cryptopulgo / customer-churn-prediction-with-model-stacking

The goal of this project is to predict customer churn (whether a customer will leave the telecom service) using a model stacking approach. Model stacking involves training multiple models and combining their predictions using another model.

Enhancing Customer Churn Prediction with Continuous Experiment Tracking in Machine Learning

Introduction

Welcome to the GitHub repository for the project Enhancing Customer Churn Prediction with Continuous Experiment Tracking in Machine Learning. This hands-on project focuses on predicting customer churn in a telecom service using a model stacking approach. We aim to provide you with insights into dealing with classification problems, handling imbalanced datasets, and utilizing model stacking to enhance predictive performance.

Dataset

You can use the Telco Customer Churn dataset from Kaggle. This dataset contains information about telecom customers, including various features like contract type, monthly charges, and whether the customer churned or not.

Objective

The primary goal of this project is to predict customer churn, i.e., whether a customer will leave the telecom service or not. We achieve this by employing a model stacking approach, which involves training multiple machine learning models and combining their predictions using another model.

Project Steps

Follow these steps to understand and reproduce the project:

1. Import Libraries: Import necessary libraries and initialize Comet ML.
2. Load and Explore Data: Load dataset and perform exploratory data analysis (EDA).
3. Preprocessing: Preprocess data by encoding and scaling features.
4. Model Training: Train multiple machine learning models, including Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine.
5. Hyperparameter Tuning: Use Optuna to optimize hyperparameters for the models.
6. Ensemble Modeling: Create a stacking ensemble of models for improved predictions.
7. Optimization Results: Display the best hyperparameters and accuracy.
8. End Experiment: Conclude the Comet ML experiment.

Detail explanation and interpret the results into business insights in the blog.

This project will give you insights into dealing with classification problems, handling imbalanced datasets (if applicable), and utilizing model stacking to enhance predictive performance.

References

• GitHub Repository
• Kaggle Project
• Medium Article

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Thank you for your interest in this project, and I hope you find it informative and valuable for your machine learning journey.