British Airways Data Science Virtual Internship

This repository includes two data science tasks completed as part of the British Airways Data Science Virtual Internship on the Forage platform:

• Task 1: Web Scraping & Sentiment Analysis
• Task 2: Predictive Modeling of Customer Bookings

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📌 Task Overview

Task	Description	Tools & Libraries Used
Task 1	Collected and analyzed customer reviews from Skytrax using sentiment analysis	BeautifulSoup, NLTK, TextBlob, VADER, WordCloud
Task 2	Developed ML models to predict whether users would complete a flight booking	pandas, scikit-learn, RandomForest, matplotlib, seaborn

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🛠️ Task 1 – Web Scraping & Sentiment Analysis

🎯 Objective

Scrape British Airways customer reviews from Skytrax and perform sentiment analysis to identify customer satisfaction patterns.

🔍 Key Steps

• Collected ~1000 reviews using requests and BeautifulSoup.
• Preprocessed text using NLTK (tokenization, stopwords, POS tagging).
• Applied sentiment analysis:
  • VADER SentimentIntensityAnalyzer
  • TextBlob polarity scoring
• Created visual summaries with word clouds and frequency charts.

📊 Key Results

• VADER: ~61% positive reviews
• TextBlob: ~67% positive sentiment
• Common keywords: service, flight, crew, food

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🤖 Task 2 – Predictive Modeling of Customer Bookings

🎯 Objective

Build a machine learning model to predict whether a user will complete a flight booking.

🔍 Key Steps

• Performed EDA and feature engineering (e.g., days_to_flight, length_of_stay)
• Addressed class imbalance and outliers.
• Evaluated feature importance using mutual_info_classif.

📈 Model Performance

• Model: Random Forest Classifier
• Train Accuracy: 99.98%
• Test Accuracy: 88.05%
• ROC AUC and confusion matrix used for evaluation.

✅ Recommendations

• Use class_weight='balanced' to address class imbalance
• Consider improving with SMOTE, XGBoost, or hyperparameter tuning via GridSearchCV.

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📊 Sample Visualizations

Task 1 - Word Cloud

Task 2 - Important Features

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📂 Directory Structure

├── Task 1/
│   └── Task1_british_airways_web_scraping.ipynb
├── Task 2/
│   └── Task2_Predictive_modeling_of_customer_bookings.ipynb
└── README.md

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⚙️ Installation

# Install the required packages
pip install -r requirements.txt

# Launch the Jupyter Notebook interface
jupyter notebook

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👩‍💻 Author

Ece Yavuzyilmaz
Participant, British Airways Data Science Virtual Internship
🔗 GitHub Profile