This ACME Robotics Product will use the Perception Module vision data alongside the sensor feedback, to avoid collision and choose a different path to traverse through the environment based on human detection and tracking from the continuous video input data stream, thus avoiding human collision.

A simple web app that helped students visualize the SVM algorithm according to their choice of hyperparameter setting.

Multinomial classification tasks in Reddit

Data Mining at UCSB with Professor Sang Oh (Fall 2016)

Created a web app, that predicts the type of flower using Iris Dataset by the University of California, Irvine

Support Vector Machine by using python and jupyter notebookes , its a Face Detection System

Finding Donors with Machine Learning ( Support Vector Machine, Gradient Boosting Classifier, Random Forest Classifier )

Get to know if you are Diabetic or NOT.

Investigated factors that affect the likelihood of charity donations being made based on real census data. Developed a naive classifier to compare testing results to. Trained and tested several supervised machine learning models on preprocessed census data to predict the likelihood of donations. Selected the best model based on accuracy, a modif…

Java Sensei un entorno inteligente de aprendizaje para el lenguaje Java

This is a repository with exercises extracted from the book "Introduction to machine learning with R" from Scott V. Burger. It will help you gain a solid foundation in machine learning principles. Using the R programming and then move into more advanced topics such as neural networks and tree-based methods.

Algorithmic Trading in R

IST 5535 Machine Learning Algorithms and Applications in R Project, where we developed a predictive model to predict the UPDRS score for Parkinsons Disease based on different dysphonia (noise) measures.

Analysis on Breast Cancer data set using Support Vector Machines, Bayesian Logistic Regression and Naive Bayes coded from scratch.

Binary Classification using Machine Learning

This repository contains a detailed analysis of the Spambase Dataset using different classification algorithms, including Logistic Regression, Logistic Regression with Backward Feature Elimination (BFE), Support Vector Machine (SVM), SVM with Normalized Data, Decision Trees, Random Forest, K-Nearest Neighbors (K-NN), and K-NN with Normalized Data.

Classify candidate exoplanets using various machine learning models like Random Forest, KNN, Logistic Regression and SVM

This repository contains the code for a web-based diabetes prediction application using a machine learning model. The application is built using Flask and allows users to input various health parameters to predict the likelihood of diabetes using ensemble voting classifier.

Identify the most efficient machine learning model to identify potential donors. Project covers Linear Regression, Perceptron Algorithm, Decision Trees, Naive Bayes, Support Vector Machines and Ensemble Methods.