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adult-data-set-classification-and-classifier-comparison

In this program we apply machine learning principals to predict weather income exceeds $50k per year on the Adult data set. We use the four techniques to achieve better performance which includes choosing appropriate classifier, preprocessing techniques, parallel infrastructure and external libraries. This program will focus on proper use of each classifier by fine tuning the hyperparameter to achieve the best results, the classifiers include SVM, KNN, Random Forest, Gaussian Naïve Bayes. The preprocessing techniques used to eliminate noise and inconsistency of data are standard scaler, label Encoder and quantile transformer. The best accuracy was achieved by random forest, this Classifier outperforms every other classifier as it makes multiple decision tree which prevents overfitting.

amazonproductrecommendation-cf-als-spark

We have chosen Amazon product sales data set comprising of sales activity and user ratings for each product. The idea is to create a product suggestion/recommendation system for each user based on his previous purchases and his rating for each one. A Collaborative Filtering model is built to predict the virtual ratings for the product that the user did not purchase. The system predicts the user rating for all the items and we display the products which user may be like, buy and rate higher.

apriori

Python Implementation of Apriori Algorithm for finding Frequent sets and Association Rules

apriori-algo-association-rules

Recommender Engine based on apriori algo

big-data-automation

This is a Movie Recommender System built on both Hadoop and Spark using MovieLens 10 million ratings dataset. The project's deployment has been automated using Ansible.

boston

Predicting prices using linear regression on The Boston Housing Dataset

cf-nade

A implementation of CF-NADE. Yin Zheng, et. al. "A Neural Autoregressive Approach to Collaborative Filtering", accepted by ICML 2016.

ffm

finding-frequent-itemsets

Mining association rules using the A priori algorithm and a standalone Spark cluster

movie-recommendation-engine

Recommendation engine developed in apache spark with movielens latest dataset.

movie-recommendation-system

A Spark project using MovieLens small dataset for movie recommendation system

movie-recommendation-system-using-apache-spark

Movie Recommendation System using Apache Spark and Python implementing User based Collaborative Filtering and Item Based Collaborative Filtering Algorithms

movie-recommender

Movie Recommender system using association rules

movie_recommendation_system

Launched a distributed application using Spark and MLlib ALS recommendation engine to analyze a complex dataset of 10 million movie ratings from MovieLens.

movielens

使用 Spark MLlib 的 ALS 算法的电影推荐系统

movielens-recommender

A pure Python implement of Collaborative Filtering based on MovieLens' dataset.

movielensrecommendation

Built recommender engine for MovieLens dataset using Spark to learn user preferences based on various factors like ratings, users and items

neural_collaborative_filtering

Neural Collaborative Filtering

parallel-frequent-itemset-mining-lastfm360k

Hadoop streaming implementation of Li, et al: "PFP: Parallel FP-Growth for Query Recommendation", applied to the lastfm360k dataset

parllel-fp-growth

this project contains the implementation of the parllel fp growth using spark

pyspark-recommendation-demo

movie recommendation demo using collaborative filtering and lfm(spark mllib ALS)

python-fp-growth

An implementation of the FP-growth algorithm in pure Python.

recommender-engine

MovieLens Recommender Engine - Apache Spark™ for Data Analytics and ML

recommender-system

This program implements Apriori Algorithm in python3 using mlxtend library

recommender-system-using-apache-spark-mllib-

Implementation of Recommender Systems (RS) using Apache Spark MLlib on movielens dataset

similarity-user-based

Code used in paper Analysis of Similarity Measures for Collaborative Filtering Recommendation, presented in ISCCDA 2017, NIE, Mysuru.

spark-movie-lens

An on-line movie recommender using Spark, Python, and the MovieLens dataset

ts-ppr

Codes for IEEE TKDE 2016 paper: Recommendation for repeat consumption from user implicit feedback