Various Classification models used are Logistic regression, K-NN, Support Vector Machine, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification using R

Random forest analysis of match statistics and team performances in five seasons of the English Premier League (EPL)

Model predicts and detects the suicidal tendency of a person by runnning R script of ML algorithm.

The project concerns an international e-commerce company* based in the USA who want to discover key insights from their customer database. They want to use some of the most advanced machine learning techniques to study their customers.

Predicting Usefulness of Code Review Comments using Textual Features and Developer Experience

Data Mining Project: Classification of the Car Evaluation Dataset.

Aims to build a classification model that can provide a basic understanding of what types of personal characteristics are likely to cause stroke and the probability that those characteristics lead to stroke.

Machine learning analysis & visualisation of cellular spatial point patterns

Credit risk analysis using the LASSO, Random Forests and the SMOTE technique for balancing

Code to classify hedgerows for a livestock landscape in Catacamas, Olancho, Honduras

Human resource has become one of the main concerns of managers in almost all types of businesses which include private companies, educational institutions and governmental organizations. Business Organizations are really interested to settle plans for correctly selecting proper employees. After hiring employees, managements become concerned abou…

Implementing different flavors of Classification and Regression Machine Learning Algorithms on different datasets in the US region.

Methodology research comparing statistical and ML methods of competing risks analysis

Using a variety of techniques, including descriptive analysis, machine learning models, and K-means clustering, to identify key customer segments.

This original code is the product of Travis Zalesky's Final Project in U. of AZ MS GIST class 601B - Remote Sensing. It is being provided publicly in the interest of transparity and repeatability.

A predictive analytics model for a Kaggle competition to predict the price of car using a dataset containing information on 40,000 used cars.

This project aims to predict customer churn using machine learning techniques in R. By analyzing customer data, we identify patterns and build models to help businesses improve customer retention strategies.

For my Data Analytics subject I studied about car consumption habits depending on different factors collected in a survey made by Smartme Analytics.

Machine learning binary classification RStudio