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…

Inflated Discrete Choice Models

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

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…

Nested Dichotomy Logistic Regression Models

This MATLAB 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. Python version: https://github.com/T-Obuchi/Accele…

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..

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

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.

This project encompasses a range of neural and non-neural model implementations to classifiy MNIST digits. The goal is to compare the performance of each technique including details of hyper-parameters, training ans testing errors, training and testing duration and additional parameters used in the analysis.

This program uses the polarity and intensity of the words to assign one of five ratings to product reviews using Multinomial Logistic Regression. The data given was biased with more positive than negative reviews and hence required regularization.

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

Machine Learning algorithms (Classification, Clustering, Regression) on Iris dataset in R

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

Multinomial logistic regression modeling used to determine influential factors in nicotine usage status

Bayesian multinomial logistic regression implemented in R with runjags for MCMC.

This repository contains readme and code for the thesis research project.

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

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.

NLP of Self-Driving Car Tweets