1012 |     "\n",
1013 |     "df.flight_day = df.flight_day.map(mapping)"
1014 |    ]
1015 |   },
1016 |   {
1017 |    "cell_type": "code",
1018 |    "execution_count": 98,
1019 |    "id": "ba56606a-4c3a-4f23-b45b-943bc376f7aa",
1020 |    "metadata": {},
1021 |    "outputs": [
1022 |     {
1023 |      "data": {
1024 |       "text/plain": [
1025 |        "1    8100\n",
1026 |        "3    7671\n",
1027 |        "2    7670\n",
1028 |        "4    7423\n",
1029 |        "5    6759\n",
1030 |        "7    6550\n",
1031 |        "6    5809\n",
1032 |        "Name: flight_day, dtype: int64"
1033 |       ]
1034 |      },
1035 |      "execution_count": 98,
1036 |      "metadata": {},
1037 |      "output_type": "execute_result"
1038 |     }
1039 |    ],
1040 |    "source": [
1041 |     "df.flight_day.value_counts()"
1042 |    ]
1043 |   },
1044 |   {
1045 |    "cell_type": "markdown",
1046 |    "id": "cb094eea-5531-4f35-8226-fbb953bc8846",
1047 |    "metadata": {},
1048 |    "source": [
1049 |     "Most of the customers want to travel on Monday and choose Saturday as least preffered day as flight day.  "
1050 |    ]
1051 |   },
1052 |   {
1053 |    "cell_type": "markdown",
1054 |    "id": "07344b29-7a55-4093-b892-b240e46c3f32",
1055 |    "metadata": {},
1056 |    "source": [
1057 |     "### Booking Origin"
1058 |    ]
1059 |   },
1060 |   {
1061 |    "cell_type": "code",
1062 |    "execution_count": 122,
1063 |    "id": "b1be60f0-9c41-47d5-ae29-6cad5c4ba916",
1064 |    "metadata": {},
1065 |    "outputs": [
1066 |     {
1067 |      "data": {
1068 |       "text/plain": [
1069 |        "Text(0, 0.5, 'Number of bookings')"
1070 |       ]
1071 |      },
1072 |      "execution_count": 122,
1073 |      "metadata": {},
1074 |      "output_type": "execute_result"
1075 |     },
1076 |     {
1077 |      "data": {
1078 |       "image/png": 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\n",
1079 |       "text/plain": [
1080 |        "<Figure size 1080x360 with 1 Axes>"
1081 |       ]
1082 |      },
1083 |      "metadata": {
1084 |       "needs_background": "light"
1085 |      },
1086 |      "output_type": "display_data"
1087 |     }
1088 |    ],
1089 |    "source": [
1090 |     "plt.figure(figsize=(15,5))\n",
1091 |     "ax = df.booking_origin.value_counts()[:20].plot(kind=\"bar\")\n",
1092 |     "ax.set_xlabel(\"Countries\")\n",
1093 |     "ax.set_ylabel(\"Number of bookings\")"
1094 |    ]
1095 |   },
1096 |   {
1097 |    "cell_type": "markdown",
1098 |    "id": "6816a14d-ec09-4372-9213-da04c5e28b77",
1099 |    "metadata": {},
1100 |    "source": [
1101 |     "Above chart shows travellers from which country had maximum booking applications. "
1102 |    ]
1103 |   },
1104 |   {
1105 |    "cell_type": "code",
1106 |    "execution_count": 123,
1107 |    "id": "03541101-79a5-4cd6-89b8-75786acab617",
1108 |    "metadata": {},
1109 |    "outputs": [
1110 |     {
1111 |      "data": {
1112 |       "text/plain": [
1113 |        "Text(0, 0.5, 'Number of complete bookings')"
1114 |       ]
1115 |      },
1116 |      "execution_count": 123,
1117 |      "metadata": {},
1118 |      "output_type": "execute_result"
1119 |     },
1120 |     {
1121 |      "data": {
