Johns Hopkins University Bloomberg School of Public Health: Data Science Specialization Program: Regression Models Course: Motor Trend Project repo: date created 61229

Finding Donors for CharityML

Elegant Mathematica-style model manipulation, fitting and exploration in MATLAB.

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…

The projects which involve the use of ML concepts

GEARS a toolbox for Global parameter Estimation with Automated Regularisation via Sampling by Jake Alan Pitt and Julio R. Banga

Implementation of the algorithm described in the following paper. Korenberg, M., Billings, S.A. and Liu, Y.P. (1987) An Orthogonal Parameter Estimation Algorithm for Nonlinear Stochastic Systems

Reference repository for code used in our NBDT publication on the dynamic clicks task

Meta-analysis toolbox for basic research applications. Developed in MATLAB R2016b.

This MATLAB script fits either a linear or hyperbolic function to time-series data (e.g., growth data).

code to infer model parameters from data (first dedicated to the dynamic clicks task)

tsiR: An R package for time-series Susceptible-Infected-Recovered models of epidemics

Course Project for the course CS 736

The project involves projective geometry, geometric transformations, modelling of cameras, feature extraction, stereo vision, recognition and deep learning, 3d-modelling, geometry of surfaces and their silhouettes, tracking, and visualisation.

Statistical Modelling of Swine Flu Outbreak Data

Fitting a stochastic individual based model for arboviral disease.

Python framework for multi-parameter optimization and evaluation of protein folding models

Semester Project for Timeseries Course / Aristotle University of Thessaloniki / Winter Semester 2020

This notebook explores the housing dataset from Kaggle to predict Sales Prices of housing using advanced regression techniques such as feature engineering and gradient boosting.