ChurnShield: An AI-Based Model for Predicting Customer Churn and Recommending Retention Strategies

Problem: Businesses lose revenue due to customer churn but lack predictive mechanisms to retain them.

Outcome: Develop an AI-based model that predicts churn probability and suggests retention strategies.

Week 1-2: Data Collection & Preprocessing

Gather customer transaction & interaction data.

Clean and preprocess data for model training.

Week 3-4: Exploratory Data Analysis (EDA)

Perform statistical analysis and feature engineering.

Identify key factors influencing churn.

Week 5-6: Model Selection & Training

Train multiple machine learning models (Logistic Regression, Random Forest, XGBoost).

Tune hyperparameters for optimization.

Week 7-8: Model Evaluation & Optimization

Compare models using accuracy, precision, recall, and F1-score.

Implement feature selection to improve model efficiency.

Week 9-10: Dashboard Development & Visualization

Create real-time visualization of churn insights.

Develop a dashboard for decision-makers.

Week 11-12: Report & Deployment Strategy

Document findings and model performance.

Deploy model using Flask/Django API.