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

House Price Prediction is a machine learning workflow for estimating housing prices, featuring data cleaning, EDA, feature scaling, categorical encoding, and multiple regression models (Linear, Ridge, Decision Tree, Gradient Boosting, XGBoost, CatBoost, SVR) implemented in Python with Scikit-learn.

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.

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

Intermediate Machine Learning Course By Kaggle

Stacked Classifier

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

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

Machine Learning Models

Code templates for different ML algorithms

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!

Encode Categorical Features based on Target/Class

Housing Prices Prediction using Machine Learning Developed a regression model to predict housing prices using data preprocessing, feature engineering, and various regression algorithms. Tuned hyperparameters and evaluated performance with key metrics (RMSE, MAE, R²).