From data preprocessing to deep learning

This project is a Market Basket Analysis App that analyzes customer purchase patterns to generate association rules and offer personalized product recommendations.

Collaborative Filtering based Recommender for books

Using the apriori association rule learning algorithm to identify goods commonly associated and purchased together.

Market Basket Analysis on transactions information of a cafe using Associative Rule Learning/ Apriori

This repository consists of the code files of th ML algorithms which I have implemented during the machine learning course.

Technologies and tools for big data analysis

Ülkelere göre birliktelik kuralları çıkarmak için tasarlanmış Python projesi; veri manipülasyonları, tanımlayıcı veri analizi, görselleştirme, veri ön-işleme ve birliktelik analizi adımlarını içerir.

By examining the products that customers purchase together, we will provide recommendations to similar shoppers. This is a data mining approach that can be used to enhance customer satisfaction and increase sales.

Armut, Turkey's largest online service platform, connects service providers with customers looking for services such as cleaning, renovation, and transportation. Armut aims to create a product recommendation system using Association Rule Learning based on customer service usage and categories.

Association Mining Deployment as an API Web Application

This is a collection of different machine learning algorithms that I have been working on during my master's program show casing my knowledge of different ML algorithms

Association Rule Learning with Apriori.

In this repository, we will explore apriori and eclat algorithms of association rule learning models for market basket optimization.

Reading and Exploring Dataset in Jupyter or Google Colab using Python. Training the Apriori Model on the dataset. Viewing the results as a pandas dataframe (Apriori and Eclat)

Wolfram Language (aka Mathematica) paclet for association rule learning.

Association Rule Learning

This repository is based on the lecture '고객데이터와 딥러닝을 활용한 추천시스템 구현'

This project is based on the desire of Armut, an online service platform, to create a product recommendation system with Learning Association Rule by using the data set containing the service users and the services and categories these users have received.

Association Rule Learning project on online_retail_II dataset, you can read the readme file for the details of the project. You can find the link of the dataset in the codes.