Machine Learning - Regression
Predict Student Performance
An introductory project to supervised machine learning focusing on a classic regression problem. Using a student performance dataset from Kaggle, you'll perform data cleaning, exploratory data analysis (EDA), and basic feature engineering. The goal is to train a linear regression model to predict students' final grades based on factors like study time, past failures, and demographic information, and then evaluate its performance with standard metrics.
Technologies Used
🐍Python
🔧 Scikit-learn
🔧 Pandas
🔧 Matplotlib
🔧 Seaborn
🔧 Jupyter Notebook
Project Info
CategoryMachine Learning - Regression
Technologies6
Features6
Key Features
Download and explore a real-world dataset from Kaggle.
Perform EDA using Pandas and Seaborn to uncover correlations between features and student grades.
Handle categorical features using one-hot encoding to prepare data for modeling.
Split the dataset into training and testing sets to prevent model overfitting.
Code Implementation
Data Exploration with Pandas