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.

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

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.

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.

A comprehensive geospatial analysis of blood pressure categories across the Austria region

Python project for the Fundamentals of of Data Science class for the MSc. in Data Science at the Sapienza University of Rome. The main purpose of the project is exploring Logistic Regression & Multinomial Regression concepts along with training classifiers using Gradient Descent/Ascent.

A MCMC Bayesian analysis versus Frequentist Analysis of Animal Crossing: New Horizons game players in-game behavior using a Multinomial Logistic Regression Model to adjust the original paper results.

This code explores predictive composite measures for health facility disruptions in conflict zones based on conflict intensity (measured through repeat conflict events in close proximity) and type of conflict event.

This notebook explores comprehensive machine learning analysis on a rock dataset, covering attribute distribution analysis, outlier identification using statistical values and visualizations like scatter plots,and applying Multinomial Logistic Regression,Support Vector Machines, Random Forest classifiers,Ensemble learning,hyperparameter optimizatio

This was the code I use to process a Multinomial Logistic Regression on R, with the Apollo Choice Modeling Package for R. Used to calculate the utility function of particular customers of vehicles in Bogotá, Colombia

The Exame Nacional do Ensino Médio (also known as ENEM) is a national Brazilian standardized test that allows students to conquer a spot in universities in the country and abroad (Inep, 2016). With millions of examinees from different social backgrounds, this paper aims to use the socio-economic data gathered in the 2019 exam application to pred…

This repository contains a credit scoring system that leverages machine learning to predict the likelihood of a user receiving a loan. The system includes a user interface where users can upload data files to receive loan decisions, loan probability assessments, and suggested loan amounts for eligible applicant.

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…

NLP of Self-Driving Car Tweets

Complete details of Multinomial Logistic Regression from scratch . It is optimized to perform the operation in minimum time .

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.