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Bank Customer Churn Prediction (DMML)

1. Introduction

This project focuses on implementing a data management pipeline for a bank churn prediction model. The pipeline includes data ingestion, validation, preprocessing, feature storage, model building, and orchestration using DVC and Apache Airflow.

Objectives

  • Automate the data pipeline for efficient handling of raw and processed data.
  • Ensure data quality through validation checks.
  • Store and retrieve features efficiently using PostgreSQL.
  • Automate the pipeline using Apache Airflow.
  • Implement version control using DVC to track dataset and model changes.

2. Pipeline Overview

The pipeline workflow consists of the following stages:

  1. Data Ingestion – Fetch data from Kaggle and an external API.
  2. Raw Data Storage – Store raw data in an AWS S3 bucket with timestamp-based versioning.
  3. Data Validation – Check for missing values, duplicates, and type mismatches.
  4. Data Preparation & Transformation – Clean and transform data for training.
  5. Feature Store – Store selected features in PostgreSQL.
  6. DVC for Versioning – Track data and model changes.
  7. Model Building – Train and evaluate multiple ML models.
  8. Orchestration & Automation – Use Airflow to automate pipeline execution.

3. Data Ingestion

Sources

  • Kaggle Dataset: Historical churn data via the Kaggle API.
  • External API: Provides new data for inference.

Implementation

  • Script: 1_Data_Ingestion.py
  • Local save path: data/raw/bank_churn_YYYYMMDD_HHMMSS.csv
  • Logging enabled for data download tracking.

Challenges & Solutions

  • Kaggle API authentication → Resolved via correct kaggle.json setup.
  • API rate limits → Handled using retry logic with exponential backoff.

4. Raw Data Storage

Storage Location

  • AWS S3 Bucket: dmml-bank-churn-data
  • Directory Structure:
    • raw_data/ – Original datasets.
    • reports/ – Validation reports.

Versioning Strategy

  • Timestamp-based file naming.
  • Older versions retained for reproducibility.

Challenges

  • Permission errors → Solved by updating bucket policies and IAM roles.
  • Network failures → Retry with progressive backoff.
  • Policy updates → Monitored and adjusted as needed.

5. Data Validation

Checks Performed

  • Missing values
  • Data type mismatches
  • Duplicate records
  • Outlier detection

Report Generation

  • Logs and validation reports saved in reports/.
  • Summary statistics, histograms, and box plots generated.

Screenshot Placeholder:
Validation Report

6. Data Preparation & Transformation

Feature Engineering

  • BalancePerProduct: Balance / NumOfProducts
  • Geography: One-hot encoding

Data Cleaning

  • Drop irrelevant columns.
  • Handle missing values (imputation/removal).

Storage

  • Processed data saved to processed_data/ in S3.
  • Transformed data in transformed_data/.

Challenges

  • Handled categorical variables via encoding.
  • Used statistical checks for outliers and incorrect values.
  • Unified training/API data formats with pre-processing rules.

7. Feature Store

Database: PostgreSQL

Table Schema

CREATE TABLE feature_values ( id SERIAL PRIMARY KEY, CreditScore FLOAT, Age FLOAT, Tenure INT, Balance FLOAT, NumOfProducts INT, IsActiveMember INT, Geography_France BOOLEAN, Geography_Germany BOOLEAN, Geography_Spain BOOLEAN, BalancePerProduct FLOAT, Exited INT, -- Nullable for API data data_source VARCHAR(10), -- 'train' or 'api' version TIMESTAMP -- Timestamp-based versioning )

8. DVC for Versioning

  • DVC is used to track raw data, processed data, features, and trained model files.
  • Remote storage is configured using AWS S3 to store versioned artifacts.
  • Enables reproducibility by allowing restoration of any previous version of data or models.
  • Integrates seamlessly with Git for full pipeline version control.

9. Model Building

  • Multiple machine learning models were trained and evaluated.
  • Feature selection and correlation analysis were performed prior to training.
  • Models such as Logistic Regression and Random Forest were used.
  • Performance metrics (accuracy, precision, recall, F1 score) were computed.
  • The best-performing model was saved for downstream inference.

10. Orchestration & Automation

  • Apache Airflow was used to automate the entire pipeline.
  • Separate DAGs were created for data ingestion, validation, transformation, and model training.
  • Tasks are modular and scheduled for periodic execution.
  • Retry mechanisms and logs are included for failure handling and monitoring.

11. Conclusion

  • The project successfully implemented a full ML pipeline for bank churn prediction.
  • Ensured automation, reproducibility, and version control throughout the pipeline.
  • Integrated tools like DVC, PostgreSQL, Airflow, and S3 for robust data handling.

Key Takeaways

  • Scalability: Supports retraining with new data inputs automatically.
  • Reproducibility: Pipeline outputs can be reproduced at any stage using DVC.
  • Automation: Airflow minimizes manual intervention across stages.
  • Performance: Feature engineering and model evaluation ensured optimal results.

Future Enhancements

  • Implement a real-time feature store for live inference.
  • Integrate model monitoring to detect drift.
  • Deploy the model via a web API for real-time usage.