- Extracted customer reviews of British Airways flights from a travel review website using web scraping.
- Classified reviews into positive, negative, and neutral categories using NLP.
- Visualized sentiment trends using charts and word clouds.
- Analyzed flight booking data to discover customer ticket buying behavior.
- Applied machine learning to predict which days of the week typically have the most flight bookings.
| Area | Tools & Libraries |
|---|---|
| Web Scraping | BeautifulSoup, requests |
| Data Processing | pandas, numpy |
| NLP & Sentiment Analysis | nltk, spaCy, TextBlob |
| Visualization | matplotlib, seaborn, wordcloud |
| Machine Learning | scikit-learn (RandomForest, DecisionTree, etc.) |
- Scraped British Airways customer reviews using
BeautifulSoupandrequests. - Parsed HTML pages to extract review text, rating, review date, and reviewer details.
- Loaded separate dataset for flight bookings containing booking date, flight details, and ticket class.
- Cleaned review texts (removal of stopwords, punctuation, lemmatization).
- Labeled reviews into:
- Positive
- Negative
- Neutral
- Applied polarity scoring using
TextBloband keyword-based tagging for validation.
- Word Clouds: Generated for each sentiment group to highlight frequent terms.
- Bar Charts & Pie Charts: To show the proportion of sentiment types and keyword frequency.
- Explored trends based on routes, review dates, and classes.
- Preprocessed flight booking data and extracted relevant features (e.g., day of the week, seasonality).
- Explored booking frequency trends visually.
- Trained machine learning models to predict the most common days for ticket purchases.
- Evaluated model performance using accuracy and cross-validation.
- Common issues found in negative reviews included delays, customer service, and seat comfort.
- Positive reviews focused on cleanliness, flight staff behavior, and on-time performance.
- Most bookings occurred mid-week, suggesting strategic pricing/marketing opportunities.