This project aims at developing, validating, and testing several classification statistical models that could predict whether or not an office room is occupied using several data features, namely temperature (◦C), light (lx), humidity (%), CO2 (ppm), and a humidity ratio. The data is modeled using classification techniques i.e. Logistic regressi…

This is a project that was completed while taking the Udemy course - Python for Machine Learning & Data Science Masterclass.

R based data analytics on German Cars Market

Determining financial factors affecting the health of an individual

CS760: Machine Learning

Analysis of 2015 Mexican Ministry of Health administrative data

This is the repository for my R project on modeling historical weather data in Santa Barbara.

Classification prediction model

Projects using tree methods (CART, Random Forests, Boosted Trees)

This repository implements the basic machine learning classifiers for the problem of Yelp reviews classification. We assume the problem to be a binary classification problem. The models implemented are Naive Bayes, Logistic Regression, Support Vector Machine (linear), Decision Trees, Bagged Decision Trees, Random Fforests, and Boosted Decision T…

Compare different classification models to predict the accuracy of identifying credit card transactions as normal or fraud

Custom built Decision Tree + Boosted Trees + KernelPLS in python

Neural Networks, Ada Boost, Random Forest, KNN, BoostedForest

Classifying the survival of passengers aboard the Titanic via the use of various Machine Learning algorithms.

Protein classification with deep learning and boosted trees using topological features

Boosting algorithm(Machine learning) on Decision stumps(Decision tree with one split).

R code to reproduce analyses in "Rapid winter warming could disrupt coastal marine fish community structure" (Clark et al, Nature Climate Change, 2020)

Classification Trees, Random Forest, Boosting | Columbia Business School

Regression model for predicting house prices of residential homes in Ames, Iowa. Dataset contains 79 explanatory variables. Project includes key topics such as dataset cleaning, feature selection/engineering, EDA and applying grid search to find the best model.

Using radial velocity data to identify exoplanet companions