WQD7005 Data Mining AA1 The case study focuses on analyzing customer behavior in an e-commerce context using a dataset of customer transactions. The primary objective is to understand the factors that influence customer churn, which is defined as customers ceasing to purchase from the e-commerce platform.
Key goals of the case study include:
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Data Understanding and Preparation:
- To explore customer attributes and purchase history.
- To preprocess the data by handling missing values and ensuring data quality.
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Predictive Modeling:
- To employ a decision tree to identify key predictors of churn.
- To use ensemble methods, such as Random Forest and Gradient Boosting, to improve the predictive accuracy.
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Behavioral Insights:
- To gain insights into customer purchase patterns and preferences.
- To determine the profile of customers who are at risk of churning.
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Business Strategy Development:
- To leverage the model findings to devise strategies aimed at improving customer retention.
- To suggest targeted interventions for customer segments identified as having a higher churn risk.