A library of extension and helper modules for Python's data analysis and machine learning libraries.

Recommender Systems Code Exploring Concepts with Surprise and Mlxtend Packages - Code for my blog article

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.

Model stacking example on toy dataset using XGBoost, LightGBM and more, combined with mlxtend model stacking.

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).

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Mlxtend, Association_rules, Apriori, FP Growth

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

Implementation of ensemble methods for classification and regression using scikit-learn, XGBoost, and mlxtend. Covers bagging, boosting, random forests, voting, and stacking on Bank Marketing and Boston Housing datasets with evaluation metrics.

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.

mlxtend ths library used to impor different datasets

Machine Learning - Stacking through Voting Classifier, Mlxtend, Vecstack

Extraction of frequent itemsets and association rules in Python. Using the "mlxtend" library.

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.

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

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

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

Analyze footwear e-commerce data with EDA, predictive modeling (XGBoost), clustering (K-Means), anomaly detection, fairness analysis, and association rule mining using Python. Utilizes Pandas, Scikit-learn, SHAP, and MLxtend to uncover pricing trends, product insights, and market patterns for retailers and analysts.

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.

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.

Pandas, mlxtend, apriori algo

Using scikit-learn and mlxtend

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Auto machine learning framework based on sklearn, mlxtend, etc.

Association Analysis using Apriori Algorithm (Python: Pandas, Numpy, Mlxtend)

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Eksplorasi association rule dataset Covid-19 menggunakan FP-Growth mlxtend.

Market Basket Analysis for Bakery using apriori rule mining