Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

A Python library called ScratchML was created to build the most fundamental Machine Learning models from scratch (using only Numpy), emphasizing producing user-friendly, straightforward, and easy-to-use implementations for novices and enthusiasts.

Evolutionary decision trees

A Python-based chess-like board game featuring advanced AI strategies including Minimax and Alpha-Beta pruning algorithms. Implements both offensive and defensive heuristics with performance metrics tracking and GUI visualization using Pygame.

scikit_learn

PyXAI (Python eXplainable AI) is a Python library (version 3.6 or later) allowing to bring formal explanations suited to (regression or classification) tree-based ML models (Decision Trees, Random Forests, Boosted Trees, ...).

A multi-model machine learning project that achieves up to 100% accuracy in classifying star types using five different AI algorithms

Essential NLP & ML, short & fast pure Python code

This Machine Learning project predicts diseases based on the symptoms provided by the user. It employs three different machine learning algorithms: Decision Tree, Random Forest, and Naive Bayes, to ensure accurate predictions. The user interface is built using Tkinter for easy interaction.

Decision tree methods in federated learning with FLEXible.

This repository contains a simple implementation of the ID3 decision tree learning algorithm in Python. The ID3 algorithm is a popular machine learning algorithm used for building decision trees based on given data.

Leverages big data and machine learning for wildlife conservation using GBIF species data. PySpark is used for preprocessing, K-Means for clustering, and Decision Trees for habitat prediction. Tableau visualizes species distribution, biodiversity, and conservation insights.

Analyzing customer churn data and implementing other algorithms such as Decision Tree and Random Forest to predict churn.

Heart disease classification using machine learning algorithms with hyperparameter tuning for optimized model performance. Algorithms include XGBoost, Random Forest, Logistic Regression, and moreto find the best model for accurate heart disease prediction.

Output-Constrained Decision Trees (OCDT)

solution for "Predicting Movie Rental Durations" of Data Camp in data scientist track's

🤖📖 Application that compares most known ML algorithms and generates plots and markdown files.

Python package for Visual Studio Code extension dec-tree-vscode for Decision Tree

The final experiment of machine learning 24, spring in HUST.

A Python-based tool for building and analyzing decision trees in pharmacoeconomics.