Fit interpretable models. Explain blackbox machine learning.

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Movie Revenue Prediction System predicts the revenue of a movie with 14 parameters: name, rating, genre, year, released, score, votes, director, writer, star, country, budget, company and runtime using gradient boosting______________________________ Training Accuracy: 91.58%____________ Testing Accuracy: 82.42%.

This repo contains exploratory data analysis and modeling code for employee churn prediction system.

A game theoretic approach to explain the output of any machine learning model.

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

This project aims to Predict customer churn in subscription business using ML. Dataset has usage behavior, demographics, churn status. Trained Logistic Regression, Random Forest, Gradient Boosting. Best model deployed for future churn prediction.

Tools created for machine learning classification model evaluation

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

This repository provides a systematic approach to winning the "Guess Who?" game through advanced machine learning techniques. It offers a comprehensive methodology for enhancing gameplay strategy and optimizing decision-making processes with meticulous attention to detail.

Detect Credit Card Fraud with Machine Learning in R

Investment Analysis and Asset Mgmt, Time Series Analysis & Forecasting, Machine Learning in Finance & Causal Inference Methods

Implementations of main Machine Learning Agorithms from scratch: Gaussian Mixture Model, Gradient Boosting, Adam, RMSProp, PCA, QR, Eigendecomposition, Decision Trees etc.

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

Two algorithms based on linear programming to discover classification rules for interpretable learning.

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-label Classification Rules

Investigate personnel elements influencing organizational dynamics by looking at HR analytics data using python and advanced machine learning models. Forecast employment status, estimate the period of termination, and maximize performance and satisfaction initiatives.

Implementing Tree-based algorithms from scratch (Decision Tree, Random Forest, and Gradient Boosting) from scratch and comparing it to the scikit-learn implementation.