Semantic Segmentation Using U-Net Architecture

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.

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

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

Unofficial but extremely useful Label and One Hot encoders.

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

Trabalho Prático 02 da disciplina de Sistemas de Recomendação.

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

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

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

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

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.

Implementation of Character level CNN

Generic encoding of record types

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

Customer churn analysis for a telecommunication company

Kaggle Challenge

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

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.

Using random forest to predict Titanic passenger survival.