Car Price Prediction : Predictions made by using linear regression aaproach

[ICML 2024] Code repository for "TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression". We address the problem of sub-optimal covariance estimation in deep heteroscedastic regression by proposing a new model and metric.

OLS Bootstrap on Cross-Sectional Data

GWAS of trait variance (C++)

Supervised-ML---Multiple-Linear-Regression---Toyota-Cars. EDA, Correlation Analysis, Model Building, Model Testing, Model Validation Techniques, Collinearity Problem Check, Residual Analysis, Model Deletion Diagnostics (checking Outliers or Influencers) Two Techniques : 1. Cook's Distance & 2. Leverage value, Improving the Model, Model - Re-buil…

Assignment-05-Multiple-Linear-Regression-2. Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. R&D Spend -- Research and devolop spend in the past few years Administration -- spend on administration in the past few y…

This project is about to use linear regression to examine the relationship between various economic variables and the mortgage rate in the United States.

R package to perform regression-based Brown-Forsythe test

Traditional Regression problem project in Python

Supervised-ML---Multiple-Linear-Regression---Cars-dataset. Model MPG of a car based on other variables. EDA, Correlation Analysis, Model Building, Model Testing, Model Validation Techniques, Collinearity Problem Check, Residual Analysis, Model Deletion Diagnostics (checking Outliers or Influencers) Two Techniques : 1. Cook's Distance & 2. Levera…

This repo provides supplemental material for the article titled: "Assessing Potential Heteroscedasticity in Psychological Data: A GAMLSS approach"

Various models and techniques to show how to handle heteroscedastic data

Ordinary least square (OLS) regression analysis carried out in this project. The selected dependent variables are some public health indicators like anxiety, diabetes. We tried to find the independent variables which are responsible for this health hazard.

time series analysis in R use cases

This is an linear approach machine learning model used to predict the values of variable(dependent) based on other variables(independent).

Usual linear regression or XGBoost? Combo! Or how I was investigating the impact of intellectual capital on NASDAQ-100 capitalization during 2 years.

This R package allows calibration parameter estimation for inexact computer models with heteroscedastic errors proposed by Sung, Barber, and Walker (2022) in SIAM/ASA Journal on Uncertainty Quantification.

Used libraries and functions as follows:

Estimation of the Hubble constant using Gaussian process regression and viable alternatives