joekibz / moringa-wk10

May 2023 - Pyspark | MLFlow - Databricks | Spark streaming ...

moringa-wk10

May 2023 - Pyspark | MLFlow - Databricks | Spark streaming ...

Monday wk10 - Project - Machine learning with PySpark

Background Information

Customer churn is a significant challenge in the telecom industry. Identifying customers who are likely to churn is crucial for implementing proactive measures to retain them. By leveraging PySpark, we can take advantage of its distributed computing capabilities to handle large volumes of data efficiently and build an accurate machine learning model for churn prediction.

Problem Statement

The goal of this project is to develop a machine learning model using PySpark that accurately predicts customer churn in a telecom company. The model should achieve a minimum accuracy of 0.8, enabling the company to proactively identify and retain customers at risk of leaving. By effectively predicting churn, the company can implement targeted retention strategies, reduce customer attrition, and improve overall business performance.

Solution:

Ipynb notebook - Telco_Customer_Churn_Pyspark.ipynb
Python file - telco_customer_churn_pyspark (2).ipynb
Note: There are some matplotlib charts, ipynb notebook recommended