Machine learning for beginner(Data Science enthusiast)

The objective of this project is to analyze the 3 million grocery orders from more than 200,000 Instacart users and predict which previously purchased item will be in user's next order. Customer segmentation and affinity analysis are done to study customer purchase patterns and for better product marketing and cross-selling.

rnn based model for recommendations

kNN-based next-basket recommendation

Includes top ten must know machine learning methods with R.

🍊 :package: Frequent itemsets and association rules mining for Orange 3.

About Next Basket Recommendations Based on Neural Network.

Use Instacart public dataset to report which products are often shopped together. 🍋🍉🥑🥦

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.

Market Basket Analysis with Recommendation Algorithms & Shiny App Implementation of a Product Recommendation System for an Online Retailer

An Interactive Approach to Understanding Unsupervised Learning Algorithms

Grocery Recommendation on Instacart Data

Discover hidden patterns and relationships in unstructured data with Python

Hadoop MapReduce implementation of Market Basket Analysis for Frequent Item-set and Association Rule mining using Apriori algorithm.

A Recommender system that predicts your next order based on your previous purchases. Also, it discuss the association between product purchases.

Apriori for association rule mining with Python bindings 🦀🐍

Simple python implementation of Apriori Algorithm to extract association rules from a given set of transactions

Frequent Itemsets via Apriori Algorithm Apriori function to extract frequent itemsets for association rule mining We have a dataset of a mall with 7500 transactions of different customers buying different items from the store. We have to find correlations between the different items in the store. so that we can know if a customer is buying apple, banana and mango. what is the next item, The customer would be interested in buying from the store.

[ 전공 프로젝트: 분석 프로그래밍 ] L사의 고객 세분화를 통한 맞춤형 상품 추천

Market Basket Analysis What is it? Market Basket Analysis is a modelling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items. For example, if you are in an English pub and you buy a pint of beer and don't buy a bar meal, you are more likely to buy crisps (US. chips) at the same time than somebody who didn't buy beer.

This repo contains my market basket analysis project in Python.

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.

Data analysis about Brazilian e-commerce business Olist

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Agile software development for a WebApp that prompts users to search, purchase products, and then recommends two items frequently bought together.

E-commerce and Sales Management Platform with Customer Analysis and Forecasting

Market Basket Analysis using Apriori Algorithm on grocery data.

This comprehensive dataset is a goldmine for data scientists, analysts, and researchers interested in exploring a wide range of topics within the realm of online retail. It encompasses a rich collection of customer behavior and characteristics, making it a versatile resource for tackling multiple aspects of data analysis and prediction.

Basket Analysis

Market basket analysis of retail and movie datasets using brute force and apriori algorithm