I am currently working on a customer churn project and I'm struggling with the initial setup. Does anyone have a step-by-step guide on how to integrate predictive modeling into a standard Python workflow? I specifically need advice on handling imbalanced datasets and which libraries are most reliable for high-accuracy results in a production setting.
3 answers
When you start with predictive modeling, the first hurdle is always data preprocessing. For retail churn, you should utilize the Scikit-learn library, specifically the SMOTE technique from imblearn to handle those imbalanced classes you mentioned. Start by cleaning your features and performing robust exploratory data analysis. Once your data is balanced, I recommend using XGBoost or LightGBM as they tend to perform exceptionally well with tabular retail data. Always ensure you are using cross-validation to prevent overfitting before you even think about deploying to a production environment.
Have you considered whether your features are actually capturing temporal behavior, or are you just looking at static snapshots for your predictive modeling?
I suggest focusing on feature engineering first; it usually provides a bigger boost to predictive modeling performance than just tuning the algorithm hyperparameters.
Susan is right. I found that creating "days since last purchase" as a feature improved my model's F1-score by nearly 15%.
Kevin, that is a great point. I am currently using a rolling window approach to capture monthly spending habits. It has definitely improved the accuracy of the model compared to the static baseline I had last week.