A linear regression implementation

Trying to solve heteroscedasticity using transformations, and differents models.

LGES-WBS-Template-Duration-Analysis

Multiple-Linear-Regression-1. Consider only the below columns and prepare a prediction model for predicting Price of Toyota Corolla.p

Methods for dimension reduction in regression based on a Nearest Neighbor estimation of gradients per point.

Penn ESE 305 Final Project using Machine Learning Techniques

Analysis of heatstroke Deaths in 1881, using modern statistics

You can find a fictive business case for a linear regression

Using OLS regression (and Ridge and Lasso to compare), we worked on a project that uses a dataset to predict housing prices based on user inputs on house details.

An OLS model that predicts golf scoring average improvement using PGA tour data.

OLS for an Inflation-Targeting Policy Analysis (Chile)

This project is a hands-on exploration of multiple regression modeling techniques applied to real-world challenges in the context of real estate. It aims to understand how different features influence house or property prices. By utilizing multiple regression models, the project provides insights, decision support, and enhances data science skills.

OLS results and Linear Regression in R

Just a place to play around with stats

Project for the Machine Learning II 2022/23 class at the Faculty of Economic Sciences, University of Warsaw. I was responsible for the housing price prediction task for which I build OLS, random forests, XGBoost and neural networks regression models.

Apex team`s multiple regression project. It contains: What is Multiple Regression? Advantages and disadvantages of multiple regression, least square method and real implementation.

This case study is for the analysis of viewers of a particular show, so that more viewers can be indulged in the show.

Used libraries and functions as follows:

Print-friendly, minimalist CV page

✈️ Flight delay EDA & estimation on a 12GB dataset using a custom C++ OLS estimator.