I have used Multinomial Naive Bayes, Random Trees Embedding, Random Forest Regressor, Random Forest Classifier, Multinomial Logistic Regression, Linear Support Vector Classifier, Linear Regression, Extra Tree Regressor, Extra Tree Classifier, Decision Tree Classifier, Binary Logistic Regression and calculated accuracy score, confusion matrix and…

Nested Dichotomy Logistic Regression Models

Automating Assumption Checks for Regression Models (Work in Progress, Currently Paused)

Given a dataset with data on the size of the sepals (petals) and the size of the petals (corolla) of 150 irises consisting of three species, namely Iris setosa, Iris versicolor, and Iris virginica. Petal size will be used as a measure of flower classification using multinomial logistic regression.

This project aims to conduct a random survey design for collecting responses regarding wine preferences of Italian consumers. Furthermore, it attempts to understand how preference share gets affected as we vary different attributes associated with wine with the use of a research method called Conjoint Analysis..

ml5 (friendly machine learning for the web) SharePoint Framework (SPFx) extension

Inflated Discrete Choice Models

This Python package enables to efficiently compute leave-one-out cross validation error for multinomial logistic regression with elastic net (L1 and L2) penalty. The computation is based on an analytical approximation, which enables to avoid re-optimization and to reduce much computational time. MATLAB version: https://github.com/T-Obuchi/Accele…

Spatio-temporal estimates of HIV risk group proportions for AGYW across 13 priority countries in sub-Saharan Africa

Bayesian Machine Learning with PYMC3. Data from the Kyiv School of Economics.

Logistic Regression is a supervised learning algorithm that is used when the target variable is categorical. In Logistic Regression the target variable is categorical where we have to strict the range of predicted values. Consider a classification problem, where we need to classify whether an email is a spam or not. So we have to predict either …

Our industrial attachment project involves developing a credit scoring system to determine Upay users' loan eligibility. This system uses machine learning to forecast loan approval using transaction history and customer data. This project aims to provide a reliable credit score system for loan disbursement. It will also inform decision makers about

Finding out whether it is possible to predict the quality of the wine using Random Forests, Neural Networks, Classification trees and other methods

Given the title of a fake news article A and the title of a coming news article B, program classifies B into agree, disagree, and unrelated.

2022 FIFA World Cup (Qatar) prediction, using Multinomial Logistic Regression.

Choice-Based Conjoint Analysis, Multinomial Logit Model, Multinomial Logit Model with random coefficients

Tools created for machine learning classification model evaluation

A collection of fundamental Machine Learning Algorithms Implemented from scratch along-with their applications for various ML tasks like clustering, thresholding, data analysis, prediction, regression and image classification.

Developed a random forest model to predict satellite images with 91.4% accuracy and 0.02% standard deviation of accuracies for all replications.