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).

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.

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.

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

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.

un algorithme qui predit si une personne X a le corona

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.

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

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.

Running MLflow experiments

Pattern Recognition and Machine Learning based Assignments and Labs, under Prof. Richa Singh in Course CSL2050.

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.

Assignment-09-Association-Rules-Data-Mining-my_movies. 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.

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

Market Basket Analysis using Python, pandas, and mlxtend. Uncovers frequent itemsets and association rules from grocery transaction data. Includes visualizations.

"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 🚀.