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.

Kaggle Challenge

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.

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 project aims to practice the steps of Crisp Data Mining ( CRISP-DM ). The repository includes 3 phases, data understanding, supervised learning, and unsupervised learning.

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

Unofficial but extremely useful Label and One Hot encoders.

Trabalho Prático 02 da disciplina de Sistemas de Recomendação.

Using random forest to predict Titanic passenger survival.

Implementation of Character level CNN

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

Generic encoding of record types

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.

To predict whether booked appointment will be completed or it will be no show.

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

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

Customer churn analysis for a telecommunication company