Data Science Project–Learn to build the Best Recommendation Engine

Build a recommendation engine by working on a data science project based on Kaggle data science competition - WSDM - KKBox's Music Recommendation.

Data Science Project–Learn to build the Best Recommendation Engine
 |  BY ProjectPro

Any discussion in the world of data science and machine learning is incomplete without the mention of prediction and recommendation engines. Recommendation engines have become the shining star of big data.  Building a recommendation engine is at the heart of modern marketing with user level personalization becoming the secret to success for media and retail business domains. It is extremely important for data scientists to follow the right approach when building a recommendation engine as it is a big investment for any organization both technically and financially.


Recommender System Machine Learning Project for Beginners-1

Downloadable solution code | Explanatory videos | Tech Support

Start Project

Data Science Project Problem Statement for WSDM - KKBox's Music Recommendation Challenge

Data Science Projects

With unlimited music streaming services, it has become important to personalize algorithms, however this would be possible only if there is enough historical data. Currently KKBOX uses a collaborative filtering music recommendation engine but aims to employ novel data science techniques which could lead to better results. The new recommendation algorithm would know if a listener likes a new artist or a new song and what kind of song to recommend to brand new users.

Target Audience for the Data Science Project based on the Kaggle Data Science Challenge WSDM - KKBox's Music Recommendation –

Get Closer To Your Dream of Becoming a Data Scientist with 70+ Solved End-to-End ML Projects

Pre-requisites for WSDM - KKBox's Music Recommendation Data Science Project –

  • Good Knowledge of basic data science concepts.
  • Good grasp of Python or R data science libraries. If not, take the data science course in Python or R programming before enrolling for this data science project.
  • All tools used in this data science project are free and easily available on the web.

Recommended Reading:  

What will you learn from this data science project?

Working on data science projects is an important milestone in the journey towards becoming an enterprise data scientist. This data science project aims to help data scientists/data analysts learn how to build a recommendation engine with the end goal of reducing churn, enhancing user experience, and increasing profitability for business success.

  • Learn to implement music recommender system using both Python and R data science programming languages.
  • You will learn the usage and implementation of below mentioned data science libraries in R programming language -
  1.  reshape
  2. reshape2
  3.  xgboost
  4. caret
  5. jsonlite
  6. Matrix
  7. dplyr
  8. lubridate
  1. numpy
  2. seaborn
  3. pandas
  4. matplotlib
  5.  ggplot, plotting system for python based on R’s ggplot2.
  • Learn how to tune algorithm parameters to build an optimal algorithm.
  • Learn to explore data (EDA) using various data visualization techniques.

Access to a curated library of 250+ end-to-end industry projects with solution code, videos and tech support.

Request a demo

About the KKBOX Dataset

You will work with Asia’s leading music streaming brand KKBOX dataset that provides over 40 million tracks at your fingertips having over 10 million members across the region. The training dataset contains information on the first observable listening event for every user-song pair in a given time duration. The dataset also provides metadata information for every user and song pair.

So, what are you waiting for?

Enrol now to learn how to build the best music recommendation engine using KKBOX Dataset.

Check out other interesting Big Data Projects and Data Science Projects to build your project portfolio.

 

PREVIOUS

Access Solved Big Data and Data Projects

About the Author

ProjectPro

ProjectPro is the only online platform designed to help professionals gain practical, hands-on experience in big data, data engineering, data science, and machine learning related technologies. Having over 270+ reusable project templates in data science and big data with step-by-step walkthroughs,

Meet The Author arrow link