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xgboost

A binary classification model to accurately predict whether a mushroom is edible or poisonous based on a given dataset. The dataset, a cleaned version of the original Mushroom Dataset from UCI, includes features such as cap diameter, cap shape, gill attachment, gill color, and others. The goal is to create a reliable model that can assist in identifying potentially harmful mushrooms.

Project to automate training of Classification model using custom scripts and MLFlow. Classification model is trained using XGBoost library.

My attempts to solve homework from the Moscow Institute of Physics and Technology course

Attempted to create a model that could determine the genre of a song based off of features from Spotify's data.

Machine Learning - Recommendation System

Machine Learning - Binary Classification

Machine Learning - Classification Problem

Machine Learning - Multiclass Classification

Machine Learning - Regression Problem

Machine Learning - Binary Classification

A Clojure wrapper for XGBoost

Using machine learning to detect if an individual has Parkinson's disease.

King County House Sales

A VERY early alpha, don't use. Or use, but refactor first and send a PR.

Compiles an xgboost model into python if-else code