rivas-lab/snpnet

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

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wyattowalsh/regularized-linear-regression-deep-dive

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.

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SarodYatawatta/smart-calibration

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

Language: Python - Size: 5.29 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 6 - Forks: 0

sandipanpaul21/Logistic-regression-in-python

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

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Labo-Lacourse/Code_chap_23_logistic_regression_regularization

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

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elgabbas/Conservation-Prioritisation-Sensitivity

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

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tboudart/Financial-Markets-Regression-Analysis

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 plots. The plot displaying the residuals against the predicted values indicated multiplicative errors. I, therefore, took the natural log transformation of the dependent variable. The resulting model's R2 was significantly, negatively impacted. After examining scatter plots between the log transformation of market capitalization and the independent variables, I discovered the independent variables also had to be transformed to produce a linear relationship. Using the log transformation of both the dependent and independent variables, I developed models using all the regression techniques mentioned to strike a balance between R2 and producing a parsimonious model. All the models produced similar results, with an R2 of around .80. Since OLS is easiest to explain, had similar residual plots, and the highest R2 of all the models, it was the best model developed.

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micahwiesner67/NY_100YR_Flood_Prediction

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.

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PiotrTymoszuk/htGLMNET

High Throughput Light Weight Regularized Regression Modeling for Molecular Data

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nikhilbordekar/Yes-Bank-s-Stock-Closing-Price-Prediction-by-Regression

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.

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belohith/ml-algorithms

A demonstration of the basic Machine Learning Algorithms

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Bhevendra/ML-Regression

Regression on BOSTON dataset from sklearn

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charumakhijani/advanced-house-price-prediction

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julian0112/Insurance-ML-Regression-Models

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,.

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shussin245/GeneExpressionAnalysis

A tutorial demonstrating how to analyze gene expression data using elastic net models to predict patient responses to immunotherapy, focusing on regularization, cross-validation, and feature importance.

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Khushi130404/Regulexa

Regulexa is a Python project that showcases and compares Ridge, Lasso, and Elastic-Net regularization techniques in machine learning. It includes visualizations and performance insights to help prevent overfitting and improve model generalization.

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vn33/Linear-Regression-Polynomial-Regression-Regularization-Assumptions

In this project, we implement a linear regression model and its extensions on a student grades dataset to enhance performance. The workflow includes advanced EDA, data preprocessing, and assumption checks. Key steps: dataset overview, univariate and bivariate analysis, data preprocessing, model building(2nd degree,l1,l2,EN) and result visualization

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AmbreenMahhoor/What-is-Elastic-Net-Regression

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PayThePizzo/HouseSalesPricePrediction

Prediction of Sales Prices of Houses

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vaibhavdangar09/YES_BANK_STOCK_CLOSING_PRICE

Yes-Bank-Stock-Closing-Price-Prediction refers to a type of project or task in the field of data science and machine learning that involves developing predictive models to estimate the Closing Price of stock

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HaHaIamHarry/Commercial-Real-Estate-CRE-Loan-Credit-Risk-Model

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

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liang-sarah/F1_ML

Predicting 2023 Formula One Constructors' Championship Standings

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iremustek/Predictive-Maintenance-Analysis

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

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Nandan9911/Problems-on-car-and-house-price-prediction

Regression analysis

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pcastellanoescuder/lassoloops

Lasso + Bootstrap methods for predictive modeling

Language: R - Size: 158 KB - Last synced at: 8 months ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0

fau-masters-collected-works-cgarbin/regression-no-libraries

Ridge, elastic net, and logistic regressions implemented without using any statistical or machine learning library. All steps are done by hand, using matrix operations as much as possible.

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devosmitachatterjee2018/Statistical_Learning_for_Big_Data_Report12062020

The project encompasses the statistical analysis of a high-dimensional data using different classification, feature selection, clustering and dimension reduction techniques.

Language: Python - Size: 5.11 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0

paddywardle/Health-Insurance-Regression---Python

Various Regression models including linear, polynomial, ridge, lasso and elastic net were experimented with to find which model best predicted health insurance costs. The models were evaluated using cross-validation, from which the best models were optimized using randomized search. The best model was then evaluated on the test data.

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sdixit5/Walmart-Weekly-Sales-Prediction

This project compares the different machine learning models on Walmart Weekly Sales Data and predicts the weekly sales for the test data.

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electrofocus/machine-learning-practicum 📦

Machine Learning Basics

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