This project showcases how machine learning can revolutionize customer analysis and benefit predictions, while emphasizing the importance of data privacy. It reflects the effective application of advanced algorithms to achieve actionable results for real-world business objectives.

Project Overview

Sure Tomorrow Insurance Company set out to explore the potential of machine learning to tackle key business goals:

1. Identify customers with similar characteristics to a target customer for personalized marketing campaigns.
2. Predict the likelihood of a new customer receiving an insurance benefit, improving performance against a dummy baseline.
3. Forecast the number of benefits a customer may receive using linear regression.
4. Protect sensitive customer data through effective obfuscation techniques without compromising model performance.

These tasks align with the company’s vision for enhancing customer engagement and safeguarding data security.

Methodology and Results

The project followed a structured and meticulous approach:

• Data Preprocessing
• Task Implementation:
  1. Customer Similarity Analysis
  2. Insurance Benefit Prediction
  3. Benefit Forecasting
  4. Data Protection
• Model Evaluation

Conclusion

The project delivered the following key findings:

• Customer segmentation: Machine learning effectively identified clusters for targeted marketing.
• Predictive reliability: Models reliably forecasted insurance benefit likelihood and quantity.
• Privacy protection: Data obfuscation techniques successfully preserved model performance in linear regression.
• Data preprocessing: Scaling and addressing class imbalance are critical steps for improving overall model accuracy.

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