Discover relevant information about categorical data with entity embeddings using Neural Networks (powered by Keras)

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

This repo contains various data science strategy and machine learning models to deal with structure as well as unstructured data. It contains module on feature-preprocessing, feature-engineering, machine-learning-models, bayesian-parameter-tuning, etc, built using libraries such as scikit-learn, keras, h2o, xgboost, lightgbm, catboost, etc.

Deep Learning and Entity Embeddings to predict driving behaviour and cluster accident hotspots

Train word2vec and keeps all entities regardless of their frequency

Kaggle Competition