A minimalistic framework for Numerical Association Rule Mining

Analytics and Systems of Big Data

"Frequent Mining Algorithms" is a Python library that includes frequent mining algorithms. This library contains popular algorithms used to discover frequent items and patterns in datasets. Frequent mining is widely used in various applications to uncover significant insights, such as market basket analysis, network traffic analysis, etc.

Market Basket Analysis What is it? Market Basket Analysis is a modelling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items. For example, if you are in an English pub and you buy a pint of beer and don't buy a bar meal, you are more likely to buy crisps (US. chips…

A python implementation of the Multiple Support Apriori Algorithm

This project is a Market Basket Analysis App that analyzes customer purchase patterns to generate association rules and offer personalized product recommendations.

designing association rule mining model for given data-set using apriori algorithm

implementation of Apriori algorithm using python3

This is an API implementation of our graduation project "details" using FastAPI

Identification, Recommendation, & Ranking of Occupants' Behavioral Energy - Master's Thesis

implementation of fp_growth algorithm using python3

Codes related to data warehousing and mining

UG Final Year Project based on Suffix Forest Based Tri-clustering

Market basket analysis using Apriori algorithm and association rules

An association rule extractor for 2023 NY Complaint Data.

This repository contains HWs of data mining course

This in-depth market basket analysis goes through a complete project cycle towards extracting valuable insights that the business can implement allowing them to scale. From preprocessing the data, to exploratory data analysis, association rule mining, interpretation and insights, and recommendations. This project was made to tackle these problems.

A data mining study was conducted to determine the correlations between factors associated with high and low suicide rates in countries worldwide. Pandas and mlxtend were used in Python, as well as the data mining program Rapidminer.