https://github.com/bomada/sparkify

This project is the final Capstone project of the Udacity Data Scientist Nanodegree program. The aim is to learn how to manipulate realistic datasets with Spark to engineer relevant features for predicting churn. Input data is related to the fictive music streaming service Sparkify (similar to Spotify and Pandora).
https://github.com/bomada/sparkify

churn ml music portfolio python spark streaming

Last synced: 11 days ago
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This project is the final Capstone project of the Udacity Data Scientist Nanodegree program. The aim is to learn how to manipulate realistic datasets with Spark to engineer relevant features for predicting churn. Input data is related to the fictive music streaming service Sparkify (similar to Spotify and Pandora).

• Host: GitHub
• URL: https://github.com/bomada/sparkify
• Owner: Bomada
• License: mit
• Created: 2019-08-09T10:59:36.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-12-07T23:53:56.000Z (about 2 years ago)
• Last Synced: 2023-03-04T03:45:07.065Z (almost 2 years ago)
• Topics: churn, ml, music, portfolio, python, spark, streaming
• Language: Jupyter Notebook
• Size: 126 KB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 29
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Sparkify Project

This project is the final Capstone project of the Udacity Data Scientist Nanodegree program. The aim is to learn how to manipulate realistic datasets with Spark to engineer relevant features for predicting churn. Input data is related to the fictive music streaming service Sparkify (similar to Spotify and Pandora).

# Table of Contents

1. [Installation](#installation)

2. [Project Motivation](#motivation)

3. [File Descriptions](#files)

4. [Project Process](#process)

5. [Result Summary](#result)

6. [Licensing, Authors, Acknowledgements](#licensing)

# Installation 

The code should run with no issues using Python versions 3. See the requirements.txt file for details about library versions.

# Project Motivation 

Since I'm a long time user of the Spotify streaming service I've wanted to test out some ideas for a while. In this project I could work with similar data and also try out Spark which is new to me.

# File Descriptions 

- **mini_sparkify_event_data.json**: Fictious music streaming data provided by Udacity. This file is not available in the repository due to it's size.

- **sparkify.ipynb**: Exploratory notebook with all steps necessary to build a model that predicts churn for the Sparkify data.

# Project Process 

When working with this project I followed the CRISP-DM process. The details are found in the notebook and also on this blog post:

https://medium.com/@marcusnilsson78/sparkify-project-write-up-5dfbaac36cc6

# Result Summary 

The model got a F1 score in validation of 46.2% which is not that good. As the dataset available was very small, it's hard to trust the model in it's current state. It should be trained and validated further on a greater dataset. Please see the blog mentioned above to read the complete results.

# Licensing, Authors, Acknowledgements 

## Code

See details in licensing file.

## Data

Data in the .json file cannot be used without consent from Udacity.

## Acknowledgements

Besides the Udacity training material, the following resources were of much help in this project:

- https://mapr.com/blog/predicting-breast-cancer-using-apache-spark-machine-learning-logistic-regression/

- https://stackoverflow.com/questions/36132322/join-two-data-frames-select-all-columns-from-one-and-some-columns-from-the-othe

- https://stackoverflow.com/questions/38664620/any-way-to-access-methods-from-individual-stages-in-pyspark-pipelinemodel?rq=1