Scikit-Learn compatible transformer that turns categorical variables into dense entity embeddings.

Open source machine learning library with various machine learning tools

Weight of Evidence Encoding & Information Value

Perform semi automated exploratory data analysis, feature engineering and feature selection on provided dataset by visualizing every possibilities on each step and assisting the user to make a meaningful decision to achieve a low-bias and low-variance model.

[ECAI' 25]: MOCA-HESP: Meta High-dimensional Bayesian Optimization for Combinatorial and Mixed Spaces via Hyper-ellipsoid Partitioning

A machine learning project to predict loan defaults in a German bank's customer base. Using the German Credit Risk dataset, it explores key factors contributing to defaults and trains models like Random Forest, GBM, and XGBoost. Includes EDA, data processing, hyperparameter tuning, and model evaluation.

An interactive HR-analytics app predicting employee churn via scikit-learn pipelines and Streamlit

Code templates for different ML algorithms

🎬This KickStarter project is about some🎞 foreign films🎥 and music videos🎶. This is an analysis 📽of their 'goal currency' and release time.🎦

This repo contains code for experimenting with categorical encoding - WoE, Catboost, Target encoder, and many more.

Stacked Classifier

Intermediate Machine Learning Course By Kaggle

Machine Learning Models

Exploratory data analysis and model preparation for DrivenData contest: PumpItUp!

Customer Churn Analysis

This repository is a comprehensive guide to different Encoding techniques in Machine Learning, explaining when to use each method and best practices. You'll find practical examples, ready-to-use code, and comparisons between various techniques like Label Encoding, One-Hot Encoding, Target Encoding, and more!

The project uses Artificial Neural Network and Convolutional Neural Network to classify images into 10 different categories.

Successfully established a machine learning model that can accurately classify an e-commerce product into one of four categories, namely "Books", "Clothing & Accessories", "Household" and "Electronics", based on the product's description.

Encode Categorical Features based on Target/Class