Capstone Projects — Build Your ML Portfolio

Apply everything you've learned in end-to-end projects that showcase your skills.

Project Ideas

Beginner:
├─ Titanic survival prediction (classification)
├─ House price prediction (regression)
├─ Iris flower classification
└─ MNIST digit recognition

Intermediate:
├─ Sentiment analysis on reviews
├─ Customer churn prediction
├─ Credit fraud detection
├─ Image classification (CIFAR-10)
└─ Movie recommendation system

Advanced:
├─ Object detection (YOLO)
├─ Text generation (GPT fine-tuning)
├─ Time series forecasting
├─ Speech recognition
├─ Medical image analysis
└─ Autonomous driving (lane detection)

Project Structure

project/
├── data/
│   ├── raw/
│   ├── processed/
│   └── README.md
├── notebooks/
│   ├── 01-EDA.ipynb
│   ├── 02-preprocessing.ipynb
│   └── 03-modeling.ipynb
├── src/
│   ├── data.py
│   ├── features.py
│   ├── model.py
│   └── evaluate.py
├── models/
├── reports/
├── Dockerfile
├── requirements.txt
└── README.md

Key Takeaways

1. Projects demonstrate practical skills to employers
2. End-to-end projects are most impressive
3. Document your thought process, not just code
4. Deploy your models — it shows production skills
5. Use real datasets from Kaggle, UCI, or government
6. Clean code matters as much as good models
7. Version control (Git) is essential
8. Present results clearly with visualizations