Churn Prediction Model Project

Project Overview

This project aims to develop a churn prediction model to assist in identifying customers who are likely to churn from a subscription service. By utilizing a combination of data preprocessing, feature engineering, model building, optimization, and deployment techniques, this project provides actionable insights for customer retention.

Project Structure

• data/: Contains the dataset 'churn_dataset.xlsx'.
• notebooks/: Includes the Jupyter Notebook 'Churn_Prediction_Model.ipynb' detailing code implementation.
• app/: Holds the Flask web application for model deployment.
  • app.py: Flask app code for serving the model through an API.
  • trained_model.pkl: Serialized trained model file.
  • scaler.pkl: Serialized StandardScaler file for preprocessing.

Project Steps

1. Data Preprocessing:

   • Loaded the dataset and conducted initial exploratory analysis.
   • Managed missing data using imputation techniques and addressed potential outliers.
   • Employed one-hot encoding for categorical variables and performed a train-test split.

2. Feature Engineering:

   • Introduced a new feature 'Usage_Bill_Ratio' to capture usage-bill relationship.
   • Utilized StandardScaler to normalize numerical features for optimal model performance.

3. Model Building:

   • Explored different machine learning algorithms, including Logistic Regression and Random Forest Classifier.
   • Trained and validated models, leveraging cross-validation techniques.
   • Evaluated model performance metrics including accuracy, precision, recall, and F1-score.

4. Model Optimization:

   • Employed GridSearchCV to fine-tune hyperparameters of the Random Forest Classifier.
   • Utilized cross-validation to ensure robustness of hyperparameter choices.

5. Model Deployment:

   • Created a user-friendly Flask web application to serve the model via API.
   • Tested the API using sample data to demonstrate real-time predictions.

Project Deliverables

• Churn_Prediction_Model.ipynb: Detailed Jupyter Notebook with comprehensive code and explanations.
• Approach_Report.pdf: In-depth report summarizing the methodology, decisions, and key insights.
• Model_Performance.png: Visualization depicting model performance metrics.
• README.md: This file, offering an overview of the project, its structure, and steps.

Conclusion

This project underscores a holistic data science approach, showcasing proficiency in data preprocessing, feature engineering, model selection, optimization, and web application development. It stands as a testament to the application of machine learning in solving real-world business challenges.