Customer Churn Prediction

Table of Contents

1. Installation
2. Project Motivation
3. File Descriptions
4. Results
5. Licensing, Authors, and Acknowledgements

Installation

The code was tested using Python version 3.9. and Pyspark 3.1.2 . For other necessary libraries please use requirements.txt

pip install -r requirements.txt

Project Motivation

Sparkify is the fictitious music service similar to Spotify where the users can have either a free account with advertisement or paid account without. The log contains the information about user interaction with the service e.g., number of items per session, artist/ songs they are listened to or information about the device etc.

This project aim is to analyze the dataset containing user churns in order to identify the customer who are dissatisfied with the service before they cancel the subscription.

For this project, it was of interest in investigation of :

1. Which factors have impact on the cancelling the subscription?
2. How well can we predict customer churn?

The analysis, data exploration as well as building/ tuning machine learning models involves Pyspark.

File Descriptions

The repository contains one notebook with all necessary tools and steps.

• Load Data into Spark and Clean Dataset
• Exploratory Data Analysis
• Feature Engineering
• Modeling
• Churn Prediction

The analysis was limited to data subset. The data was provided by Udacity and unfortunately couldn’t be uploaded due to the size.

Results

The baseline is to compare different ML algorithms based on their default settings except to include the column classWeightCol to re-balance the weights

Model	f1_score	Area Under ROC	accuracy	time ms
LogisticRegression	0.52	0.43	0.5	309
LogisticRegression + Threshold	0.37	0.43	0.37	-
LinearSVC	0.52	0.39	0.5	3257
RandomForest	0.6	0.59	0.67	79
Gradient-boosted Tree	0.66	0.51	0.7	852

Parameter Tuning was done for all above models except LinearSVC due to the fact that this algorithm achieved the same results as LogisticRegression but the training last 10 times longer.

Model	f1_score	Area Under ROC	accuracy
LogisticRegression Test Set	0.49	0.46	0.47
LogisticRegression Validation Set	0.72	0.53	0.7
RandomForest Test Set	0.56	0.5	0.67
RandomForest Validation Set	0.74	0.52	0.8
Gradient-boosted Tree Test Set	0.71	0.48	0.73
Gradient-boosted Validation Test Set	0.58	0.41	0.57

The main findings of the code can be found at the post available here.

Licensing, Authors, Acknowledgements

Great thanks to Udacity for their contribution during the process.

Copyright (C) 2021 August

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