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.