1122 |       "image/png": 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RSa6Z5HVJTuh+DkhyzdUITpIkSZK0sibpE3gwcCGtafzDgQuAd08zKEmSJEnSdEyyJ3DHqnro2O2XJTl5SvFIkiRJkqZokpnA3yW50+hGkt2B300vJEmSJEnStEwyE/j3wCFj+wDPB54wtYgkSZIkSVMzSXXQk4FbJtm8u33BtIOSJEmSJE3HJNVB/y3JFlV1QVVdkORaSV65GsFJkiRJklbWJHsC71NVvxrdqKrzgftOLSJJkiRJ0tRMkgRukuQqoxtJrgpcZZnHS5IkSZIGapLCMIcBxyQZ9QbcGzhkeiFJkiRJkqZlksIw/57kG8A9uqFXVNWnpxuWJEmSJGkaJpkJpKqOBI6cciySJEmSpCmbZE+gJEmSJGkjYRIoSZIkSXNkySQwyTHd5b+vXjiSJEmSpGlabk/g9ZLcEXhAkg8AGb+zqk6aamSSJEmSpBW3XBL4L8BLgBsAr1twXwF7TCsoSZIkSdJ0LJkEVtX/AP+T5CVV9YpVjEmSJEmSNCXrLQxTVa9I8oAkr+1+7jfJCyc5OMnPkpw2NrZlkqOTfKe7vFY3niRvSHJmklOS7DL2nMd3j/9Oksdfnj+kJEmSJKlZbxKY5FXAvsA3u599k/zbBK/9HuDeC8b2A46pqp2AY7rbAPcBdup+9gHe2r33lsD+wO2B2wH7jxJHSZIkSdKGm6RFxJ7APavq4Ko6mJbYrXc2sKqOA365YPiBwCHd9UOAB42NH1rNV4AtklwPuBdwdFX9sqrOB45m3cRSkiRJkjShSfsEbjF2/Zp/xvtdp6p+3F3/CXCd7vo2wNljjzunG1tqXJIkSZJ0OSxXHXTkVcDXkxxLaxNxZ9Ys47zcqqqS1J/7OiNJ9qEtJWW77bZbqZeVJEmSpI3KJIVh3g/sBnwE+DBwh6r678v5fj/tlnnSXf6sGz8X2HbscTfoxpYaXyzOg6pq16radeutt76c4UmSJEnSxm2i5aBV9eOqOrz7+cmf8X6HA6MKn48HPjY2/riuSuhuwK+7ZaOfBv42ybW6gjB/241JkiRJki6HSZaDXi5J3g/cFdgqyTm0Kp+vBj6Y5EnAD4CHdw8/ArgvcCZwEbA3QFX9MskrgOO7x728qhYWm5EkSZIkTWhqSWBVPWqJu+6+yGMLeMYSr3MwcPAKhiZJkiRJc2vZ5aBJNkny7dUKRpIkSZI0XcsmgVV1KXBGEsttSpIkSdJGYJLloNcCTk/yNeC3o8GqesDUopIkSZIkTcUkSeBLph6FJEmSJGlVrDcJrKrPJ9ke2KmqPpPkasAm0w9NkiRJkrTS1psEJnkKsA+wJbAjsA3wNhap8qnp2WG/T674a37/1Xuu+GtKkiRJGrZJmsU/A9gduACgqr4DXHuaQUmSJEmSpmOSJPDiqvrD6EaSKwI1vZAkSZIkSdMySRL4+SQvAq6a5J7Ah4CPTzcsSZIkSdI0TJIE7gecB5wKPBU4AvjnaQYlSZIkSZqOSaqD/inJIcBXactAz6gql4NKkiRJ0gyapDronrRqoN8FAtwwyVOr6lPTDk6SJEmStLImaRZ/AHC3qjoTIMmOwCcBk0BJkiRJmjGT7Am8cJQAds4CLpxSPJIkSZKkKVpyJjDJQ7qrJyQ5AvggbU/gw4DjVyE2SZIkSdIKW2456P3Hrv8UuEt3/TzgqlOLSJIkSZI0NUsmgVW192oGIkmSJEmavkmqg94QeBaww/jjq+oB0wtLkiRJkjQNk1QH/V/gXcDHgT9NNRpJkiRJ0lRNkgT+vqreMPVIJEmSJElTN0kSeGCS/YGjgItHg1V10tSikiRJkiRNxSRJ4M2BxwJ7sGY5aHW3JUmSJEkzZJIk8GHAjarqD9MORpIkSZI0XVeY4DGnAVtMOQ5JkiRJ0iqYZCZwC+DbSY5n7T2BtoiQJEmSpBkzSRK4/9SjkCRJkiStivUmgVX1+dUIRJIkSZI0fetNApNcSKsGCnBl4ErAb6tq82kGptm0w36fXPHX/P6r91zx15QkSZLm1SQzgdcYXU8S4IHAbtMMSpIkSZI0HZNUB71MNf8L3Gs64UiSJEmSpmmS5aAPGbt5BWBX4PdTi0iSJEmSNDWTVAe9/9j1S4Dv05aESpIkSZJmzCR7AvdejUAkSZIkSdO3ZBKY5F+WeV5V1SumEI8kSZIkaYqWmwn87SJjVweeBPwFYBIoSZIkSTNmySSwqg4YXU9yDWBfYG/gA8ABSz1PGjp7GUqSJGmeLbsnMMmWwPOAvYBDgF2q6vzVCEySJEmStPKW2xP4H8BDgIOAm1fVb1YtKkmSJEnSVCzXLP75wPWBfwZ+lOSC7ufCJBesTniSJEmSpJW03J7A5RJESZIkSdIMMtGTJEmSpDliEihJkiRJc8QkUJIkSZLmiEmgJEmSJM2RXpLAJN9PcmqSk5Oc0I1tmeToJN/pLq/VjSfJG5KcmeSUJLv0EbMkSZIkbQz6nAm8W1Xdqqp27W7vBxxTVTsBx3S3Ae4D7NT97AO8ddUjlSRJkqSNxJCWgz4QOKS7fgjwoLHxQ6v5CrBFkuv1EJ8kSZIkzby+ksACjkpyYpJ9urHrVNWPu+s/Aa7TXd8GOHvsued0Y2tJsk+SE5KccN55500rbkmSJEmaaUs2i5+yO1XVuUmuDRyd5Nvjd1ZVJakNecGqOgg4CGDXXXfdoOdKkiRJ0rzoJQmsqnO7y58l+ShwO+CnSa5XVT/ulnv+rHv4ucC2Y0+/QTcmbdR22O+TK/6a33/1niv+mpIkSZotq54EJrk6cIWqurC7/rfAy4HDgccDr+4uP9Y95XDgmUk+ANwe+PXYslFJPTNZlSRJmi19zAReB/hoktH7/1dVHZnkeOCDSZ4E/AB4ePf4I4D7AmcCFwF7r37IkmadyaokSVKz6klgVZ0F3HKR8V8Ad19kvIBnrEJokiRJkrTRG1KLCEmSJEnSlJkESpIkSdIcMQmUJEmSpDliEihJkiRJc8QkUJIkSZLmiEmgJEmSJM0Rk0BJkiRJmiMmgZIkSZI0R0wCJUmSJGmOmARKkiRJ0hwxCZQkSZKkOWISKEmSJElzxCRQkiRJkuaISaAkSZIkzRGTQEmSJEmaI1fsOwBJ0ho77PfJFX/N7796zxV/TUmSNLucCZQkSZKkOWISKEmSJElzxCRQkiRJkuaISaAkSZIkzRGTQEmSJEmaIyaBkiRJkjRHTAIlSZIkaY7YJ1CStMHsZyhJ0uxyJlCSJEmS5ohJoCRJkiTNEZNASZIkSZojJoGSJEmSNEdMAiVJkiRpjpgESpIkSdIcMQmUJEmSpDliEihJkiRJc8QkUJIkSZLmyBX7DkCSpGnZYb9Prujrff/Ve67o60mS1AdnAiVJkiRpjjgTKElSj1Z6thKcsZQkLc+ZQEmSJEmaI84ESpKk9XLGUpI2HiaBkiRpo2GyKknrZxIoSZK0ymYlWZ2VOCVtGPcESpIkSdIccSZQkiRJM80ZS2nDmARKkiRJq2Clk1UTVV1eM5MEJrk3cCCwCfDOqnp1zyFJkiRJGxVnVefDTCSBSTYB3gzcEzgHOD7J4VX1zX4jkyRJkrTaTFb/PDORBAK3A86sqrMAknwAeCBgEihJkiRpkIaarM5KddBtgLPHbp/TjUmSJEmSNkCqqu8Y1ivJ3wH3rqond7cfC9y+qp459ph9gH26mzcBzljhMLYCfr7CrzkNxrmyjHNlzUKcsxAjGOdKM86VZZwrZxZiBONcaca5suY1zu2rauvF7piV5aDnAtuO3b5BN3aZqjoIOGhaASQ5oap2ndbrrxTjXFnGubJmIc5ZiBGMc6UZ58oyzpUzCzGCca4041xZxrmuWVkOejywU5IbJrky8Ejg8J5jkiRJkqSZMxMzgVV1SZJnAp+mtYg4uKpO7zksSZIkSZo5M5EEAlTVEcARPYYwtaWmK8w4V5ZxrqxZiHMWYgTjXGnGubKMc+XMQoxgnCvNOFeWcS4wE4VhJEmSJEkrY1b2BEqSJEmSVoBJoCRJkiTNkZnZEyhpdiTZZbn7q+qk1YplEkmuBewEbDoaq6rj+ototiXZBLgOY98xVfXD/iKS1pXkCsBmVXVB37HMoiU+538N/KCqLlnteKRx3ffQnsAOrP1d9Lq+YlpMkl2BvwGuD/wOOA04uqrOn/p7uydwtiXZFHgS8NesfQD7xN6C2ggk2ZN1/05f3l9EayTZHXgpsD3tgy1AVdWN+oxrXJJju6ubArsC36DFeQvghKq6Q1+xLZTkycC+tP6jJwO7Af9XVXv0GddiklwNeD6wXVU9JclOwE2q6hM9h3aZJM8C9gd+CvypG66qukV/UUlNkv8CngZcSms/tTlwYFX9R6+BLdB9zp9cVb9N8hhgF1qcP+g5tMsk+QotrlNon+83A04Hrgn8fVUd1WN4JDkVWPIg18+kyy/JzYCdWfsY6dD+IlpXkiOA3wOnsua7iKp6WW9BjUmyN/As4HvAicDPaH+fNwZ2pyWDL5nmCVRnApeRZDfgjcBfAVemtaf4bVVt3mtga3sv8G3gXsDLgb2Ab/Ua0RKSbA38E+t+cAzqYDvJ24CrAXcD3gn8HfC1XoNa27uA59I+NC7tOZZFVdXdAJJ8BNilqk7tbt+MlsAOyb7AbYGvVNXdktwU+LeeY1rKu2n/7qMk+lzgQ8BgkkDa3+dNquoXfQeyPrPwmdSdJX4x6570GdwBbJLvschB95BOUAE7V9UFSfYCPgXsR/udGlQSCLwVuGWSW9JO/LwTOBS4S69Rre1HwJNGLbuS7Ew7DvlH4CNAr0kgcL/u8hnd5Xu7y716iGVZST5YVQ9fJHEd3O97kv2Bu9I+N48A7gN8kfb/c0huMKS/t0VcDdi9qn632J1JbkVboWQS2JM30RrTf4g2m/E4WoY+JH9ZVQ9L8sCqOqQ7y/mFvoNawmHAf9Om558GPB44r9eIFnfHqrpFklOq6mVJDqAdLAzFr6tqSPEs5yajBBCgqk5L8ld9BrSI31fV75OQ5CpV9e0kN+k7qCXsWFWPSPIogKq6KEn6DmqBs2lLwmbBLHwmHQa8gAVnswdq17HrmwIPA7bsKZalXCnJlYAHAW+qqj8mGeKSqEuqqpI8kBbnu5I8qe+gFrjxeM/mqvpmkptW1VlD+FgazZomuWdV3Xrsrv2SnEQ7ATAU+3aX91v2UcPwd8Atga9X1d5JrgO8r+eYFvOpJH/b94z0UqrqzdBm/avqS+P3LTY2DSaB61FVZybZpKouBd6d5OvAC/uOa8wfu8tfdbMsPwGu3WM8y/mL7ots36r6PPD5JMf3HdQiRmdlLkpyfeAXwPV6jGehY5P8B+1M68WjwaHts+uckuSdrPmC2Iu2dGhIzkmyBfC/wNFJzgcGs+RqgT8kuSrdmeIkOzL2f2AgzgI+l+STrP3/c1D7MDqz8Jl0XlUd3ncQk1hk9vf1SU4E/qWPeJbwduD7tCXqxyXZHhjinsALk7wQeCzwN93+xSv1HNNCpyd5K/CB7vYjgG8muQprjk2GIOMH1UnuyMAKI1bVj7vLH3T/J3eqqs90n/dDO1b/XVX9KcklSTanLWPctu+gFvEV4KPd784fWTOrOqTVfNBWHC7cX7vY2Iob2n+sobkoyZWBk5O8BvgxA/vgAA7qilq8BDgc2IxhfeGOG30p/Ljbc/cjhneWGOATXVLwH8BJtAPud/Ya0dpu312On3UvYDBL2MbsDfw9a85yHkdb5jQYVfXg7upLu72M1wSO7DGk5exPi23bJIfR9g08odeI1vXD7ufK3c+QzcJn0v7diZRjWDup/kh/IS1uQaGQK9A+owZ1nFFVbwDeMDb0gyR36yueZTwCeDTwxKr6SZLtGN6S1ScATwee093+EvAPtN+rIf2dPgk4OMk1aYnA+cAg6yYkeQqwD+1zaEfaXvW3AXfvM64FTuiOkd5BW0r9G+D/eo1oca+jbZ04tQZYACXJHYA7Alsned7YXZvTtp9NP4YB/r0MRnc25me0s2/PpR0cvqWqzuw1sBmV5H60parb0s5ybA68bMhnubszmptW1awsb9PlkOROtDOv7+72iW1WVd/rO65x3dnMv6MlA7vRDma+UlU/7zWwGTYLn0lJ3gfclFZwY7zQzuAOYscKQgFcQptxe21VndFPROvqlq79G3D9qrpPt4/tDlX1rp5DW8eCGaGrAZtU1YV9xzWruiSQIX+fJzkZuB3w1dES1iSnVtXNew1sCUl2ADavqqGt8CHJccBdq2qQy+iT3IW2t/JptER/5ELg41X1nanHYBI4m5I8pqret+DswWUGuvRq0JLsUVWfTfKQxe4f0pn3DLh66bisW8kUGFahiG6T+660/Ys37pYAf6iqdu85tHUkOaGqdl3/I/vTJdH/yLr/P4c4Uz14Sc6oqqHuUZ05ST5FK7D04qq6ZZIr0vY2Deoge3xGqKp2TKsE/LaqGsyM0Cx8vsNlJ3MfyrqtAob4nfnVqrp9kq9X1a27/58nDaHASbff89tZogXU0LakJHkPcCNaTYfBbk1Isn23DPhqVXXRar73oJZpDMUyVZqAwZQVvnp3eY1eo9gA3cHhU1j3g3goZ7TvAnwWuP8i9xVtD17vMvzqpeMGX8kUeDBwa9rSX6rqR0mG+nv1mST/QCtm8tvRYFX9sr+Q1jEqtnI/BlpsJck/VtVrkryRxT/jn91DWEv5cpKdq+qbfQcyiRk4QbVVVX2w229HVV2SZIifTc+gmxECqKrvJBnafv9Z+HwH+BitWNWJDG8P9UKfT/Ii4KpJ7klbbvvxnmMaeT7tGO6ARe4b4paU73U/Q9+acP3u5NRmwHZpFYGfWlVPn/YbmwQubvBVmqrq7d3lIPqdTOhjtKVXn2GAXxhVtX93uXffsazH0KuXjpuFSqZ/6KrwjYqtXH19T+jRI7rLZ4yNFe1s51DMQrGVURudE3qNYjK70falf492ADu4kvEjM3KC6rdJ/oI1xZV2Y5jVbC+uqj+kq7LZzQgNbenWLHy+Q2sVcO++g5jQfrQ9jKcCT6W1YBhETYKqekp3OaT9nksaHR8n2ay7/Zt+I1rS62lt3g4HqKpvJLnzaryxSeAiRlWagJ+zpgrSjWn7MgbxgZfkDcvdP7Az2SNXq6p/6juI9UmyL2250IW0jc+7APsNqMzw0KuXjpuFSqYfTPJ2YItuCdYTaf/ug1NVN+w7hgkMvthKVX28uzyk71gmMCsHrzAbJ6ieRzvY2jHJl4Ctaa0shmbIM0Ijs/D5Dm02/eY11q5oqLr9a+9ggN9BS22VGRnSlhm4rC/xe+m+f5L8HHhcjbU1GYqqOjtrt1VZlYkSk8DlHUcrzXwtWtPT42ln4ofQaPTEvgO4HD6R5L5VdUTfgazHE6vqwCT3Av6CVqL7vfTf+HZkseqlg/vC6Ay6kmnap+5/007wXADcBPiXqjq618CWkNbf7O+B0VnCzwFvr6ohlWN/ZVeA4fmsKbby3H5DWluSj7PMrEpVPWAVw1lWrel1dm3GllgO1CycoDqdtvT/JrRZ1TMYXtVvGPCM0JhBf76PuRPwhBmZTV+4z3IU6xBWe4y2ylybVtXys93tuwFfZiBbZsYcBDyvqo4FSHJX2rHSHXuMaTFnp7Utqe47fl/WrFaZKgvDLCPJSVW1S5JnAVft9pCcXFW36ju2WZTkQtpexosZcM+W7iz2LZIcCHyuqj462qTdd2wLWb30zzfkymsLpbUKuBIwmsF6LHBpVT25v6jWlmTTqvp933Esp6vKBvAQ4Lqs6WP5KOCnVTWYpDXJA2h7cK5Pq1a9PfCtqvrrXgNbRJKX0BL/uwNvpmuvU1Uv6TWwMaPv9fWNaePRVVldx+gEy5Ak+TaL7LOsdXtw9ibJUcDjR6vmklwPeE9V3avfyNaW5BtVdcv1jfUtyVbAgcA9aMfFRwH7rsa/uTOBy0taH4+9aGfkYJV6d0yqK7byT8DODLwSX1UNtdjGQid2H3I3BF7YFQkZTInhJJvSlgbdiXaQ9cUkbx3qgfcMFIo4Kcltq2po+9YWc9sFX2CfTfKN3qJZ3GlJfkrb//sF4ItDO0nR7VUkyQELqq1+PMnQ9gm+grYv8DNdtcC7AY/pOaZFVdUruqsfTvIJBnSCKsl1gW1oyytvTTvYgjZTfbXeAlvCwGeEgMtaLuzPmpUJnwdePpR/85EZm02fhX2W245tmwL4KbBdX8Es46zuxNR7u9uPAc7qMZ5FVWvz1MsKQ5PA5e0LvBD4aFWdnuRGwLHrec5qG1Xi25PhVuKbqbLCtIT/VsBZVXVRV0RgSMViDqXtV3xjd/vRtA+5we1rmZFCEbcH9kryA1rFzcEuFQIuTbJjVX0XoPtMGlSRpar6y7TG1n9D+1x6c5JfDXQFxdWT3KiqzgJIckPWVF4eij9W1S+SXCHJFarq2CSv7zuoxSR5BnBYVf2qqi5OcrUkT6+qt/QdG63wwhNozbfHS8RfCLyoj4DWYxYqbx4MnAY8vLv9WNp++mX3jq22pWbTaScnh2YW9lkek+TTwPu724+gFfwbmicCL2PNMtUvdGODkuQ1wCtpy+mPBG4BPLeq3rfsE1fivV0OurRZ2Eic5MSqus1oCWM3dnxV3bbv2EaSHFRV+2TtRsIjNbRZy26f2F7Ajarq5d0B7XWrahDJS5JvVtXO6xsbgrGltaPLzYBPVdXf9B3byIwtFbo77SDrLFqyuj2w92jPwxAkuQEtAbwLcEvgl7TZwFf1Gtgiktybtm9k/O/zqVX16V4DG5PkM8CDgFcBW9EOYm9bVUPb18Ji2yWGtpQ+yUOr6sN9x7E+6frF9R3Hcpb49x7clplutcQeLJhNr6onreepq26GjpMezJoZ4OOq6qN9xjPLRr8z3d/p/WjFq45bjWWrzgQu7y3dnqv30M5uDmqJQ2cWKvHt013ORFlh4C205Z97AC+nnSn+MDCUxPqkJLtV1VcAktye4Za6H3yhiGpNWm9JS1wAvlBVQ1tiCUBVHZPWNHrUPPyMqhpa36sf0opo/VtVPa3vYJZTVUd2f5837Ya+PcC/zwcCv6fNCu0FXJP2uTREmyRJdWeXk2zCwPpzVdWHZ2CJOszGjNDvktypqr4Ily1h/d16ntOHmZlNn6HjpC8Dl9C2pAziBPnILBX+6oxysT2BD1XVr7N2pdCpv7EWUVV/k9YaYm/aPrGv0Ta/DqVKJMxAJb5xXQWkHVi7WfyhvQW0uNt3BYG+DlBV5ycZ0oHMbWglr3/Y3d4OOCPJqQxvGeNilUwHVeEurSXIU1izZOR93ez1G5d5Wi8W2Q/6hSRvG9h+0FvT4nt0kv2A7wCfr6p39RvWkm7Dms+kWyYZ1GdSVf127ObQW1ocCfx3WssVaFUtj+wxnnXMyBJ1mI3Km08DDu2OQQDOZ1hbJ0Z+1a1COQ44LMnPaEv/ByfJvyw2PqSTFEkeTvtO/xxtBcUbk7ygqv6n18DWeG13uWjhr14iWt4nuoJAvwP+vqv1sSrf6S4HnUB3NvNBwBtoZeQDvKh67onSxfXsqvrPPuOYVJL3AjsCJ7Nmj0PVwHoaJvkqrYTw8V0yuDVw1FCWNC21fHFkiMsYYbiVTJOcAtxhdLCd1iz+/waWTAOQ5IO0menRl9qjgS2qalD7QbsDrjvRZlcfA1BVy/6/7cMsfCal9eb6d1pZ9jDQqsoASa5AS/zu3g0dTasOOpg9bbOwRH3okjy+xnpsJhn9X/wdcGhVPaqfyBbXfab/nva7M5pNP6wGVHFzJMnzx25uSlse+K2qGsxetm557T2r6mfd7a1pS22HVnXzhAWFvxYdG4IkW9KKAl3a/X+9RlX9ZNrv60zgMpLcgnZWa0/al9n9q+qkblnb/9FzT5TuP8ujgJlIAmlnNHceLRUasDcAHwWuneRfaWeK/7nfkNZYqtJZVf1wySetsizTVLabaRlSP6GwduGFS1lTOXBobrZg7+exSb7ZWzSLSKuueRXacqEvAHce6okJZuMz6TW0755V6Rv156jW6Pqt3c9QDXqJepLHVNX7kjxvsfur6nWLja+yfZNcpaoOAqiqC7oD108A5/Qb2rpmaTa9qg4Yv53ktcBg9ih3rjBKADu/YJi9Nmeh8BcAVfXLseu/ZZVmqk0Cl/dG2nKRF1XVZevcq+pHSYaSFHwpyZtoFUIv+08zsH0DI6fRpuZ/vL4H9qmqDktyIu1sdoAHDekALLNR6ez+y9xXDKup7LuBryYZbWx/EK0y3xDNwn7Q+1TVoCoUL2MWPpN+OqTPn+V0+ytfxbotiwbT1oDhL1EfHaQOuaXSPYAj03qCvqGbCToCOKaq9us5tsuk9SZe7ATPYGfTF3E1WkXbITky61YHPaLHeJbyXOBzSdYq/NVvSMPictAZNwuVpMY26V6D1nrha6y90X1om3RHS22vw9p7Fwcx0zZLlc6GLMm2VXV2d30X2vJFaLNX21TVJ3oLbglJvkUrCrPWflC6Dfp9LmGdkRmMtXSfn7diwJ9JSQ6kJar/y9oxDulECgBJvkjrG/eftBNBe9NmDRbd59SHbgbr4tF1WrL6+wEWBBq0bgnop2iflw8E3lZVB/Yb1ewb7e3vbm4CbE3rvfim/qJaV7fa57LvzKFWB+1+x4dc+KtXzgQuY0bOaj5pNNU9ktY7bEgOpyVUX1gw/jcM8Ax8kmfRDmR+ypqlgUXr3TIEg690NiMJwdFJ7l1V3+9mzk8CSPJE4MW0pU1Dc+++A1jGLMxgLPTSvgOYwObARcDfjo0NbTZ95KpdBdt0S4Bf2q2qGEwSSNvKsQtAd0B4cZKTRmND0X2PHwjsRvv3/j9a77Dem12PLfc/iNZz8Rjg7NH4EE9QwLC3UIy539j1S2grAS7pK5hlfJl2fPQnWjXowVlkW8qOSX4NnLpgOWuvkhxTVXdf39g0mAQu792sOat5N7qzmr1GtK7/Yd0vrw/RKt4NxQOBF9aCnotJfgn8G8NbercvcJMhbhrvjCqdfYHhVjqbhYTgecBRSfasqu8AdNUs96L1uBucIe8Hraq3d5cv6zuWSVXV5/uOYQLvrKovjQ+kleIfoou74jDfSfJM4Fxgs55jAiDJdYFtgKsmuTVr9v1uTltyNzT/BbwZeHB3+5G05XdD6B04vtz/8AVjgztBMSNbKEYrkD5dVTdd74N7lOTJtBM7n2VNddCXV9XB/Ua2jicBd2BNnHcFTgRu2MX73h5jG1X7vhqwVZJrsfZn0jarEoPLQZeWNY3YT62qm4+PDSC2m9I+wF4DvGDsrs2BF1TVYD7cskzz+vG/26Holojdc6Bn30hyNdZUOnsM7d/8sPGNxZpMWvP1t9P2AT4ZuB2wZ1Wd32dcS1nqYGZgv++b0r58F/ZhG1J1u5nZK5TkpKraZX1jQ5DktrSD6y2AV9CqML5mtIe1T0keDzyBVgzoeNYccF1Ia/00tMTllIXLu5N8Y2gVGGfBLG2hSPIx4FlDOLG3lCRnAHccnShP8hfAl6vqJss/c3V1+xYfV1U/7W5fBziU1iriuKq6Wc/x7Qs8h/Z9/qOxuy4A3rEaS4CdCVzeYM9q0vYF3Y/2ZTt+Vu5CWs+zIdlimfuuulpBbICzaJuJP8nae3B6XcK4xIHr6EDmX5J8F3hxVR2zupEtbegJQbd0bW9av6MvA3vUsHruLfQK2vKwtQ5meo5pofcC3wbuRWtqvhctMRiMqhryDDUASe5Aa1Wz9YJl1ZvT9goNTlWNloX9hoH1i6vW0uCQJA+tqg/3Hc8EPtWtTPgA7XP/EcARaaXk8aTfBhn8Foox1wJOT+tLPV7sbzD7lGnVQC8cu31hNzY0244SwM7PurFfJvljX0GNdHtoD0zyrOqpL7FJ4PL2pU3VPpt28LUH8PheI+pU1ceAjyW5Q1X9X9/xrMcJSZ5SVe8YH+yWFJzYU0zL+WH3c+XuZxCWO3DtlpHcDDisuxyKwSYEY0l1aC0N7g78LMngZoPGDPZgJskVu9nzv6yqhyV5YFUdkuS/WHc/sNbvyrSTjldk7WXVF9Da1gxGksOXu38IB7BJ7g+cMkoA05pyPxT4AbBvVX2vz/gW8fDucmE1w0fSPreGtvd/yGZhC8XIS/oOYCljJ6POpFXU/hjt/+IDgVN6C2xpn0vyCdoWKWi/759La2fyq96iWtfbkzwbuHN3+3PA26tq6omqy0FnXJLXAK+k9T46kla85LlV9b5ln7iKuin4jwJ/YE3StyvtIOfBtQoNMS+P7kuDqvpN37FMKslTR3uzhiDJ17sZq1Fj5ivRKont1ndssyjJZ2hLV18FbEU7s3nbqrpjn3HBmiWKSb5WVbdLchzwdOAnwNcGVlBrZiTZvqp+kORqVXVR3/EsJsl5wNm0PWtfZUGfzSHsvUxyCrBbVV2U5H60giaPAm4NPKyq7tVrgJqa7qD/d7SaDoNuFj8uyVbAL2ogB+pJ9l/u/qHtB+9O6D4UGO2h/hLw4aH8fY4keSdwJdb0sHwscGlVPXnq7z2wv4tByJqWBosawlnNkSQnV9WtkjyYtjz0ebS1zoPbN9AtXRvNUp1eVZ/tM56lJLkZbQZry27o57R15af3F9VsMiFYWd3BzGg/6KAOZsaSwCcDHwZuDryHNpv1kiGdnJgl3bLQdwGbVdV2SW4JPLWqnt5zaJfpViLck5ZU3QL4JPD+IX1mju+nS3IwcEZV/Xt3e3B7LLul9E+nleEv2izW24a0XD3rVl8EGFz1RWgnU4Cdquoz3b76TarqwvU9b7Uk2Q14NfBL2sqz99JO9F2BdvxxZI/haYoW2+u7Wvt/XQ66uNf2HcAGuFJ3uSfwoar6dTv5MTxVdSywWF/DoTkIeF4XL0nuCryDtj9HG+agrurVS2hV5DZjWOXiZ0pVjS9hOmTJB/bj2mPLhUb7wd7cXV59kcdrMq+nLac+HKCqvpHkzss+Y5VV1aW0lShHpvXlehRt2dXLVqO4wYTSre64iLb0+y1j9226+FN6dShtr9Vor9CjaYnBw3qLaF2j6ouj7/W7MqDqiyNJngLsQzuxuyOt8uLbaP8PhuJNwItoJ/Y+C9ynqr6SVgTw/bTfr0FIsiutjdL2rN1LeShttIDLTlL8O3Bt2onToW71uDTJjlX1XbisPcylq/HGJoGLGMLSlQ3w8STfpi11+PskW9NmCnT5XX2UAAJU1WgNuTZQVb2zu/p53MPyZ1uiONCvgROA51e/PcQ2oSX5i52FcsnJn6Gqzl5wcm9VDhA2RJf87UlLAHcA3kDbBjAUrwdOpu2p/FZVnQCQ1i5icP1qgZtV1c5jt49N8s3eolncFYG/WqT64u2B42hJ6xA8g1b5+asAVfWdtDY7Q3LFqjoKoEugvwJQVd8e4In9w2hV6U+l9QkcqtcA