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.

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

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

Results from empirical assessment of LASSO CV, ElasicNetCV, and RidgeCV from scikit-learn

Predicte weekly sales for 20 Walmart stores using historical data. : Deal with missing values and train a linear regression model, avoiding overfitting

Logistic Regression with Ridge and Lasso

A comprehensive data science and ML project portfolio - featuring six end-to-end projects. Showcases expertise in unsupervised clustering, supervised classification and regression, deep learning architectures, advanced feature selection, and full-stack web application deployment across healthcare, finance, bioinformatics, and real estate domains.

Regularised regression (Ridge & Lasso) to predict Australian residential property prices for Surprise Housing's market entry strategy, identifying 12 key value drivers.

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

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.

Kernels & Regularization

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