Beginner Python ML Score Predictor
Budget / Salary₹12,500–37,500
TypeFreelance project
LocationRemote
Posted1 hour ago
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.
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.
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