Enhancing Model Precision

Requirements Clarification & Assessment

1. Understanding the Problem Context:

   • Objective: Develop a classification model to predict customer purchases on an e-commerce platform post-search.
   • Metric of Interest: Precision, which measures the ratio of true positive predictions to the total number of positive predictions made by the model.
   • Assumption of Imbalanced Data: The dataset likely contains more instances of customers not making a purchase (negative class) than those who do (positive class).

2. Data Understanding:

   • Class Imbalance: Determine the extent of class imbalance and its impact on model performance.
   • Feature Analysis: Identify features that may be influential in predicting purchases, such as user behavior, search terms, and historical purchase data.
   • Data Quality: Assess the dataset for missing values, noise, or outliers that could affect model precision.

3. Model Evaluation:

   • Current Model Performance: Examine the current precision and recall to understand the trade-offs and areas for improvement.
   • Alternative Metrics: Consider using precision-recall curves for evaluation due to the class imbalance.

4. Constraints and Considerations:

   • Business Impact: Ensure that improvements in precision do not significantly reduce recall, as missing potential customers could have business implications.
   • Resource Availability: Evaluate the availability of computational resources and time constraints for model retraining and feature engineering.