feature-transformation

leffff/waveml

Open source machine learning library with various machine learning tools

kaledhoshme123/Multi-Scale-CycleGAN-Night-to-Day

Converting night into day is one of the most interesting applications in generative models, due to the great difficulty in recreating the scene during the day, especially in cases of extreme darkness, and thus the difficulty lies in imagining the scene during the day when the lighting is very weak.

pszwed-ai/fcm_classifier_transformer

This repository contains source code to the article: Piotr Szwed: Classification and feature transformation with Fuzzy Cognitive Maps, Applied Soft Computing, Elsevier 2021

lmego/customer_segments

Creating Customer Segments - 4th project for Udacity's Machine Learning Nanodegree

davpinto/mmlbh-feature-engineering

Feature engineering in machine learning

coco11563/Traceable_Automatic_Feature_Transformation_via_Cascading_Actor-Critic_Agents

Code for <Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents>

airsmed-aiiv/TSIT

TSIT implementation in TensorFlow; TSIT: A Simple and Versatile Framework for Image-to-Image Translation

paliwalabhishek/AutoEncoder

Implementation of the stacked autoencoder in Tensorflow

Ansu-John/MLlib-Working-with-Features

Extracting, transforming and selecting features using Spark MLlib

Faisal-AlDhuwayhi/Identify-Customer-Segments

Identifying Customer Segments using unsupervised learning techniques

Machine-Learning-Tubes/stage-1-feature-engineering

Tahap 1 Tugas Besar - data preprocessing pada dataset Telco Customer Churn

ManarAlharbi/DSND-Term1-Identify_Customer_Segments

Apply unsupervised learning techniques to identify customers segments.

moreirab/customer-segments

Machine Learning Engineer Nanodegree, Unsupervised Learning, Creating Customer Segments

risarora/ML_recipes.md

A collection of working snippets used for machine learning related tasks.

Shriram-Vibhute/House_Price_Prediction

This project aims to predict Prices of House. It involves several key stages, including data preprocessing, feature engineering, model selection, and evaluation. The goal is to develop a model that provides accurate and reliable price predictions based on the given features.

zainab-rizwan/Airbnb-Price-Recomendation-System

Airbnb price prediction with machine learning models using Amsterdam dataset.

asayem172153/Machine-Learning-related-works

Here I have Demonstrated Some of my Machine Learning works

ashva7/customer_segments

Customer Segments - Machine Learning Nanodegree from Udacity

gouravaich/creating-customer-segments

Apply unsupervised machine learning techniques on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data

gupta-astha/Data-Science-projects

magnusax/BinaryEncoder

Scikit-klearn compatible BinaryEncoder class capable of handling unseen categories in an automated fashion

OmerGamie/sfguide-intro-to-machine-learning-with-snowpark-ml-for-python-main

rkraut81/EDA-and-Feature-Engineering

This repository contains all the EDA done on various data sources as well as Feature Engineering

srikanthv0610/House_Price_Prediction

Using the dataset compiled by Dean De Cock. Applying Feature Transformation, Feature Selection and K-fold Cross Validation

TrilokiDA/Data-pre-processing

Data preprocessing is a data mining technique that is used to transform the raw data into a useful and efficient format.

ashishyadav24092000/FULL-Feature-Transformations

In this project we have performed all types of feature transfromation on the titanic dataset and we have seen the usage of qqplot to check whether a feature is normal/gaussian distributed or not.

kengz/feature_transform

Build ColumnTransformers (Scikit or DaskML) for feature transformation by specifying configs.

Sabbir185/Machine-learning-with-python

SayamAlt/Bank-Customer-Churn-Prediction-using-PySpark

Successfully established a machine learning model using PySpark which can accurately classify whether a bank customer will churn or not up to an accuracy of more than 86% on the test set.

sushantdhumak/Creating-Customer-Segments

Machine Learning Nano-degree Project : To identify customer segments hidden in product spending data collected for customers of a wholesale distributor