Semantic Segmentation Using U-Net Architecture
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Updated
Jul 25, 2024 - Jupyter Notebook
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.
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