Student Performance Prediction using Machine Learning (SVR Model)

A comprehensive data science project aimed at predicting student academic outcomes with high precision.

Key Contributions:

Data Preprocessing:

Performed extensive data cleaning, handling missing values, and feature engineering using Python (Pandas & NumPy).

Predictive Modeling:

Developed and fine-tuned a Support Vector Regression (SVR) model to achieve an impressive accuracy of 97.5%.

Data Insights: Analyzed key factors influencing student success to provide actionable insights.

Tools Used: Python, and Jupyter Notebook.