🟢 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)

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

• Load dataset
• Rows & columns
• Identify target variable
• Numerical vs categorical features
• High-level observations

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✅ STEP 3: Data Quality Checks (COMPLETED)

• Missing values check
• Data types check
• Identify hidden data issues

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✅ STEP 4: Data Cleaning (COMPLETED)

• Fix TotalCharges datatype
• Handle hidden missing values logically
• Validate clean dataset

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🟡 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

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⏳ STEP 6: Feature Engineering

• Drop identifier (customerID)
• Encode categorical variables
• Scale numerical features
• Prepare final modeling dataset

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⏳ STEP 7: Train-Test Split

• Stratified split
• Explain why stratification matters

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⏳ STEP 8: Baseline Model

• Logistic Regression

Evaluate:

• Accuracy
• Precision
• Recall
• F1-score

Explain results in business terms

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⏳ STEP 9: Advanced Model

• Random Forest / XGBoost
• Compare with baseline
• Select final model

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⏳ STEP 10: Model Interpretation

• Feature importance
• Understand churn drivers
• Explain why customers churn

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⏳ 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.

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🟡 PHASE 2: ENGINEERING & PRODUCTION (LATER)

⏳ STEP 12: Refactor Project Structure

• Convert notebook logic to Python scripts
• Clean project layout

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⏳ STEP 13: Build Prediction API

• FastAPI
• Input validation
• Model inference endpoint

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⏳ STEP 14: Dockerization

• Write Dockerfile
• Build Docker image
• Run container locally

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⏳ STEP 15: Cloud Deployment

• Deploy to AWS (EC2 / ECS)
• Public endpoint
• Test with sample requests

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⏳ STEP 16: Monitoring & Future Enhancements

• Model drift discussion
• Retraining ideas
• Monitoring metrics

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🔵 PHASE 3: PORTFOLIO & CAREER

⏳ STEP 17: README & Documentation

• Problem statement
• EDA insights
• Model performance
• Business impact
• Architecture diagram

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⏳ STEP 18: Resume & Interview Prep

• Convert project into resume bullets
• Prepare interview explanations
• STAR method answers