KolatimiDave/Expresso-Customer-Churn-Prediction

This repository explains how to predict customer churn. An Hackathon Organized by Data Science Nigeria(DSN-AI) to help Expresso predict customer Churn. My 2nd place solution, log_loss of 0.246675. I've also added a section in the notebook to get a score of 0.246643, which could be the unofficial 1st place solution. # Expresso-Customer-Churn-Prediction This repository explains how to predict customer churn. An Hackathon Organized by Data Science Nigeria(DSN-AI) to help Expresso predict customer Churn. My 2nd place solution , log_loss of 0.246675 on Zindi where the competition was hosted. I've also added a section in the notebook to get a score of 0.246643, which could be the 'unofficial' 1st place solution . ### About Expresso: Expresso is an African telecommunications company that provides customers with airtime and mobile data bundles. The objective of this challenge is to develop a machine learning model to predict the likelihood of each Expresso customer “churning,” i.e. becoming inactive and not making any transactions for 90 days #### My Approach * Handled Missing Values * Preprocessed Catgegorical variables * Clustering * Feature Creation * KFold Validation * Model Blending #### Improvements that can be made * Feature Selection * Handling missing data more efficiently * Hyper-parameter tuning #### Requirements - pip install requirements.txt ### Leaderboard Scores - Catboost - 0.2466929 - Xgboost - 0.2469854 - Xgboost and Catboost Blended - 0.246643 If you have any questions, comments or concerns, feel free to reach me on linkedin