Some experiments about Machine Learning

Implementation of the Apriori and Eclat algorithms, two of the best-known basic algorithms for mining frequent item sets in a set of transactions, implementation in Python.

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.

Analytics and Systems of Big Data

"Frequent Mining Algorithms" is a Python library that includes frequent mining algorithms. This library contains popular algorithms used to discover frequent items and patterns in datasets. Frequent mining is widely used in various applications to uncover significant insights, such as market basket analysis, network traffic analysis, etc.

Implementation of Apriori and Eclat Algorithms (With CPU & GPU)

MS-Apriori is used for frequent item set mining and association rule learning over transactional data.

Market Basket Analysis using Apriori Algorithm on grocery data.

A python code, implementing the Data Mining algorithm - Apriori.

Data Mining Course Repository, 7th Semester 2023-24, IITD

Frequent Itemset Generation and Association Rule Mining

Implementation of algorithms for big data using python, numpy, pandas.

Learning embeddings for transactions via frequent itemsets, Word2Vec, and Doc2Vec

Frequent item set mining

Recommendation systems for Yelp (collaborative filtering & content-based)

Market Basket Analysis using Hadoop MapReduce in Python

Frequent Itemset Mining Using the Apriori Algorithm

An Apache Spark implementation of the Apriori algorithm to calculate the frequent item sets and association rules.