snpnet - Efficient Lasso Solver for Large-scale genetic variant data

Logistic Regression technique in machine learning both theory and code in Python. Includes topics from Assumptions, Multi Class Classifications, Regularization (l1 and l2), Weight of Evidence and Information Value

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.

To know internal working of machine learning algorithms, I have implemented types of regression through scratch.

Deep reinforcement learning for smart calibration of radio telescopes. Automatic hyper-parameter tuning.

R code used for the analyses of the paper: Spatial conservation prioritisation in data-poor countries: a quantitative sensitivity analysis using different taxa

Algorithmes d’apprentissage et modèles statistiques: Un exemple de régression logistique régularisée et de validation croisée pour prédire le décrochage scolaire

My role in this group project was to perform regression analysis on quarterly financial data to predict a company's market capitalization. I used R to develop ordinary least squares (OLS), stepwise, ridge, lasso, relaxed lasso, and elastic net regression models. I first used stepwise and OLS regression to develop a model and examine its residual…

ML Project implementing ANN, SVM, Random Forest, Elastic Net regression models from scratch.

I created multiple models to predict the discharge volume of a 100 year flood on rivers in NY state. The discharge of 100 year flood events is dependent upon watershed drainage area, and elevation among other variables.

The project aims to enhance aircraft engine maintenance operations and planning using statistical learning and machine learning methods.

Data Models in R for Multiple Linear Regression and three models (Ridge, Lasso, and Elastic-Net), to predict Medicare claim costs of Type 2 diabetes patients with other diagnoses. We used Data from Entrepreneur’s Medicare Claims Synthetic Public Use Files (DE-SynPUFs) for our analysis.

This project focuses on forecasting the closing prices of Yes Bank's stock. Through data analysis and predictive modeling, this project provides valuable insights for investors and traders, aiding them in making informed decisions about their investments in Yes Bank's stock.

High Throughput Light Weight Regularized Regression Modeling for Molecular Data

Regression on BOSTON dataset from sklearn

A project aim to predict default rate of Commercial Real Estate(CRE) Loans

A demonstration of the basic Machine Learning Algorithms

The project will be focused on using regression to predict the "charges" target values of an insurance dataset based on different features. To make this possible we are going to make four different regression models, those being: Linear Regression, Lasso Regression, Ridge Regression and Elastic Net,.

This project focuses on forecasting the closing prices of Yes Bank's stock. Through data analysis and predictive modeling, this project provides valuable insights for investors and traders, aiding them in making informed decisions about their investments in Yes Bank's stock.