A list of python notebooks for Machine learning basics- regression and classification.

Regressions

Simple Multiple Regression problem to predict vehicle price based on various KPI's done as part of statistics coursework.

Desafio de Regressão para o curso de Data Science e Machine Learning da Tera. Aqui aplicamos uma regressão múltipla com seleção de 6 features e posteriormente treinamos um modelo de regressão random forest com tuning dos hiperparâmetros em que atingimos um erro médio absoluto de apenas R$ 15.400 nas previsões com um R² de 0.956

Identifying the factors affecting the attendance rate of students in Texas using descriptive and statistical analysis in Excel.

R script that performs complete multiple regression on two data sets

This repository houses the files related to my homework assignments for the Multivariate Analysis class. Throughout the coursework, I utilized R Studio for all of my work. In addition to the homework, I also completed two projects as part of this course. Feel free to explore the files and projects included here to gain insights into the MVA class.

Regression model provides detailed insight that can be applied to further improve products and services.

This is the final written report for Statistical Methods course

illini esports discord community growth and engagement analysis

supervised machine learning

R files to accompany Statistical Reasoning in Sports by Tabor and Franklin and climate modeling project for Honors Precalculus

Performed data cleaning, visualization, and statistical testing in R on Spotify’s Global Top 50 songs. Implemented multiple regression to identify multivariate predictors of song popularity.

solving problems with data analysis, hypotheses and the most used statistical tests in ecology

Conducted multivariate data analysis and exploration of wine quality dataset, and built predictive models using multiple regression in R

Machine Learning Techniques (Regression, Classification, Classifier, Support Vector Machines, Clustering)

아주대학교 2021-2 비즈니스 애널리틱스 프로젝트

Analysis of real estate sales data. Tasks include understanding dataset structure, variable conversion, descriptive analysis, pairwise comparisons, linear relationship analysis, multiple regression modeling, feature selection using stepwise methods, final model summary, assumptions checking, and LASSO variable selection. Results are documented.