96+qQdQhWMYLaL/fZ9G+P7dnlQprmQQuIzPQLL6q9uv2Bf66qi5N8lvaJl1dfmcleQlrvsAeQ6sYqg3UHRg+lHZQOH7G8OV9xTTjXg+cQ+shFlqRiB1pje4Ppp2J78uP/XedirOT3BGobk/tvgykuNJIkkNpS/2PAF5WVaf1HNI6qurgtJLx1wa+MXbXTxhYJdPOSUl2GyUDSW5PO9kzJIOuvjjm4qr6wyiZSnJFhndiajyZ+t2C+4YW63lVtWwxqIH46QwkgKMq5TvRqv5DW6p+8XLPWSnuCVxGki+ypln8/emaxVfVYJazJXncYuNVdehqx7Kx6JYvvoy2FwPaXoyX1kB7xw1ZkiNpM1UnMjZ7UVUH9BbUAjO0ZGSpvQOjfcG99hAbFQHq6/03Vl1xiAOBe9D+bx5Fq2bZ+z7QkSR/Yk21xfGDisH+Lg1dkm/RDgpH/eK2A84ALqH9nfa+9C7JW2hxjVdfPIc2s/GJqrpbX7GN606U/wp4HPAs2l7Lb1bVi/uMa1ySS2m/Q6G1zhoVgQqwaVVdaannrra0/rqPAo5h7TZaQ+u1eSBwXeB/GXCcAN2Jvh1Y+2T51I/jTQKXkQE3ix9JMt5bZFPaGveTqmpQJcQ1n5KcVj03ZF2fJGcyG0tGSPJ/tJNS/9MN/R1t/+puo2Swx9i2LHuXSSuiK2SypKr6wWrFspQZqr54Bdr+xb+lJVWfBt45tDhnRZL3ATcFTmfNDGbVQPr/jiR59yLDQ4zzvbQVPSez5mR5VdWzp/7e/g4sLcmXabNB/0PbqHsu8OqqusmyT+xRki2AD1TVvfuOZdbMUlXYWZHkIOCNVXVq37EsJcmXqmr39T+yf92G8QNpxRgK+ArwXNpn022q6os9hqcVlOQfq+o13Ym+dT6XVuMAQasvyeZVdUG6pvALeaJlckluUlVnLHHf7lX1pdWOaWOQ5IwhHwfPmm7Wf+c+TkqYBC4jyW1pey+2oJXsvSbwmtEa/SHq9oycXlU37juWWZPkLt3Vh9CWEIx6LT6Ktrb8ub0ENoOSnEY7Q3hFYCfansqLWbM8bAhLmUblze/CjCwZ0fzoigQ8jnaGeB1VNbTqsIO2VFI1MpTkKsknqup+Sb5HS/7Hq4LUkGoSDH0pfbdM+b3AM2pBv98htgWZFd0M239U1dAKFQGzdwItyYeAZ1fVqheosjDMMqrq+O7qbxjmxvGFs1ebAH8FfLC/iGbXqCpskgOqatexuz6eZGgb8oduG+BWfQexHvcfu34RbanQSAGDSwK76r9PYd29A4Na3qIV8QbgP4Dr0T7T319VX+83pJl2ImuSqu2A87vrW9D23d2wt8jGVNX9ustBxLMeQ6++eDptj+JJSR634AT+4EpuzpDdgJO7ExWDOrnbGf1/nJXjtq2Abyb5GmufiJ766jOTwEUkWbbq0cCWBY73NLyElgg+oqdYNhZXT3KjUbn9JDfEPmcb6ntD2LOynKraGxZfFpRkqMtDP0YrVPQZBtgmQCunql4PvL7bG/ZI4OAkV6VVhn1/VX2nz/hmzSipSvIO4KNVdUR3+z7Ag3oMbS1Jlp2dqqqTViuWCQy9+uIfq+rFXVXYw5IcAryyqv7E8CpuzpKhbzf6ISy+WiLJ369+OOv10r7e2OWgi0hyHnA2rUHnV1lwxqgG1kew63P0aFoT2e/RNmYPpUHvzElyb1rD+PGeLU+tqk/3GtgMSXIO8Lql7q+qJe9bbYstCxrqUqG+i7+oX91n/cHALapqk77jmUXjhd6WG+tLkmOXubuqao9VC2Y9hl59cfxzvKuX8FbaLPBewEeG+Bk/S7pei+Pt0364zMNXTbeU/mFVdeKC8ZfRZq4H8e+eJOvbBzjJY/4czgQu7rrAPWl7wR4NfJJ25vX0XqMak+TGtPgeBfwc+G9aUj+IksyzrKqO7Hq23LQb+natUs+WjchyjcMHIckdgDsCWyd53thdm9PiH6JPJLnvaBZDG7+up9l9aLOBdwc+R49njjcCP0ryz6zZ870X8KMe41nLjH2Hb86wl9Jf9v1TVb8CHpXk8cAXaW0YdDkkeQBwAHB9Wm/I7WlLMP+6z7jGPAz4UJK9qur/uiq2b6W1XLlrr5Gt7dgkHwY+Np5AJ7kyrSjl44FjgfdMKwBnAtcjrdn1o2h7M142lBm2bsPzF4AnVdWZ3dhZQ9o0Psv66tmysRjqTNq4rhDQXYGnAW8bu+tC4ONDXG6X5ELa0uSLgT8ysEIMWjlJRici7wt8DfgA7WDht8s+UcvqCsTsD9yZlrAcB7x8QIVh9qiqz44VrlrLUGbZZkGSp1fVWxYZvxHwj1X1tB7CmnlJvgHsAXymqm6d5G7AY6rqST2HdpkktwA+CjyDto8e4NFDOqGfZFPgibQTUTek9bK8KnAFWj/Yt0x7H7hJ4BK65G9P2pfwDsDhwMFVdW6fcY0keRDtzPDuwJG0A4R3zshm8kHrs2fLxiIz1Dg8yfZD37+o+ZPks7T9fx+uqvP7jmdjk+TqQ0yok7ysqvafhR5nSW4AvJE1fQK/AOxbVef0F5WmLckJVbVrlwzeuqr+lOQbVXXLvmODtSoB70xbqvwZ4Jl0PQ2HcsJnXFfZfyvgd92s9eq8r0ngupIcCtwMOILWc++0nkNaUpKrAw+kJat7AIfSNr0f1WtgM6zPni0bi8xQ4/BuD85iZaSHtPfmplX17aWKRgysWIQ0WN0qj3cCm1XVdkluSdvz/fSeQ5s5SY6mnah4bzf0GGCvqrpnf1Fp2pJ8hlZM6VW0xOVnwG2r6o59xjUy1l4F1iwJHlUGHlSblb6ZBC6iW2o5OkM4/hc06KVXSa5FWwv9iKq6e9/xzKo+e7Zo9SW5zdjNTYGHApdU1T/2FNI6khxUVfssKBpx2WfTkBJWaciSfBX4O+Dw0WqFJKdV1c36jWxt3Wqkh7LutoSX9xXTQosVqrJ41cYryV8C16GtkvodbdniXrQ9gZ9cWIhFw2dhmEVU1RX6juHy6JYMHdT96PLrrWeLVt8iX1xf6v7th+SdSa47KhrRFTd4KPB9LBIibZCqOrvVirjMENutfAz4Na2/4WD2MS3wiySPoVVSh7Yi6Rc9xrOoJHcHvlxVv+s7lhn3euCFY8uo/wQckuTmwL+xdu9dzQCTQGldL+07AK2esf0D0M5s3ga4Zk/hLOVtwD0AktyZtgznWcCtaCd9/q63yKTZcna3JLS6fTj7sqa59JDcoKqG3o/tibQ9gf9JW5nwZWDvXiNa3OOAtyb5JW3f4nHAF91ru8GuU1WnLhysqlOT7NBDPPozmQRKCwytD6Sm7kTW7Be4hNZrczBVzjqbjO2xfARwUFV9GPhwkpP7C0uaOU8DDgS2Ac6lVeEb4n7ALye5+WIH3UPRFdQa/AqZqno8QJLr006YvZnW3sBj4A2zxTL32XJjBvkLIHW68vuLbZId9F5Q/XlmpKLuJkmuWFWX0HrF7TN2n5/j0uRuUlV7jQ8k2R34Uk/xrCXJqbTvoSsCe3eNry9mzffQLfqMDyDJG1n8uxKAoVXS7pas/g1wc1pf5TfRZgS1YU5I8pSqesf4YJIn006mDsKC1T3rmJWidavBwjCS5lq3JOzvaX3DoDXjfntV/bG3oBZI8mJav7ifA9sBu1RVdRv1D6mq3Zd9AUnA4j1Mh9TXNMn2y90/hHY23Z7kkZfR+i5epqoOWd2Ilpfk58B3acvqj62q7/cb0WxKch1a770/sCbp2xW4MvDgqvpJX7GNG6sOGtr35fnd9S2AH87Iid9VYRIoaa4leSdwJWB04PJY4NKqenJ/Ua0ryW7A9YCjRhvzk9yYVureFhHSMpLcAbgj8BzaHraRzWkHsEPpcbYpbcnqXwKnAu/qVgAM0qz0hE3y17QTfXcCdgLOqKrH9hvVbOqaw4+q6Z5eVZ/tM56lJHkHrWXaEd3t+wAPqqqn9hvZcLiMSNK8u+2CA8DPdk1wB6WqvrLI2P/rIxZpBl0Z2Ix23HONsfELGFZhpUOAP9KWK96H1vB6314jWt7gZxKSbE6bEdqe1nLjmnSNw7XhqupY4Nj1PrB/u1XVU0Y3qupTSV7TZ0BDYxIoad5dmmTHqvouQJIbMcyS8ZIup67g1+eTvGcISyqXsXNV3RwgybuAobWrmUVfHPt5U1Wd03M8Wh0/SvLPwPu623sBP+oxnsExCZQ0714AHNsVYAjtbPEQy5xLupySvL6qngO8Kck6s1cD6gN72V7kqrpkQT/DQVhQRO1qSS4Y3cUAi6iNiukkuVpVXdR3PFo1j6LtV/0o7f/rcd2YOu4JlDT3klwFuEl384yqGmpzZkmXQ5LbVNWJSe6y2P1DaQ2U5FJg1Iw7tNL7FzHQBGsWdPtB30XbP71dklsCT62qIbYG0QpLcvWxBvcaYxIoaS4luS1w9qiiWZLHAQ8FfgC81DLSkjT7knyVtu/z8FERmySnVdXNln+mZlmSOwLvxOR/SVfoOwBJ6snbaaWuSXJn4NXAocCvgYN6jEvSlCTZPcnRSf5fkrOSfK9bCq6NWFWdvWDIfd8bv/8E7gX8AqCqvsGaVlDCPYGS5tcmY7N9jwAOqqoPAx9OcnJ/YUmaoncBz6X1OTMRmA9nd7NC1fWF3Rf4Vs8xaRVU1dkL9tX6Oz/GJFDSvNokyRW7Hlx3B/YZu8/PRmnj9Ouq+lTfQWhVPQ04ENgGOBc4CnhGrxFpNZj8r4d7AiXNpSQvBu4L/JzWQ2qXqqokfwkcUlW79xqgpBWX5NXAJsBHgMsKQFXVSb0FJWnFJdmKlvzfg1ZY6Sjg2e73X8MkUNLcSrIbcD3gqFH1sCQ3pm0k96BQ2sgkWazJdVXVHqsejKYqyb8sc3dV1StWLRituiS7V9WX1jc2z0wCJUmStFFJ8vxFhq8OPAn4i6rabJVD0ipKclJV7bK+sXnmvhdJkrRRS/K8BUNFWwr+xar6Xg8hacqq6oDR9STXoO0J2xv4AHDAUs/TbOv6Qt4R2HrB7/3mtKXg6tgiQpIkbeyuseBnc2BX4FNJHtlnYJqeJFsmeSVwCm3iY5eq+qeq+lnPoWl6rgxsRvv3Hv+dv4DWL1Idl4NKkqS5lGRL4DMuEdv4JPkP4CG0vq9vrqrf9BySVlGS7avqB33HMWQmgZIkaW4l+XpV3brvOLSykvyJVgH2Etry38vuohWG2byXwDRVSV5fVc9J8nHW/ncHoKoe0ENYg+SeQEmSNJeS3A04v+84tPKqyi1P8+m93eVre41iBjgTKEmSNmpJTmXdWYEtgR8Bj6uqb69+VJLUH5NASZK0UUuy/YKhAn4x6g8qaeOSZHfgpcD2tJWPo2XAN+ozriExCZQkSZK00UjybeC5wInApaPxqvpFb0ENjHsCJUmSJG1Mfl1Vn+o7iCFzJlCSJEnSRiPJq2nN4T9CqxILQFWd1FtQA2MSKEmSJGmjkeTYRYarqvZY9WAGyiRQkiRJkuaIewIlSZIkzbwkz1swVMDPgS9W1fd6CGmwbKQpSZIkaWNwjQU/mwO7Ap9K8sg+Axsal4NKkiRJ2mgl2RL4TFXt0ncsQ+FMoCRJkqSNVlX9ktYwXh2TQEmSJEkbrSR3A87vO44hsTCMJEmSpJmX5FRaMZhxWwI/Ah63+hENl3sCJUmSJM28JNsvGCrgF1X12z7iGTKTQEmSJEmaI+4JlCRJkqQ5YhIoSZIkSXPEJFCSNNeSXDfJB5J8N8mJSY5IcuMVfP27JrnjMvc/IMl+K/V+kiStj3sCJUlzK0mALwOHVNXburFbAptX1RdW6D1eCvymql67yH1XrKpLVuJ9JEmalEmgJGluJdkDeGlV3XnBeIDXAPehVZd7ZVX9d5K7Av9QVffrHvcm4ISqek+S7wOHAPcHrgQ8DPg98BXgUuA84FnAk7rxWwNfAk4Bdq2qZybZGngbsF0XynOq6ktJ7gIc2I0VcOequnCF/zokSXPCPoGSpHl2M+DERcYfAtwKuCWwFXB8kuMmeL2fV9UuSZ5OSxafnORtjM0EJnkScAPgjlV1aZInjD3/QOA/q+qLSbYDPg38FfAPwDO6hHAzWhIpSdLlYhIoSdK67gS8v6ouBX6a5PPAbYEL1vO8j3SXJ9ISyaV8qHvthe4B7NwmIgHYvEv6vgS8LslhwEeq6pwJ/xySJK3DwjCSpHl2OnCbDXj8Jaz93bnpgvsv7i4vZfkTrUs1Lr4CsFtV3ar72aaqflNVrwaeDFwV+FKSm25AzJIkrcUkUJI0zz4LXCXJPqOBJLcAfgU8Iskm3T69OwNfA35Am6m7SpItgLtP8B4XAteYMJ6jaPsGR7HcqrvcsapOrap/B44HTAIlSZebSaAkaW5Vq472YOAeXYuI04FXAf9FK9jyDVqi+I9V9ZOqOhv4IHBad/n1Cd7m48CDk5yc5G/W89hnA7smOSXJN4GndePPSXJaklOAPwKf2rA/qSRJa1gdVJIkSZLmiDOBkiRJkjRHTAIlSZIkaY6YBEqSJEnSHDEJlCRJkqQ5YhIoSZIkSXPEJFCSJEmS5ohJoCRJkiTNEZNASZIkSZoj/x+6OMRbQdGykAAAAABJRU5ErkJggg==\n",
1123 |       "text/plain": [
1124 |        "<Figure size 1080x360 with 1 Axes>"
