Student Performance Prediction Model

Project Overview

This repository contains an end-to-end project focused on developing a predictive model to estimate student performance. The project includes comprehensive Exploratory Data Analysis (EDA) and the application of several machine learning techniques.

Key Achievements

• Predictive Modeling: Developed a model to predict student performance, demonstrating a robust understanding of predictive modeling techniques.
• Exploratory Data Analysis: Performed extensive EDA using matplotlib and seaborn to uncover key insights that informed the development of the predictive models.
• Machine Learning Implementation: Applied multiple machine learning techniques and achieved the highest R² score of 0.88 using Ridge Regression.

Technologies Used

• Python
• Libraries:
  • Data Analysis & Visualization: pandas, numpy, matplotlib, seaborn
  • Machine Learning: scikit-learn

Getting Started

To get a local copy up and running follow these simple steps.

Prerequisites

• Install Python
• Install pip package manager