Welcome to the AllLifeBank Personal Loan Campaign Modelling project! This project, part of the "Supervised Learning: Classification" course, aims to utilize the AllLifeBank dataset to build a predictive model. This model's goal is to assist the marketing department in identifying potential customers more likely to purchase a personal loan.
The challenge is to leverage customer data to predict the likelihood of a customer purchasing a personal loan. This analysis and predictive model need to be accurate to allow AllLifeBank's marketing department to plan and target their campaigns effectively and efficiently.
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Data Analysis: Conduct a thorough analysis of the AllLifeBank dataset, emphasizing factors that affect the likelihood of personal loan acceptance.
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Model Development: Use classification algorithms to develop a predictive model that identifies potential loan customers from AllLifeBank's broader customer base.
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Strategic Insight: Provide actionable strategic insight to the marketing department, using the model's findings to optimize campaign targeting and potentially improve acceptance rates for personal loan offers.
- Knowledge of supervised learning techniques, particularly classification algorithms.
- Software requirements: Python and associated libraries and packages (numpy, pandas, scikit-learn, matplotlib, seaborn).