categorical-features

Tensorflow implementation of Product-based Neural Networks. An extended version is at https://github.com/Atomu2014/product-nets-distributed.

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4,5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting (GBDT, GBRT, GBM), Random Forest and Adaboost w/categorical features support for Python

This repository contains a notebook demonstrating a practical implementation of the so-called Entity Embedding for Encoding Categorical Features for Training a Neural Network.

Interactive ML Toolset

glmdisc Python package: discretization, factor level grouping, interaction discovery for logistic regression

Predicting the ideological direction of Supreme Court decisions: ensemble vs. unified case-based model

Multimodal deep learning package that uses both categorical and text-based features in a single deep architecture for regression and binary classification use cases.

A small tutorial to demonstrate the power of CatBoost Algorithm

Kaggle Categorical Feature Encoding Challenge II, private score 0.78795 (110 place)

Generic encoding of record types

Supervised Learning Problem. In this categorizing the customers in four groups, as follows: 1- Basic Service 2- E-Service 3- Plus Service 4- Total Service.

A python package to compute pairwise Euclidean distances on datasets with categorical features in little time

This study creates machine learning models to predict the seriousness of car crashes using 2019 and 2020 crash reports from the publicly accessable database maintained by the Chicago Police Department. A car crash is considered serious if the crash results in an injury or the car is towed due to the crash. Models use categorical features that describe conditions at the time of the crash and crash causes to predict the required target. The current focus is to classify whether a crash results in an injury. All machine learning models are trained, validated, and tested on randomly split 2019 crash reports. The best model (along with all others) are then tested using the full set of 2020 crash reports.

Kaggle Competition (Encoding categorical variables)

A set of tools for machine learning (for the current day, there are active learning utilities and implementations of some stacking-based techniques).

log

Category encoders integrated with Fast.ai

Explore various natural language processing models using Python.

Feature Importance of categorical variables by converting them into dummy variables (One-hot-encoding) can skewed or hard to interpret results. Here I present a method to get around this problem using H2O.

Why data analysis? , How to understand the problem, what to do for data analysis, and how clean the data for building Machine Learning models

My solution for Kaggle competition "categorical feature encoding challenge II" with public and private score of 0.783.

Binary classification, with every feature as categoricals

Medium Post: some techniques useful to deal with missing values of Categorical Features