Google Street View House Number(SVHN) Dataset, and classifying them through CNN

Demo on the capability of Yandex CatBoost gradient boosting classifier on a fictitious IBM HR dataset obtained from Kaggle. Data exploration, cleaning, preprocessing and model tuning are performed on the dataset

A command-line utility program for automating the trivial, frequently occurring data preparation tasks: missing value interpolation, outlier removal, and encoding categorical variables.

Adaptive Reinforcement Learning of curious AI basketball agents

This repository contains Sentiment Classification, Word Level Text Generation, Character Level Text Generation and other important codes/notes on NLP. Python and Keras are used for implementation.

Deeplearning4J框架搭建的第一个问答小AI

Analysis and preprocessing of the kdd cup 99 dataset using python and scikit-learn

Keras 응용(CNN, RNN, GAN, DNN, ETC...) 사용법 예시

Machine-learning models to predict whether customers respond to a marketing campaign

one hot encoding using numpy, sklearn, and keras. Created Date: 7 Jan 2019

Movie Recommendation System

Generic encoding of record types

Determining the housing prices of California properties for new sellers and also for buyers to estimate the profitability of the deal.

Kaggle Challenge

Implementation of Character level CNN

Data analysis and machine learning

Customer churn analysis for a telecommunication company

Deep Neural Networks like Single Layer Perceptron and Multi Layer Perceptron implementation using Tensorflow library on Datasets like MNIST and Naval Mine for categorical Classification. Saving and Restoring Tensorflow "Variables" weights for testing.

This is my contribution to a competition on kaggle.com, where you have a dataset with 79 explanatory variables describing (almost) every aspect of c. 1500 residential homes in Ames, Iowa. The aim is to predict the final price of each home.

A Machine Learning project to predict house prices on Ames Housing Data

Basic ML using Sklearn to save/load a model, split training & test dataset, create dummy variables and one hot encoder

AIND Jupyter Notebook to predict student admissions using Keras Neural Networks

Different types of word embedding for text processing

This is the code for "Recurrent NeuralNetwork using keras and numpy" By M.Junaid Fiaz

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.

Using random forest to predict Titanic passenger survival.