🟢 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
TotalChargesdatatype - 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
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