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.

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

Machine Learning Models

Encode Categorical Features based on Target/Class

Code templates for different ML algorithms

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

Customer Churn Analysis

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

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

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.

Heart Risk Level Predicting Regression Model & Web using Feature Engineering and Data Preprocessing 🐤

Text Processing RNN leverages RNN and LSTM models for advanced text processing. It features deep learning techniques for NLP tasks, utilizing GloVe for word embeddings, aimed at both educational and practical applications.

The feature engineering techniques discussed are - dimensionality reduction(pca), scaling(standard scaler, normalizer, minmaxscaler), categorical encoding(one hot/dummy), binning, clustering, feature selection. These are techniques performed on a dataset consisting of Californian House Prices.