◻  Data Preparation

◻  Use Python (Pandas, NumPy) to clean, preprocess, and visualize a real-world dataset.

◻  Handle missing data, outliers, and categorical features using techniques covered in Scikit-learn.

◻  Model Training

◻  Train a machine learning model (e.g., logistic regression, decision tree, or neural network) using Scikit-learn, TensorFlow, or PyTorch.

◻  Evaluate performance with metrics like accuracy, precision, recall, and F1-score.

◻  Deployment

◻  Deploy your trained model as a REST API using Flask or FastAPI.

◻  Write and test API endpoints for prediction.

◻  Version Control

◻  Host your project on GitHub, use branches for feature development, and document your workflow.

◻  Debugging and Testing

◻  Write unit tests for key functions and use debugging tools (e.g., pdb) to ensure code reliability.

◻  Algorithm Challenges

◻  Solve at least 5 medium-level LeetCode or HackerRank problems to demonstrate algorithmic thinking.

◻  Code Quality

◻  Refactor your project for readability and maintainability using OOP principles (e.g., creating classes for data preprocessing and model training).