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

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.

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.