Student-Performance-Predictor

Student Performance Predictor

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Project Overview

Student Performance Predictor is intended as an educational project to demonstrate the end-to-end workflow of a supervised learning task:

• Data collection / cleaning
• Feature engineering
• Model training and hyperparameter tuning
• Evaluation and visualization

Use this repository to reproduce results, extend the model, or integrate additional data and features.

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Features

• Data preprocessing utilities
• Exploratory Data Analysis (EDA) notebooks
• Baseline models (e.g., logistic regression, decision tree, random forest)
• Model evaluation scripts and visualization
• (Optional) demo / app to try predictions interactively

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Tech Stack

• Language: Python 3.8+
• Data: pandas, numpy
• Modeling: scikit-learn
• Visualization: matplotlib, seaborn
• Notebooks: Jupyter
• (Optional) Web demo: Streamlit or Flask (if included)
• Dev tools: pip / virtualenv

Add or adjust versions in requirements.txt as needed.

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Repository Structure

This is a suggested/typical layout — adapt if actual repo differs:

• data/ - datasets (gitignored if large)
• notebooks/ - analysis & experiments (Jupyter notebooks)
• src/ - data processing and model training code
• models/ - saved model artifacts
• requirements.txt - Python dependencies
• README.md - this file

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Getting Started

Prerequisites

• Python 3.8 or newer
• git
• (Optional) virtualenv or conda

Clone the repository

Open a terminal and run:

git clone https://github.com/NihalDR/Student-Performance-Predictor.git
cd Student-Performance-Predictor

Install dependencies

Create and activate a virtual environment, then install:

# using venv
python -m venv .venv
# macOS / Linux
source .venv/bin/activate
# Windows (PowerShell)
.venv\Scripts\Activate.ps1

pip install --upgrade pip
pip install -r requirements.txt

If there is no requirements.txt, install the minimum tools:

pip install pandas numpy scikit-learn matplotlib seaborn jupyter

Run notebooks / scripts

Start Jupyter to view notebooks:

jupyter notebook
# or
jupyter lab

Run a training script (example):

python src/train.py --config config/train.yaml

If there is a demo app (Streamlit example):

streamlit run app.py
# or for Flask
python app.py

Adjust commands to match scripts present in the repository.

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Dataset

Place your dataset file(s) inside the data/ folder. Example expected location:

• data/students.csv

If using a public dataset (e.g., UCI Student Performance dataset), include a copy or a link in data/README.md. Ensure large datasets are not committed to git — prefer instructions to download or a script to fetch them.

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Training & Evaluation

1. Preprocess the data (scripts in src/preprocess.py or notebooks).
2. Train models (src/train.py) and save best models to models/.
3. Evaluate performance using cross-validation and holdout test set, and visualize metrics in notebooks/ or src/evaluate.py.

Common evaluation metrics:

• For regression: RMSE, MAE, R^2
• For classification: Accuracy, Precision, Recall, F1, ROC-AUC

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License

This project does not include a license by default. To make contributions and reuse clearer, add a LICENSE file (e.g., MIT License). Example:

MIT License
See the LICENSE file for details.

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Contact

For questions, issues, or feature requests, please open a GitHub Issue in this repository or contact the owner: @NihalDR.

Happy modeling!

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