Student Performance Prediction

This project predicts student performance using a Jupyter Notebook and evaluates model accuracy with the R² score metric.

Project Overview

The main goal of this project is to analyze various factors that affect student performance and develop a predictive model. This model will help in understanding the key determinants of student outcomes and potentially support educational improvements.

Table of Contents

• Project Overview
• Dataset
• Features
• Installation
• Usage
• Results

Dataset

The dataset used in this project includes various features that are presumed to influence student performance, such as:

• Study hours
• Attendance rate
• Parental education
• Socioeconomic status

Note: Ensure that the dataset is available and preprocessed appropriately before running the notebook.

Features

• Data Cleaning and Preprocessing: Handles missing data and prepares features for modeling.
• Exploratory Data Analysis (EDA): Insights and visualizations to understand relationships.
• Modeling: Uses regression techniques to predict student performance.
• Evaluation: Evaluates model accuracy using the R² score.

Installation

1. Clone this repository:

   git clone https://github.com/your-username/student-performance-prediction.git

2. Navigate to the project directory:

cd student-performance-prediction

3. Install the Required Lib

pip install -r requirements.txt

Usage

Open the Jupyter Notebook:

jupyter notebook StudentsPerformance.ipynb Run each cell in the notebook to preprocess data, build models, and evaluate the results.

Results

The model's accuracy is measured using the R² score, which indicates how well the predicted values approximate the actual data points. The results and findings from the predictions are visualized in the notebook, helping to analyze the model’s performance.