Data search & enrichment library for Machine Learning → Easily find and add relevant features to your ML & AI pipeline from hundreds of public and premium external data sources, including open & commercial LLMs

Feature Selection using Metaheuristics Made Easy: Open Source MAFESE Library in Python

This project aims to understand and predict a car's fuel efficiency based on its characteristics. I have built a multiple linear regression model using stats models and scikit-learn.

Data pre-processing with modular components for: normalizer/standarizer, unbiaser, trimmer and feature selector.

End-to-End Machine Learning project I made as a machine learning intern @ Mentorness

Machine Learning in R

Up to 90% accuracy with just 5 features using KNN algorithm and PCA for feature engineering. The dataset contained less than 1000 observations. The model's accuracy could be improved using more observations, further hyperparameter optimization and feature engineering

Synthetic data generation package to balance imblanaced datasets

Feature selection package of the mlr3 ecosystem.

Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models

This project explores an IBM telecom dataset, conducting initial EDA and data preprocessing. It examines three genetic algorithm variations for feature selection: one-point, two-point, and uniform crossover. Logistic regression is used to predict customer churn, and performance is evaluated using error bar plots.

This project uses Exploratory Data Analysis (EDA) to uncover trends and insights from restaurant cuisine ratings, helping improve menus, enhance customer experiences, and guide targeted marketing strategies for business success.

Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.

Feature selection with Firefly Algorithm

Machine learning and data analysis package implemented in JavaScript and its online demo.

EvalML is an AutoML library written in python.

Principal Component Analysis (PCA) is a powerful dimensionality reduction technique commonly used in machine learning and data analysis. It transforms a dataset into a set of linearly uncorrelated variables called principal components.

Easy to use Python library of customized functions for cleaning and analyzing data.

The repository presents the notebooks and models used for my experimental thesis entitled: "Experimental Study of the Steel Market Through CNN-LSTM Deep Learning Models: Practical Applications for Cost Reduction in Industries"

A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.