Beginner Python ML Score Predictor

I’d like a complete, beginner-friendly machine-learning mini-project in Python that predicts student exam scores. The model should learn from several everyday factors—study hours and attendance, past test scores with homework results, plus broader study-habit indicators such as sleep patterns and class participation. What I need from you is a small but realistic workflow that someone new to AI/ML can clone, read, and run without frustration. Think of it as a guided tutorial packaged with production-worthy code quality. Core tech I expect you to use: • Python 3.x with pandas and NumPy for data wrangling • scikit-learn for the regression pipeline (LinearRegression, RandomForestRegressor or any comparable beginner-friendly algorithms) • Jupyter Notebook (or a .py script with CLI flags) so the user can walk through each step visually Optional but welcome: matplotlib or seaborn for quick exploratory plots, and joblib or pickle for saving the trained model. Please include: 1. Well-commented source code that clearly separates data preprocessing, feature engineering, model training, evaluation, and inference. 2. A concise README explaining project goals, environment setup (virtualenv or conda), and step-by-step commands to reproduce results from scratch. 3. Inline explanations in the notebook (or docstrings in .py files) so a learner understands why each block of code exists. 4. Suggestions on where to find or how to simulate a small sample dataset matching the above features, along with instructions to extend it to a larger real-world dataset later. Acceptance criteria • Running `pip install -r requirements.txt` followed by the documented commands trains the model and prints performance metrics (e.g., MAE, RMSE) on a held-out test set. • All code cells execute top-to-bottom without errors on a fresh machine. • Comments and markdown cells are clear enough for a first-time ML student to follow. If anything in the spec feels unclear, let’s discuss before you start coding so we can keep the learning path smooth.