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

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.

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.

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.

un algorithme qui predit si une personne X a le corona

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.

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

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

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

This code 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.

Market Basket Analysis for Bakery using apriori rule mining

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.

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.

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

Unsupervised-ML---Association-Rules-Data-Mining-Titanic. Data Preprocessing: As the data is categorical format, we are using One Hot Encoding to convert into numerical format. Apriori Algorithm: frequent item sets & association rules. A leverage value of 0 indicates independence. Range will be [-1 1]. A high conviction value means that the conse…