This repository contains a machine learning project that predicts a professional's salary based on their years of experience using Polynomial Regression with Ridge Regularization. The project follows a full ML workflow from data preprocessing to model deployment.

A Machine Learning project to predict bike-sharing counts using regression models, including linear, polynomial, and regularized techniques

Vegetation classification using ML: GAMs, SVM, PCA, Random Forests & Q-Learning-based feature selection.

Your all-in-one Machine Learning resource – from scratch implementations to ensemble learning and real-world model tuning. This repository is a complete collection of 25+ essential ML algorithms written in clean, beginner-friendly Jupyter Notebooks. Each algorithm is explained with intuitive theory, visualizations, and hands-on implementation.

This repository contains some of my relevant academics projects in applied mathematics

Explore various regression models including univariate and multivariate linear regression, along with regularization techniques such as Ridge Regression and Lasso Regression. This repository contains Jupyter Notebook files (.ipynb) demonstrating the implementation and usage of different regression models. Additionally, datasets used for training an

Logistic Regression with Ridge and Lasso

Based on data of 20 Walmart stores, predict weekly sales : Deal with missing values and train a linear regression model, avoiding overfitting