📘 CUSTOMER CHURN PROJECT — MASTER STEP LIST

🟢 PHASE 1: DATA SCIENCE CORE (CURRENT FOCUS)

✅ STEP 1: Business Understanding (COMPLETED)

• What is churn?


• Why churn matters to business


• Business objective


• Success metric (Recall > Precision)

✅ STEP 2: Load Data & Initial Understanding (COMPLETED)

• Load dataset


• Rows & columns


• Identify target variable


• Numerical vs categorical features


• High-level observations

✅ STEP 3: Data Quality Checks (COMPLETED)

• Missing values check


• Data types check


• Identify hidden data issues

✅ STEP 4: Data Cleaning (COMPLETED)

• Fix TotalCharges datatype


• Handle hidden missing values logically


• Validate clean dataset

🟡 STEP 5: Exploratory Data Analysis (EDA) (IN PROGRESS)

We will do EDA step by step:

• Churn distribution


• Churn vs tenure


• Churn vs contract type


• Churn vs monthly charges


• Correlation analysis


• Write business insights for each plot

📌 This is the most important DS phase

⏳ STEP 6: Feature Engineering

• Drop identifier (customerID)


• Encode categorical variables


• Scale numerical features


• Prepare final modeling dataset

⏳ STEP 7: Train-Test Split

• Stratified split


• Explain why stratification matters

⏳ STEP 8: Baseline Model

• Logistic Regression


• 
  

  Evaluate:

  
  
  
  
  
  • Accuracy
  
  
  • Precision
  
  
  • Recall
  
  
  • F1-score
  
  
  
  
  
  




• Explain results in business terms

⏳ STEP 9: Advanced Model

• Random Forest / XGBoost


• Compare with baseline


• Select final model

⏳ STEP 10: Model Interpretation

• Feature importance


• Understand churn drivers


• Explain why customers churn

⏳ STEP 11: Business Recommendations

• Who to target?


• What actions to take?


• How this model helps reduce churn?

📌 This step makes you a Data Scientist, not just a coder.

🟡 PHASE 2: ENGINEERING & PRODUCTION (LATER)

⏳ STEP 12: Refactor Project Structure

• Convert notebook logic to Python scripts


• Clean project layout

⏳ STEP 13: Build Prediction API

• FastAPI


• Input validation


• Model inference endpoint

⏳ STEP 14: Dockerization

• Write Dockerfile


• Build Docker image


• Run container locally

⏳ STEP 15: Cloud Deployment

• Deploy to AWS (EC2 / ECS)


• Public endpoint


• Test with sample requests

⏳ STEP 16: Monitoring & Future Enhancements

• Model drift discussion


• Retraining ideas


• Monitoring metrics

🔵 PHASE 3: PORTFOLIO & CAREER

⏳ STEP 17: README & Documentation

• Problem statement


• EDA insights


• Model performance


• Business impact


• Architecture diagram

⏳ STEP 18: Resume & Interview Prep

• Convert project into resume bullets


• Prepare interview explanations


• STAR method answers