Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

data-science machine-learning data-mining deep-learning scikit-learn data-visualization feature-selection feature-extraction data-analysis data-scientists feature-engineering features feature-scaling

• Updated Jun 29, 2025
• Jupyter Notebook

A Machine Learning Approach of Emotional Model

data-science machine-learning feature-selection feature-extraction music-information-retrieval digital-signal-processing librosa feature-scaling

• Updated Aug 5, 2024
• Python

numpy linear-regression sklearn logistic-regression matplotlib regularization gradient-descent feature-engineering polynomial-regression decision-boundary learning-curve feature-scaling contour-plot cost-function feature-mapping mean-normalization regularization-to-avoid-overfitting standard-normal

• Updated Jun 4, 2024
• Jupyter Notebook

This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.

python statistics eda feature-selection feature-extraction dimensionality-reduction feature-engineering feature-scaling

• Updated Jan 24, 2022
• Jupyter Notebook

Exemplary, annotated machine learning pipeline for any tabular data problem.

modeling sklearn h2o data-visualization feature-selection feature-engineering data-cleaning machine-learning-pipelines feature-scaling data-imputation featuretools feature-encoding

• Updated Aug 30, 2019
• Jupyter Notebook

Comprehensive ML curriculum: from NumPy to Neural Networks. 50+ Jupyter notebooks with from-scratch implementations and real-world projects.

python nlp data-science machine-learning deep-learning tensorflow svm naive-bayes scikit-learn jupyter-notebook regression mnist neural-networks pca classification gradient-descent knn decision-tree cifar10 feature-scaling

• Updated Feb 28, 2026
• Jupyter Notebook

A curated list of awesome open source and commercial feature store tools and platforms 🚀

kubernetes machine-learning awesome feature-selection datascience feature-extraction dataops awesome-list feature-engineering feature-scaling model-serving mlops feature-store

• Updated Apr 20, 2022

Tutorial on how to perform feature encoding, feature scaling, and missing values imputation using the scikit-learn library

feature-scaling imputation-methods feature-encoding

• Updated May 30, 2021
• Jupyter Notebook

Given dataset of Diamonds with features such as Cut, Carat, Clarity etc. I have used libraries such as Pandas, Numpy, Matplotlib, Seaborn to Analyse and Estimate the Price of Diamonds based on the features. Using Scikit-Learn , implemented Algorithms to increase the effective R2 score.

machine-learning numpy pandas data-visualization seaborn matplotlib feature-engineering feature-scaling

• Updated Aug 22, 2018
• Jupyter Notebook

My solution to House-Prices Advanced Regression Techniques, A beginner-friendly project on Kaggle.

data-visualization python3 kaggle-competition xgboost hyperparameter-optimization feature-engineering ridge-regression feature-scaling support-vector-regression lasso-regression-model lgbmregressor

• Updated May 26, 2022
• Jupyter Notebook

Machine Learning Notebooks

machine-learning linear-regression jupyter-notebook ml feature-scaling normal-equation

• Updated Feb 26, 2018
• Jupyter Notebook

Machine learning algorithms repository

machine-learning clustering machine-learning-algorithms outlier-detection feature-scaling

• Updated Jan 13, 2022
• Jupyter Notebook

Data Science Portfolio created for academic and personal projects.

python linear-regression sklearn pandas feature-selection performance-metrics xgboost feature-engineering hyperparameter-tuning kmeans-clustering feature-scaling gradient-boosting

• Updated Mar 25, 2023
• Jupyter Notebook

An attempt to predict the Stock Market Price using Long Short Term memory and plot its chart. By tweaking different hyper parameters, we get different trained models. The aim of this project is to identify the relation hidden in these hyper parameters.

machine-learning deep-learning jupyter-notebook python3 recurrent-neural-networks lstm stock-price-prediction research-project hacktoberfest lstm-neural-networks feature-scaling long-short-term-memory tkinter-gui tensorflow2

• Updated Oct 9, 2022
• PureBasic

Successfully established a machine learning model which can estimate the net health insurance claim of an individual based on a set of characteristics of that individual to an appreciable level of accuracy.

machine-learning exploratory-data-analysis regression-testing feature-engineering feature-scaling model-deployment regression-analysis

• Updated Jun 2, 2022
• Jupyter Notebook

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

machine-learning udacity clustering pca gaussian-mixture-models dimensionality-reduction outlier-detection unsupervised-learning udacity-nanodegree clustering-algorithm creating-customer-segments scatter-matrix feature-scaling feature-transformation biplot

• Updated Feb 9, 2017
• HTML

The purpose of this project is to analyze the impact of climate change on air quality for the city of Austin and create a machine learning model that can establish a correlation between the level of air pollutants like Ozone and NO2 and the climate parameters by using regression models and null hypothesis.

python api random-forest linear-regression plotly pandas seaborn requests polynomial-regression feature-scaling lasso-regression

• Updated Dec 8, 2022
• Jupyter Notebook

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

neural-network pandas imputation support-vector-machine data-cleaning decision-tree multicollinearity feature-scaling receiver-operating-characteristic one-hot-encode missing-values dummy-variable

• Updated Apr 9, 2018
• Jupyter Notebook

Successfully established a machine learning model which can predict an appropriate stellar class, on the basis of a distinct set of spectral characteristics, to a substantially high level of accuracy.

exploratory-data-analysis cross-validation data-visualization data-preprocessing feature-engineering feature-scaling model-evaluation multiclass-classification supervised-machine-learning model-deployment imbalanced-learning model-training

• Updated Jun 10, 2022
• Jupyter Notebook

Machine learning project clustering countries based on socio-economic & demographic indicators using K-Means, iterative imputation & feature scaling.

python data-science machine-learning clustering numpy scikit-learn exploratory-data-analysis pandas data-visualization seaborn matplotlib kmeans unsupervised-learning feature-scaling global-data economic-analysis missing-data-imputation silhouette-score iterative-imputer country-clustering

• Updated Nov 10, 2025
• Python