Using Apriori Algorithm to do Market Basket Analysis of Customers purchasing behaviours. It can predict what the customer is going to buy next by looking at the products he is buying.

This repository contains my research work on building the state of the art next basket recommendations using techniques such as Autoencoders, TF-IDF, Attention based BI-LSTM and Transformer Networks

Portfolio in R

This repository consists of collaborative filtering Recommender systems like Similarity Recommenders, KNN Recommenders, using Apple's Turicreate, A matrix Factorization system from scratch and a Deep Learning Recommender System which learns using embeddings. Besides this Market Basket Analysis using Apriori Algorithm has also been done. Deployment of Embedding Based Recommender Systems have also been done on local host using Streamlit, Fast API and PyWebIO.

This is the list of resources, I used and compiled during my research and analysis phase for Recommendations systems.

A simple Market Basket Analysis that uses the apriori algorithm to find affinities between retail products

Used association ruling to find out which products were frequently bought together. Aim is to drive higher sales volume and customer retention.

Mlxtend, Association_rules, Apriori, FP Growth

The project involves conducting a thorough analysis of Point of Sale (POS) Data for providing recommendations through which a grocery store can increase its revenue by popular combo offers & discounts for customers.

Market Basket Analysis and Exploratory Data Analysis Using SQL

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

This repository contains exploratory data analysis and marketbasket analysis for an online giftstore dataset.

Complete package for all Data Science models using R. Starting form Preprocessing, Data Manipulation, Feature Engineering, Model Building, and Model Validation.

Data Mining: Market Basket Analysis with Apriori Algorithm

A repository focusing on implementing Market Basket Analysis using the Apriori Algorithm in Python, providing insights into customer purchasing behaviour.

1. Diabetes Prediction Using Ensemble Techniques 2. Customer Segmentation Using RFM & K-Means 3. Market Basket Analysis

Based on information from historical transactions, as well as from customer and product meta data, tried to offer customers with personalized fashion recommendations tailored specifically to their preferences.

Data mining technique used to uncover associations between items that customers frequently purchase together.

To answer which items are frequently bought together we will be using Apriori & FPgrowth Algorithm

Association Rule Mining: Apriori Algorithm

Recommendation systems for e-commerce sites

Association Rules

Análise de Cesta de Mercado (Market Basket Analysis) utilizando o algoritmo Apriori para identificar regras de associação entre produtos em um conjunto de transações de supermercado.

We consult NTBO to study the market through UGC, using the CRISP-DM model. Our analysis compares visitor patterns at Portuguese attractions with other countries, providing valuable insights for informed decision-making.

MERN-based ML-Enhanced E-Commerce Platform for Gifting Products

My Data Science Research Project for Master in Data Science at Universiti Malaya

This repository is the continuous assessment for CCT College Dublin integrating the modules course (Data Visualization Techniques and Machine Learning). The focus is on the implementation of recommendation systems, market basket analysis, and the creation of an interactive dashboard using Python.

No description provided.

A simple Ionic + Angular Barcode Scan app for grocery stores backed with RESTful Web Services on Spring Boot - Participant of App Challenge 2020 - Outcome of Enterprise Mobile Application Development Master's Degree Course @ UniSA

Data Analytics