In this section, we will examine classification algorithms in machine learning.

Machine learning library for classification tasks

Machine learning library for classification tasks

The sslearn library is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.

Two algorithms based on linear programming to discover classification rules for interpretable learning.

Code for "Designing Decision Support Systems Using Counterfactual Prediction Sets". ICML 2024.

Fast Best-Subset Selection Library

My portfolio website regarding data science projects. Some visualization and analysis projects reflect work for PITAPOLICY clients.

From Python basics to Machine Learning and PyTorch Deep Learning - one day at a time, explore it all

ETL and preprocessing of data to evaluate the possibilities of creating a machine learning models to predict whether a bidding item registered in purchases from ComprasGov systems will not have interested suppliers.

This project aims to aims to predict the customer churn (likelihood of a customer leaving the company) for a telecom company using a variety of ML classification algorithms.

This repository hosts a logistic regression model for telecom customer churn prediction. Trained on historical data, it analyzes customer attributes like account weeks, contract renewal status, and data plan usage to forecast churn likelihood. Its insights aid telecom companies in proactively retaining customers and mitigating churn rates.

"Welcome to our posture detection and pose classification web application repository! Utilizing Python, Flask, and libraries like Mediapipe, we empower users to monitor and classify human poses in real-time through webcam interactions. Perfect for fitness monitoring and gesture-based interfaces."

This is a lootfilter for the game "Path of Exile". It hides low value items, uses a markup-scheme and sounds to highlight expensive gear and is based on economy data mining.

UArizona DataLab Workshops

Build a predictive accident analysis app by loading historical accident data, preprocessing with Scikit-learn's Pipeline, training a model, and deploying using Streamlit for real-time predictions.

Python machine learning classification algorithm that calculates the percentage of spam in a dataset of thousands of emails.

Developed innovative optimization and ML algorithms to tackle data science tasks, including classification and sparse recovery, focusing on the NP-hard Maximum Feasible Subsystem problem.

Machine Learning Project

Practices and Assignments from the Advanced Machine Learning Class