Customer churn prediction pipeline using PostgreSQL, dbt, Python, and machine learning.
- Python 93.5%
- Jupyter Notebook 6.5%
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Customer Churn Prediction
In this project, customer churn prediction is calculated using data from Kaggle. The primary personal goal is to build on and demonstrate full-stack data modeling, analytics engineering, and machine learning capabilities. While Kaggle is generally focused on machine learning techniques, my implementation focuses on a mixture of data engineering and data science.
Source: https://www.kaggle.com/competitions/kkbox-churn-prediction-challenge
Project pipeline:
- Data Extract/Load into PostgreSQL landing tables
- Data Cleaning and Feature Engineering using dbt