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CHECKLIST
Machine Learning Project Challenge: Comprehensive
Supervised Learning Pipeline
Project Overview: Customer Churn Prediction System
You'll build a system that predicts customer churn for a subscription-based service, covering the entire
ML lifecycle from data exploration to production deployment.
Phase 1: Data Exploration and Preprocessing
Download the Telco Customer Churn dataset
Perform exploratory data analysis (EDA)
Analyze distribution of target variable
Examine feature distributions
Identify correlations between features
Visualize key relationships
Handle missing values appropriately
Convert categorical variables using encoding techniques
Normalize/standardize numerical features
Create domain-specific features (feature engineering)
Phase 2: Supervised Learning Implementation
Split data into training, validation, and test sets
Implement and compare multiple algorithms:
Linear Models (Logistic Regression)
Decision Trees
Random Forest
Gradient Boosting (XGBoost or LightGBM)
Support Vector Machines
Neural Networks (simple MLP)
Address class imbalance using:
Resampling techniques (undersampling/oversampling)
SMOTE or ADASYN
Class weights
Implement cross-validation
Perform hyperparameter tuning using:
Grid search
Random search
Bayesian optimization
Phase 3: Model Evaluation and Selection
Evaluate models using multiple metrics:
Accuracy, Precision, Recall, F1-score
ROC-AUC and PR-AUC
Log loss
Business-specific metrics (e.g., cost of misclassification)
Analyze learning curves to identify overfitting/underfitting
Implement feature importance analysis
Create a model selection pipeline based on evaluation metrics
Document model comparison results
Phase 4: Model Productionization
Create a scikit-learn pipeline incorporating:
Preprocessing steps
Feature selection
The best performing model
Serialize the model using joblib or pickle
Write unit tests for the prediction pipeline
Implement monitoring for model drift detection
Document the productionization process
Phase 5: Backend Development (Django)
Set up a Django project structure
Create a REST API for model predictions
Implement user authentication
Design database models for:
User data
Prediction history
Model metadata
Implement logging and error handling
Create an admin panel for monitoring
Phase 6: Frontend Development
Design a responsive UI using HTML/CSS/JavaScript
Implement forms for data input
Create visualizations for prediction results
Build a dashboard for historical predictions
Ensure cross-browser compatibility
Phase 7: Deployment
Containerize application using Docker
Set up a CI/CD pipeline using GitHub Actions
Deploy to a cloud provider (AWS, GCP, or Azure)
Configure monitoring and alerting
Write comprehensive deployment documentation
Phase 8: Documentation and Presentation
Document the entire process in a comprehensive README
Create technical documentation for the API
Write a user guide for the application
Prepare a presentation highlighting:
Business problem and solution approach
Model selection process and results
System architecture
Deployment strategy
Future improvements
Record a demo video for LinkedIn
Bonus Challenges
Implement A/B testing capabilities
Add explainability tools (SHAP, LIME)
Implement model retraining capabilities
Create a batch prediction system
Add data versioning and model versioning
This challenge covers the entire supervised learning workflow while creating a practical application you
can showcase. It balances theoretical machine learning concepts with practical engineering skills that
employers value.
