Lasso/Elastic Net linear and generalized linear models

Andrew Ng's Machine Learning Course

Housing price prediction using Regularised linear regression

Solutions to Coursera's Intro to Machine Learning course in python

This repository corresponds to the course "Statistical Learning Theory" taught at the School of Mathematics and Statistics (FME), UPC under the MESIO-UPC-UB Joint Interuniversity Master's Program under the instructor Pedro Delicado

Sequential adaptive elastic net (SAEN) approach, complex-valued LARS solver for weighted Lasso/elastic-net problems, and sparsity (or model) order detection with an application to single-snapshot source localization.

Predict the vehicle price from the open source Auto data set using linear regression. In this data set, we have prices for 205 automobiles, along with other features such as fuel type, engine type,engine size,etc.

A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them on at a higher price.

Regularized logistic regressions with computational graphs

SparseStep: Approximating the Counting Norm for Sparse Regularization

This is the implementation of the five regression methods Least Square (LS), Regularized Least Square (RLS), LASSO, Robust Regression (RR) and Bayesian Regression (BR).

A Machine Learning project about a regression problem for the prediction of Taxi-out time in flights, using 9 different ML models, with different algorithms and data-scaling.

Implementation of various Machine Learning (ML) Algorithms learned in the Machine Learning course authorised by Stanford University @ Coursera

Explanations and Python implementations of Ordinary Least Squares regression, Ridge regression, Lasso regression (solved via Coordinate Descent), and Elastic Net regression (also solved via Coordinate Descent) applied to assess wine quality given numerous numerical features. Additional data analysis and visualization in Python is included.

I developed a function to perform regularized linear and Gaussian basis functions for regression. Some dataset from the UCI machine learning repository were used to validate the function.

This repository contains several machine learning projects done in Jupyter Notebooks

Build a regularized regression model to predict the price of houses with the available independent variables

Built a regression model to predict bike demand on data from Seoul, South Korea. and employed one hot encoding to create dummy variables Benchmarked Cat Boost against Linear regression, Lasso and Ridge regression, Gradient Boost and performed feature engineering and tuned the hyperparameters for the optimum performance

Machine Learning by Stanford University at Coursera

Implementation of Machine Learning algorithms using MatLab.