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.

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.

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

Analyse d'un jeu de données de transactions d'une boutique

Market basket analysis using Apriori algorithm - find product associations to improve cross-selling, bundling, and recommendation strategies.

ENSAE-ENSAI Formation Continue (Cepe)/OpenClassrooms Data Analyst 2022-2023 - Projet 6

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.

Production-grade Market Basket Analysis platform with FP-Growth association rule mining, customer segmentation, and real-time recommendations. Interactive Streamlit dashboard with multi-industry applications. 105K+ rules, RFM analysis, CLV estimation.

Retail analytics pipeline using Apriori & FP-Growth to uncover product associations from 10,000 transactions, with network visualizations and a business strategy proposal.

Complete association rule mining on Adult Census dataset using FP-Growth — with both mlxtend (efficient) and full from-scratch implementation. Includes data exploration, preprocessing, binarization, and insightful rules on income patterns.

Messing around with Databases Dimensionality Reduction and classification using Multi Layers Perceptron. (simple academic research)