Choosing the Right Projects

The 3-Project Portfolio Strategy

Project 1: End-to-End ML Pipeline Shows you can build production-ready systems

Example: Customer churn prediction API

• Data cleaning & preprocessing
• Model training (XGBoost, Random Forest)
• FastAPI deployment with Docker
• Monitoring dashboard

Project 2: Deep Learning / LLM Project Shows you understand modern AI

Example: Fine-tuned LLM for domain-specific Q&A

• Dataset creation or curation
• Fine-tuning with LoRA/QLoRA
• RAG system integration
• Evaluation metrics (RAGAS, perplexity)

Project 3: Real-World Problem Solver Shows business impact thinking

Example: Recommendation system or fraud detection

• Use real datasets (Kaggle, UCI ML Repository)
• Document business metrics improved
• A/B testing simulation

Projects to AVOID

❌ MNIST/Iris Dataset - Too basic, everyone does it ❌ Tutorial Copy-Paste - Recruiters can tell ❌ Overly Complex - Finish 3 good projects vs 1 perfect project ❌ No Code Explanation - Must have clear README

What Makes a Portfolio Project Strong?

✅ Clear README

• Problem statement
• Dataset description
• Model architecture diagram
• Results with metrics
• How to run the code

✅ Clean Code

• Organized folder structure
• Functions with docstrings
• requirements.txt or pyproject.toml
• Type hints in Python

✅ Deployed Demo (Bonus)

• Streamlit/Gradio app
• Hosted on HuggingFace Spaces or Render
• Live API endpoint
• Screenshot/GIF in README

✅ Business Metrics

• "Improved accuracy by 15%"
• "Reduced prediction latency to <100ms"
• "Increased user retention by 8%"

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