🎓 Student Performance Prediction

A machine learning project to predict student academic performance and identify key factors influencing success, helping educational institutions provide proactive support.

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🚀 Project Overview

This project addresses the challenge of identifying at-risk students by building predictive models based on demographic, social, and academic data. The goal is to provide educational institutions with a data-driven tool for early intervention. The project involves two main tasks:

1. Regression: Predicting a student's final numeric grade.
2. Classification: Predicting whether a student will pass or fail.

✨ Key Features

• Data Exploration (EDA): In-depth analysis of student data to uncover initial trends and correlations.
• Data Preprocessing: A complete pipeline for cleaning data, encoding categorical variables, and scaling features.
• Dual-Task Modeling: Implements both regression and classification models to provide a comprehensive performance analysis.
• Performance Evaluation: Uses a wide range of metrics (R², RMSE, Accuracy, Precision, Recall, F1-Score) for robust model assessment.
• Feature Importance Analysis: Identifies the key drivers of academic success from the dataset.

⚙️ Methodology

The project follows a standard machine learning workflow:

1. Data Understanding: The UCI Student Performance Dataset was used. Initial analysis was performed to understand its structure, quality, and statistical properties.
2. Exploratory Data Analysis (EDA): Visualizations such as histograms and a correlation heatmap were created to identify relationships between variables, especially their impact on the final grade (G3).
3. Data Preprocessing:
   • A binary pass_fail feature was engineered from the G3 grade.
   • Categorical features were converted to a numerical format using one-hot encoding.
   • All features were scaled using StandardScaler to prepare the data for modeling.
4. Model Building:
   • Regression Task: A Multiple Linear Regression model was trained to predict the final grade.
   • Classification Task: Logistic Regression and Decision Tree models were trained to predict the pass/fail outcome.
5. Model Evaluation: The models were evaluated on an unseen test set (20% of the data) to measure their real-world performance.

📊 Results & Key Insights

The models demonstrated strong predictive capabilities:

Model / Task	Metric	Score
Linear Regression	R² Score	0.72
Classification Models	Accuracy	90%
(Logistic & Decision Tree)	F1-Score	0.92

Key Insight: The feature importance analysis revealed that a student's second-period grade (G2) is overwhelmingly the most significant predictor of their final academic outcome, accounting for over 70% of the decision-making power in the model. Other important factors include parental education (Medu), student absences, and social habits.

🔧 How to Run This Project

To run this project on your local machine, follow these steps:

1. Clone the repository:

   git clone [https://github.com/YourUsername/YourRepositoryName.git](https://github.com/YourUsername/YourRepositoryName.git)
   cd YourRepositoryName

2. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required libraries: Create a requirements.txt file with the following content:

   pandas
   numpy
   scikit-learn
   matplotlib
   seaborn
   jupyter
   

   Then, run the installation command:

   pip install -r requirements.txt

4. Download the Dataset:

   • Download the student-mat.csv file from the UCI repository.
   • Place the student-mat.csv file in the root directory of the project.

5. Launch Jupyter Notebook:

   jupyter notebook

   Open the .ipynb notebook file and run the cells.

🛠️ Technologies Used

Technology	Description
Python	Core programming language for the project.
Pandas	Data manipulation and analysis library.
NumPy	For numerical operations and array handling.
Scikit-learn	For building and evaluating machine learning models.
Matplotlib & Seaborn	For data visualization and creating plots.
Jupyter Notebook / Colab	For interactive development and documentation.

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