supportvectorregression

This project demonstrates the use of machine learning models to estimate a pore pressure proxy using geological well log data. Based on the Well Log Facies Dataset, the project simulates pore pressure from petrophysical features like GR, ILD_log10, PE, DeltaPHI, PHIND, NM_M, and RELPOS. Multiple regression models—including Linear Regression, Ridge

BIG MART SALES PREDICTION USING MACHINE LEARNING

This project applies Support Vector Regression (SVR) to predict restaurant total bills using features from the Tips dataset. It includes data preprocessing, model training, and hyperparameter tuning to improve prediction accuracy.

A lightweight system designed to estimate and predict carbon emissions using simple, modular components. This project is built for clarity and reproducibility — perfect for learning, prototyping, or extending into production.

This repository contains various regression models built using Python, including Linear Regression, Polynomial Regression, and Multiple Linear Regression. Each model is trained and evaluated on real-world datasets with clear visualizations using Matplotlib and Seaborn.