British-Airways-Data-Science-Job-Simulation

This project focuses on analyzing customer reviews from the British Airways page on Airline Quality and predicting customer buying behavior using machine learning techniques.

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Introduction

This project is divided into two main tasks:

1. Conducting sentiment analysis on British Airways customer reviews to classify them as positive or negative.
2. Building a classification model to predict customer buying behavior based on structured data.

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Task 1: Sentiment Analysis

The customer reviews were retrieved from the British Airways page on Airline Quality.

• Web scraping was performed to collect unstructured data.
• Text preprocessing techniques (e.g., tokenization, stop-word removal) were applied.
• Sentiment analysis was conducted to classify reviews as positive or negative.
• Insights and analysis were summarized in a PowerPoint presentation.

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Task 2: Customer Behavior Prediction

Creating a classification model to predict customer buying behavior.

Challenges

The dataset exhibited a significant class imbalance, making it difficult for models to accurately predict positive class instances.

Approach

• Implemented several classification models.
• Performed cross-validation and hyperparameter tuning to optimize model performance.
• Used SMOTE (Synthetic Minority Over-sampling Technique) to address the class imbalance.

Best Performing Model

• Model: XGBoost
• Metrics:

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Observations

• All models struggled with accurately predicting the positive class due to class imbalance.
• Predictions were skewed towards the majority class, impacting the ability to generalize.
• SMOTE helped improve the model's performance in handling the positive class, but the results were still suboptimal.

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Certificate

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