SolanaO / Customer_Churn_Prediction

Binary classification project in PySpark on an AWS-EMR cluster to predict customer churn.

User Activity Based Churn Prediction With PySpark on an AWS-EMR Cluster

There are two Medium blogs related to this project:

• User Activity Based Churn Prediction With PySpark on an AWS-EMR Cluster
• Did Stacking Improve My PySpark Churn Prediction Model?

Table of Contents

• General Information
• AWS-EMR Cluster Setiings
• Local Installation Setup
• Screenshots
• Project Structure
• Specifications
• Acknowledgements

General Information

In the present project, we are investigating and predicting churn for a fictional music platform called Sparkify. This is a binary classification problem, in which the algorithm has to identify which users are most likely to churn.

AWS-EMR Cluster Settings

To train the full dataset I used an AWS-EMR cluster with the following configurations:

• release label: emr-5.33.1
• applications: PySpark 2.4.7, Livy 0.7.0
• instance type: m5.xlarge
• number of instances: 7 (1 master, 6 cores)
• bootstrap: emr_bootstrap_modeling.sh
• configuration file: emr_configuration_modeling.json

The project was run on EMR Notebooks with PySpark kernels.

Local Installation Setup

The code is written on Anaconda Jupyter Notebook with a Python3 kernel. Additional libraries and modules used:

• PySpark 3.1.2
• Pandas 1.3.4
• Numpy 1.21.2
• Matplotlib 3.5.0
• Seaborn 0.11.2

Full packages and libraries list to set up an environment can be found in the requirements.txt file.

Screenshots

The structure of the raw data:

Users' activity can be measured by the number of likes and dislikes, upgrades and downgrades of the service:

The Kendall correlation rank, displayed below as a heatmap, assists in determining the feature relevance for modeling:

The performance of the two best models: Gradient Boosted Trees and the Multilayer Perceptron are displayed using ROC and PR curves:

Table to compare the preformance metrics of the Multilayer Perceptron and of the Meta Classifier Linear Regression model:

Project Structure

Two datasets were used for this project, both too large to store on Github. The notebooks named Sparkify_Small_Data refer to work done on the small dataset of 128 MB. The notebooks named Sparkify_Full_Data refer to work done with the full dataset of 12 GB.

The specification AWS means that the notebook is downloaded from AWS-EMR and has a PySpark kernel.

There are three independent versions of the project:

• V1 - the data is preprocessed and modeled by 5 classifiers, each classifier is fitted with 5-fold cross validation and the hyperparameters are tuned via GridSearch, the best hyperparameter combination for each model is evaluated on the test set;

• V2 - the data is processed as in V1, several features are removed to eliminate redundancies, there are 6 classifiers that are spot checked on the train set using 5-fold cross validation and default parameters, the best two classifiers (GBT and MLPC) are fine tuned with GridSearch and evaluated on the test set;

• V3 - the preprocessed data is modeled using a stacked model that consists of 6 classifiers as base predictors and a Linear Regression meta-classifier.

---

├──LICENSE
├──README.md         <- The top-level README for developers.
│
├──notebooks
    ├──fullDataNotebooks
        ├──Sparkify_Full_Data_AWS_V1.ipynb
        ├──Sparkify_Full_Data_Description_AWS_V2.ipynb
        ├──Sparkify_Full_Data_Wrangling_V2.ipynb
        ├──Sparkify_Full_Data_AWS_V2.ipynb
        ├──Sparkify_Full_Data_Stacking_AWS_V3.ipynb
        ├──Sparkify_Full_Data_Stacking_AWS_V4.ipynb
    ├──fullDataNotebooks
        ├──Sparkify_Small_Data_Local_V1.ipynb
        ├──Sparkify_Small_Data_Description_V2.ipynb
        ├──Sparkify_Small_Data_Wrangling_V2.ipynb
        ├──Sparkify_Small_Data_Modeling_V2.ipynb
        ├──Sparkify_Small_Data_Stacking_V3.ipynb
│
├──reports
    |── Churn_Prediction_Report.html  <- Report of the project (V2).
    |── Churn_Prediction_Report.pdf  <- Report of the project (V2).
    |── Churn_Stacking_Report.html      <- Report of the project (V3).
    |── Churn_Stacking_Report.pdf      <- Report of the project (V3).
    |── References.html               <- List of sources for the project.
    |── References.pdf               <- List of sources for the project.
    |
    ├──fullDataReports - static versions of notebooks
        ├──Sparkify_Full_Data_AWS_V1.html
        ├──Sparkify_Full_Data_Description_AWS_V2.html
        ├──Sparkify_Full_Data_Wrangling_V2.html
        ├──Sparkify_Full_Data_AWS_V2.html
        ├──Sparkify_Full_Data_Stacking_AWS_V3.html
    ├──fullDataNotebooks - static versions of notebooks
        ├──Sparkify_Small_Data_Local_V1.html
        ├──Sparkify_Small_Data_Description_V2.html
        ├──Sparkify_Small_Data_Wrangling_V2.html
        ├──Sparkify_Small_Data_Modeling_V2.html
        ├──Sparkify_Small_Data_Stacking_V3.htm
|          
|──scripts
    ├──churn_evaluators_script.py      <- Functions to build model evaluators.
    ├──churn_modeling_script.py        <- Pipelines and functions for modeling.
    ├──churn_prepdata_script.py        <- Clean and prepare data functions.
    ├──emr_bootstrap_modeling.sh       <- Bootstrap file for AWS cluster.
    ├──emr_configuration_modeling.json <- Configuration file for AWS cluster.
│  
├── images            <- Generated graphics to be used in reporting.
│  
├── requirements.txt  <- File for reproducing the local environment.
|
├── References.md     <- List of sources used in the project.
|
├── .gitignore        <- Files to be ignored by Git.
└──

Specifications

This is an extended version of the capstone project I completed for the Data Scientist Nanodegree with Udacity.

Acknowledgements

Many thanks to Udacity who suggested the problem and provided two interesting datasets.