1125 |       ]
1126 |      },
1127 |      "metadata": {
1128 |       "needs_background": "light"
1129 |      },
1130 |      "output_type": "display_data"
1131 |     }
1132 |    ],
1133 |    "source": [
1134 |     "plt.figure(figsize=(15,5))\n",
1135 |     "ax = df[df.booking_complete ==1].booking_origin.value_counts()[:20].plot(kind=\"bar\")\n",
1136 |     "ax.set_xlabel(\"Countries\")\n",
1137 |     "ax.set_ylabel(\"Number of complete bookings\")"
1138 |    ]
1139 |   },
1140 |   {
1141 |    "cell_type": "markdown",
1142 |    "id": "8ae2cf98-2b59-4d0c-afa6-b92269949ff4",
1143 |    "metadata": {},
1144 |    "source": [
1145 |     "Above chart shows travellers from which country had their booking complete.  "
1146 |    ]
1147 |   },
1148 |   {
1149 |    "cell_type": "markdown",
1150 |    "id": "92ebdc99-d53b-48ed-92c5-a875e8bf62d3",
1151 |    "metadata": {},
1152 |    "source": [
1153 |     "### Booking complete"
1154 |    ]
1155 |   },
1156 |   {
1157 |    "cell_type": "code",
1158 |    "execution_count": 117,
1159 |    "id": "a7039c39-6989-4c36-b61d-acc4cc8fcbb4",
1160 |    "metadata": {},
1161 |    "outputs": [],
1162 |    "source": [
1163 |     "successful_booking_per = df.booking_complete.value_counts().values[0] / len(df) * 100"
1164 |    ]
1165 |   },
1166 |   {
1167 |    "cell_type": "code",
1168 |    "execution_count": 118,
1169 |    "id": "215d81a8-44dd-462d-81c7-acc3b6786ccf",
1170 |    "metadata": {},
1171 |    "outputs": [],
1172 |    "source": [
1173 |     "unsuccessful_booking_per = 100-successful_booking_per"
1174 |    ]
1175 |   },
1176 |   {
1177 |    "cell_type": "code",
1178 |    "execution_count": 127,
1179 |    "id": "3e64b38d-03b0-419e-9fc1-7bf465c0cee6",
1180 |    "metadata": {},
1181 |    "outputs": [
1182 |     {
1183 |      "name": "stdout",
1184 |      "output_type": "stream",
1185 |      "text": [
1186 |       "Out of 50000 booking entries only 14.96 % bookings were successfull or complete.\n"
1187 |      ]
1188 |     }
1189 |    ],
1190 |    "source": [
1191 |     "print(f\"Out of 50000 booking entries only {round(unsuccessful_booking_per,2)} % bookings were successfull or complete.\")"
1192 |    ]
1193 |   },
1194 |   {
1195 |    "cell_type": "code",
1196 |    "execution_count": null,
1197 |    "id": "6a1f9ef8-5fc9-4632-a6f7-4fc62d6a1828",
1198 |    "metadata": {},
1199 |    "outputs": [],
1200 |    "source": [
1201 |     "\n"
1202 |    ]
1203 |   },
1204 |   {
1205 |    "cell_type": "code",
1206 |    "execution_count": null,
1207 |    "id": "b355a529-18e7-4696-b251-1e1df3e865b2",
1208 |    "metadata": {},
1209 |    "outputs": [],
1210 |    "source": []
1211 |   }
1212 |  ],
1213 |  "metadata": {
1214 |   "kernelspec": {
1215 |    "display_name": "Python 3 (ipykernel)",
1216 |    "language": "python",
1217 |    "name": "python3"
1218 |   },
1219 |   "language_info": {
1220 |    "codemirror_mode": {
1221 |     "name": "ipython",
1222 |     "version": 3
1223 |    },
1224 |    "file_extension": ".py",
1225 |    "mimetype": "text/x-python",
1226 |    "name": "python",
1227 |    "nbconvert_exporter": "python",
1228 |    "pygments_lexer": "ipython3",
1229 |    "version": "3.9.7"
1230 |   }
1231 |  },
1232 |  "nbformat": 4,
1233 |  "nbformat_minor": 5
1234 | }
1235 | 


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