Movielens dataset analysis for movie recommendations using Spark in Azure

Movielens dataset analysis for movie recommendations using Spark in Azure

In this Databricks Azure tutorial project, you will use Spark Sql to analyse the movielens dataset to provide movie recommendations. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.
explanation image


Each project comes with 2-5 hours of micro-videos explaining the solution.

ipython image

Code & Dataset

Get access to 50+ solved projects with iPython notebooks and datasets.

project experience

Project Experience

Add project experience to your Linkedin/Github profiles.

Customer Love

Read All Reviews
profile image

Hiren Ahir linkedin profile url

Microsoft Azure SQL Sever Developer, BI Developer

I'm a Graduate student and came into the job market and found a university degree wasn't sufficient to get a good paying job. I aimed at hottest technology in the market Big Data but the word BigData... Read More

profile image

Dhiraj Tandon linkedin profile url

Solution Architect-Cyber Security at ColorTokens

My Interaction was very short but left a positive impression. I enrolled and asked for a refund since I could not find the time. What happened next: They initiated Refund immediately. Their... Read More

What will you learn

Understanding the problem statement & Microsoft Azure Platform
Developing end to end data pipeline using Microsoft Azure and Databricks Spark
Getting Microsoft Azure Subscription
Creating Resource Group
Introduction to Storage Account
Upload raw data to cloud
Introduction to Azure Data Factory
Create and run ADF pipelines
Introduction to Azure Databricks
Spinning up Databricks cluster
Read data from storage account
Writing Spark Sql on Databricks
Data analysis using spark on Databricks
Data cleansing using spark on Databricks
Data Transformation using spark
Movie Recommendation algorithm using Spark in Azure
Model deployment creating FlaskAPI

Project Description

A movie recommendation system is used by top streaming services like Netflix, Amazon Prime, Hulu, Hotstar etc to recommend movies to their users based on historical viewing patterns.

Before the final recommendation is made, there is a complex data pipeline that brings data from many sources to the recommendation engine. In this project, we use Databricks Spark on Azure with Spark Sql to build this data pipeline.

Our dataset is from GroupLens Research, which is a research group in the Department of Computer Science and Engineering at the University of Minnesota. They operate a movie recommender based on collaborative filtering called MovieLens. This dataset (ml-latest) describes 5-star rating and free-text tagging activity from MovieLens, a movie recommendation service. It contains 22884377 ratings and 586994 tag applications across 34208 movies. These data were created by 247753 users between January 09, 1995 and January 29, 2016. This dataset was generated on January 29, 2016.

Similar Projects

In this big data project, we will embark on real-time data collection and aggregation from a simulated real-time system using Spark Streaming.

Use the dataset on aviation for analytics to simulate a complex real-world big data pipeline based on messaging with AWS Quicksight, Druid, NiFi, Kafka, and Hive.

In this hadoop project, we are going to be continuing the series on data engineering by discussing and implementing various ways to solve the hadoop small file problem.

Curriculum For This Mini Project

Signing Up To Microsoft Azure Cloud
Create A Resource Group In Azure
Setting Up Azure Storage Account
Uploading Raw Data
Setup Azure Data Factory
Run The Adf Pipeline
Introduction To Azure Databricks
Setting Up A Cluster In Azure Databricks
Authorise Storage Account In Databricks
Reading Data From Databricks
Exploring The Dataset Using Pyspark
Data Transformation And Analysis Using Pyspark
Pyspark Data Analysis - 1
Pyspark Data Analysis - 2