This is the final project for the Data Scientist Nanodegree, where our goal is to predict churn for a fictional streaming service called Sparkify.

Churn Prediction using PySpark

Cloud Data Warehouse of Sparkify Data using Redshift

Students will build an ETL pipeline that extracts data from S3, stages them in Redshift, and transforms data into a set of dimensional tables for their analytics team.

This Git repo showcases my analysis of Sparkify dataset with PySpark on Apache Spark cluster mode and JupyterLab on Docker. The goal was to identify at-risk customers and develop retention strategies. The analysis tested multiple machine learning models and uncovered insights into customer behavior and churn patterns.

Data Analysis in Spark to Identify Customer Churn for a fictional music service.

a ETL pipeline for extracting data from s3, staging themon Redshift and transforming them into fact and dimensional tables for song play analysis

Sparkify project for predicting customer loyality.

An ETL model designed using Postgres SQL for Sparkify database 🗄, modeling user activity data to create a database and ETL pipeline🔀 for a music streaming app 🎼.

Udacity Data Engineer Nanodegree: Project Data Lake

Project: Data Modeling with Cassandra

ETL script for reading data from s3, processing them using Spark and loading them back to s3 for data analysis team