Feature engineering is the process of creating, selecting, and transforming input features to improve the performance of machine learning models. It includes techniques such as feature extraction, feature selection, encoding categorical variables, scaling numerical features, and generating new features from existing data. Effective feature engineering helps models capture meaningful patterns, improve predictive accuracy, and generalize better to unseen data.
[ECCV 2020 Spotlight] A Simple and Versatile Framework for Image-to-Image Translation
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.
Open source machine learning library with various machine learning tools
This repository contains source code to the article: Piotr Szwed: Classification and feature transformation with Fuzzy Cognitive Maps, Applied Soft Computing, Elsevier 2021
Creating Customer Segments - 4th project for Udacity's Machine Learning Nanodegree
Feature engineering in machine learning
Code for <Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents>
Implementation of the stacked autoencoder in Tensorflow
TSIT implementation in TensorFlow; TSIT: A Simple and Versatile Framework for Image-to-Image Translation
A practical repository covering real-world feature engineering techniques to transform raw data into machine-learning-ready features using Python and Scikit-Learn.
Machine Learning Nano-degree Project : To identify customer segments hidden in product spending data collected for customers of a wholesale distributor
Apply unsupervised learning techniques to identify customers segments.
Airbnb price prediction with machine learning models using Amsterdam dataset.
A collection of working snippets used for machine learning related tasks.
Extracting, transforming and selecting features using Spark MLlib
Machine Learning Engineer Nanodegree, Unsupervised Learning, Creating Customer Segments
Customer segmentation using K-Means clustering with EDA, statistical validation, and actionable business recommendations
This project aims to predict property prices using advanced regression techniques, providing accurate estimations based on input features. By leveraging machine learning algorithms, this project enables data-driven insights into real estate pricing trends, helping stakeholders make informed decisions.
Implemented Xgboost model with optimum hyperparameters to predict sales in a BigMart mall.
To read the given data and perform Feature Encoding and Transformation process. ( 19AI403 - Introduction to Data Science )