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

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

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.

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.

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

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 …

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…