Red hat dataset in kaggle competition
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Updated
Dec 19, 2017 - Jupyter Notebook
Red hat dataset in kaggle competition
Laboratory works on Methods of Artificial Intelligence course
Predicting the ideological direction of Supreme Court decisions: ensemble vs. unified case-based model
Generic encoding of record types
This repository contains a notebook demonstrating a practical implementation of the so-called Entity Embedding for Encoding Categorical Features for Training a Neural Network.
EDA-REGRESSION-CLASSIFICATION-WITH-BALCK-FRIDAY-DATASET
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.
Binary classification, with every feature as categoricals
Solution to Zalo AI Challenge 2019's Hit Song Prediction.
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.
Tensorflow implementation of Product-based Neural Networks. An extended version is at https://github.com/Atomu2014/product-nets-distributed.
Machine Learning (Pyspark-MLlib and Pyspark-Sql)
Kaggle Competition (Encoding categorical variables)
Interactive ML Toolset
Kaggle Categorical Feature Encoding Challenge II, private score 0.78795 (110 place)
This repository contains notebooks on different topics across - linear algebra, image classification, language models etc.
Data Science in the Banking Industry [Volume 1]
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