Generic implementation for Generalized Linear Models including Logistic, Poisson and Ordinal Regression for Classification purposes
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Updated
Nov 10, 2019 - Python
Generic implementation for Generalized Linear Models including Logistic, Poisson and Ordinal Regression for Classification purposes
Machine Learning From Scratch
Machine Learning algorithms from-scratch implementation. It covers most Supervised and Unsupervised algorithms. Homework assignments and Projects for graduate level Machine Learning Course taught by Dr Manfred Huber at UTA during Spring 21
Models for estimating football (soccer) team-strength
This project contains the data and code used in the paper: Denter, Nils M.; Aaldering, Lukas Jan; Caferoglu, Huseyin (2022): Forecasting future bigrams and promising patents: Introducing text-based link prediction. In Foresight ahead-of-print (ahead-of-print). DOI: doi.org/10.1108/fs-03-2021-0078.
Course XCS229i in Machine Learning from Stanford University
Visualize the predicted weather related delays of US domestic flights. See the linked video for a 3 minute whirlwind tour of the app:
Norm Constrained Generalised Linear Model using numpy, numba and scipy.
Benchopt benchmark for GLMs
A bot that provides soccer predictions using Poisson regression
This repository contains implementations of advanced regression methods, including ordinary least squares, Poisson regression, and locally weighted regression. It also explores bias-variance decomposition for regularized mean estimators. The analysis is conducted on the Capital Bikesharing dataset using Python.
Extended Elo rating system implementation based on the equivalence with logistic regression.
Statistical investigation of how sandwich components affect ant attraction, based on a full factorial design with ANOVA and Poisson regression.
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