mlxtend

Python project for Market Basket Analysis. Generates synthetic retail transactions, mines frequent itemsets using Apriori & FP-Growth, derives association rules, and outputs CSVs + visualizations. Portfolio-ready example demonstrating data science methods for uncovering product co-purchase patterns.

Cardio Monitor is a web app that helps you to find out whether you are at risk of developing heart disease. the model used for prediction has an accuracy of 92%. This is the course project of subject Big Data Analytics (BCSE0158).

Mlxtend, Association_rules, Apriori, FP Growth

This repository shows the analysis of different mining models and algorithms.

An association rule learning-based product recommendation system is desired to be created using the dataset containing users who received services and the categories of services they received.

Association-Rules-Data-Mining-Books. Apriori Algorithm, Association rules with 10% Support and 70% confidence, Association rules with 20% Support and 60% confidence, Association rules with 5% Support and 80% confidence, visualization of obtained rule.

This project performs association analysis on a sales dataset, using the Apriori algorithm. The dataset is loaded from an Excel file, and a basket of items is created for each transaction. The Apriori algorithm is then applied to find frequent itemsets and association rules based on the support, confidence, and lift metrics.

un algorithme qui predit si une personne X a le corona

A pipeline for market basket analysis aimed at identifying product associations to optimize retail promotions and bundle deals using SQLite, mlxtend & D3.js

In This Notebook I've built an Association rules Recommendation system, that make relations between itemsets and recommend the items that related to what user purchased.

Implementation of Apriori, FP-Growth, and ECLAT algorithms on natural language data

Running MLflow experiments

"SmartCart" is a cutting-edge e-commerce tool 🌟 that leverages predictive analytics to provide personalized shopping recommendations and optimize inventory management, tailored for businesses aiming to enhance customer engagement and sales 🚀.

Предоставлен файл с сервера. Вам нужно спарсить его содержимое, создать базу данных под данные, вставить данные в базу данных, удаленно подключиться к базе данных и проанализировать данные.

Apriori Algorithm Association rules with 10% Support and 70% confidence Association rules with 5% Support and 90% confidence Lift Ratio > 1 is a good influential rule in selecting the associated transactions visualization of obtained rule

Infrastruktur End to End Project ini memadukan Analisis Tren Waktu (Time Series) menggunakan metode rata-rata bergerak untuk mendeteksi momentum kenaikan penjualan produk target secara beruntun, serta Market Basket Analysis (Data Mining) dengan algoritma Apriori untuk mengidentifikasi perilaku belanja silang (cross-selling) pelanggan.

Subreddit recomendation system based on association rules harvested using data mining algorithms.

Market Basket Analysis for Bakery using apriori rule mining

Using different Association Rule Mining Algorithms to establish rules between item(s) from a transactional data. 3 different algorithms were used to generate itemsets and generate candidate rules from them based on certain metrics. Link to the dataset is given below.