There are 2 repositories under regularized-linear-regression topic.
Lasso/Elastic Net linear and generalized linear models
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.
Andrew Ng's Machine Learning Course
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.
Housing price prediction using Regularised linear regression
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
Here, we implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. In the next half, we go through some diagnostics of debugging learning algorithms and examine the effects of bias v.s. variance.
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.
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 Mathematical Intuition behind Linear Regression Algorithm
This repository contains several machine learning projects done in Jupyter Notebooks
Solutions to Coursera's Intro to Machine Learning course in python
Course work for Machine Learning Course by Stanford University on Coursera
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.
Competition: https://www.kaggle.com/c/house-prices-advanced-regression-techniques
High dimensional linear regression with missing via adaptive SLOPE
Performed rigorous preprocessing, and data cleaning, and conducted exploratory data analysis to identify trends, patterns, and outliers, leading to valuable insights. Employed various statistical methods concepts to get insights about the data for prediction.
Jupyter notebooks implementing Machine Learning algorithms in Scikit-learn and Python
The course studies fundamentals of distributed machine learning algorithms and the fundamentals of deep learning. We will cover the basics of machine learning and introduce techniques and systems that enable machine learning algorithms to be efficiently parallelized.
SparseStep: Approximating the Counting Norm for Sparse Regularization
High Throughput Light Weight Regularized Regression Modeling for Molecular Data
linear regression
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.
Build a regularized regression model to predict the price of houses with the available independent variables
In linear regression, regularization is a process of making the model more regular or simpler by shrinking the model coefficient to be closer to zero or absolute, ultimately to address over fitting.
Advanced regression model to predict housing price for an use case in Australia.
Gradient descent algorithm from scratch for linear and logistic regression with feature scaling and regularization.
Regularization, PCA and Linear Regression are implemented.
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.
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
A tool for visualizing the coefficients of various regression models, taking into account empirical data distributions.
This repository is dedicated to my participation in Datatalks Mlzoomcamp
Analysis pipeline for modeling of illness perception one year after COVID-19
Deep Study of the Linear Regression Machine Learning Model, including its Regularization Techniques