kchung.app

01

British Airways Data Science Internship Project

  • Data Science,
  • Machine Learning,
  • Sentiment Analysis
  • Data Manipulation,
  • Web Scraping,
  • BeautifulSoup
  • Python,
  • NLTK,
  • spaCy,
  • NLP

This project focuses on leveraging data analytics and predictive modeling to uncover crucial business insights and forecast customer buying behavior for British Airways. The objective is to assist the airline in acquiring customers before they embark on their holidays. By applying advanced analytical techniques, the project delivers actionable recommendations, tools, and models that drive informed business decisions and optimize customer acquisition strategies.

Predictive Machine Learning Model

Customer Booking Dataset
Size: 50,000 entries; 14 features


PowerPoint A (Insights)
Kaggle Notebook A (Predictive Model)
Comprehensive Data Analysis

Web-scraping Data
Source: skytrax (airlinequality.com)
Date collected: 2nd, September, 2024


From: 9th, October, 2011
To: 2nd, September, 2024
Duration: 12 years, and 11 months
Size: 3,855 reviews


PowerPoint B (Insights)
Kaggle Notebook B (Comprehensive Analysis)
Project Summary:

Wordcloud Animation

Wordcloud Airplane Silhouette

Review Count by Year & Month

(See PowerPoint A for insights)

Graph 1

Review Count by Year Bar Chart (1/2)

Graph 2

Review Count by Month Bar Chart (2/2)

Sentiment Score through various perspectives

(See PowerPoint A for insights)

Graph 1

Sentiment Score Ovver Years Line Chart (1/4)

Graph 2

Sentiment Score Trends Over Months Grouped Bar Chart (2/4)

Graph 3

Sentiment Score by Count Grouped Bar Chart (3/4)

Graph 4

Sentiment Score Distribution Pie Chart (4/4)

Review Status

(See PowerPoint A for insights)

Graph 1

Status Trends Over Years Line Chart (1/2)

Graph 2

Review Status Pie Chart (2/2)

ML Model

(See PowerPoint B for insights)

Graph 1

Feature Importances (1/2)

Graph 2

Model Confusion Matrix Heatmap, model struggles to predict True Positive due to lack of customer-centric data (2/2)

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