Project Description:

In this project, I explored how data science can drive strategic decision-making at British Airways by focusing on customer experience and operational efficiency. Using web scraping techniques, I collected large volumes of customer reviews from various online platforms. Through comprehensive data cleaning and sentiment analysis, I uncovered key themes influencing passenger satisfaction and loyalty. Building on these insights, I developed a predictive model to identify the most critical factors influencing buying behavior. These findings offer data-driven recommendations aimed at enhancing the customer journey, reducing churn, and ultimately increasing profitability for British Airways.

Project Summary

• Objective: Simulated how data science drives strategic decisions at British Airways, emphasizing its role in improving customer satisfaction and operational efficiency.

• Data Collection & Analysis: Scraped and analyzed large volumes of customer review data to identify sentiment trends, key pain points, and overall satisfaction drivers.

• Predictive Modeling: Built a machine learning model to understand the primary factors influencing buying behavior, enabling data-driven recommendations that could increase customer retention and revenue.

This end-to-end data science project demonstrates how thorough data gathering, advanced analytics, and predictive modeling can guide business strategy and enhance the customer experience.