GitHub - Rubaida001/Recommendation-System-for-Netflix-Dataset: Netflix is one of the most influential media streaming services in the world. Since it has an enormous collection of movies, series and documentaries, it becomes difficult for people to select the items they want to watch. In this case, recommendation system is an option to solve the issue. The aim of this project is to create a recommendation system using Netflix movie dataset. As it is a large dataset, the main challenge of the task is managing it to provide a good solution. I have used PySpark that can handle such type of dataset.

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Netflix is one of the most influential media streaming services in the world. Since it has an enormous collection of movies, series and documentaries, it becomes difficult for people to select the items they want to watch. In this case, recommendation system is an option to solve the issue. The aim of this project is to create a recommendation s…

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README.txt		README.txt
cosine_similarity.py		cosine_similarity.py
matrix_Factorization.py		matrix_Factorization.py

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Project Name: Recommendation System for Netflix Dataset
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Files:
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 1. matrix_Factorization.py: This file contatins the code with Matrix Factorization model based on Alternate Least Squares(ALS) model.
 2. cosine_similarity.py: Code for User-based Collaborative Filtering method is included in this file.
 3. coursework_2.PDF : Report on this project with all result and explanation.
 4. train_2.csv : Review of 2000 movies.
 5. test_2.csv :  List of users and movies with respective ratings.


Run the Program:
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 1. Before runing the matrix_Factorization.py file, first upload both test_2.csv and train_csv.2 in Hadoop using following command:
				
					hadoop fs -copyFromLocal train_2.csv
					hadoop fs -copyFromLocal test_2.csv
 2. Later run that file using :
			
				spark-submit matrix_Factorization.py 1> mf_out.txt

 3. For cosine_similarity.py, put the datasets into cluster. Then run following command:
				
				python cosine_similarity.py 1> cs_out.txt