Car Price Prediction : Predictions made by using linear regression aaproach
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Updated
Jan 10, 2021 - Jupyter Notebook
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.
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…
OLS Bootstrap on Cross-Sectional Data
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…
GWAS of trait variance (C++)
This project is about to use linear regression to examine the relationship between various economic variables and the mortgage rate in the United States.
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…
R package to perform regression-based Brown-Forsythe test
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).
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.
A real estate company that has a dataset containing the prices of properties in the Delhi region. It wishes to use the data to optimise the sale prices of the properties based on important factors such as area, bedrooms, parking, etc
Age-Gender-Country-Specific Death Rates Modelling and Forecasting: A Linear Mixed-Effects Model
Used libraries and functions as follows:
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