Title: Student Performance Analysis with Machine Learning
Description: This repository contains a Jupyter Notebook documenting an exploratory data analysis (EDA) and machine learning model for analyzing student performance data. The notebook includes comprehensive data visualization, statistical analysis, and the implementation of a linear regression model to predict math marks based on various features such as age, gender, hours studied, IQ, and others.
Contents:
- Jupyter Notebook:
studentdata.ipynb - Dataset:
student_extended_ml_dataset2.csv
Analysis Highlights:
- Exploration of trends, correlations, and insights using univariate, bivariate, and multivariate techniques.
- Implementation of a linear regression model to predict math marks.
- Evaluation of the model using mean squared error and R-squared score metrics.
Note: This analysis is part of a learning project aimed at demonstrating data analysis and machine learning techniques using Python and popular libraries such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn.