https://github.com/nel-zi/nuga_bank
Developed an automated data exploration and cleaning pipeline for Nuga Bank to streamline data preparation, ensure consistent data quality, and normalize datasets into structured databases for efficient analysis and reporting.
https://github.com/nel-zi/nuga_bank
data data-automation data-visualization datacleaning datatransformation etl-automation etl-pipeline
Last synced: about 1 year ago
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Developed an automated data exploration and cleaning pipeline for Nuga Bank to streamline data preparation, ensure consistent data quality, and normalize datasets into structured databases for efficient analysis and reporting.
- Host: GitHub
- URL: https://github.com/nel-zi/nuga_bank
- Owner: Nel-zi
- Created: 2025-01-20T15:34:25.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-21T18:38:37.000Z (over 1 year ago)
- Last Synced: 2025-02-17T20:34:31.011Z (over 1 year ago)
- Topics: data, data-automation, data-visualization, datacleaning, datatransformation, etl-automation, etl-pipeline
- Language: Jupyter Notebook
- Homepage:
- Size: 616 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Nuga_bank
-- EXECUTIVE SUMMARY --
● Nuga Bank is a leading financial institution, needing data exploration and cleaning processes
to streamline data preparation, enabling better insights and decision-making.
-- BUSINESS PROBLEM STATEMENT --
● Nuga Bank faces challenges in effectively exploring and cleaning vast
volumes of financial data, hindering its ability to derive actionable insights.
-- THE KEY ISSUES ARE --
● Inefficient manual data exploration and cleaning processes.
● Lack of scalability to handle growing data volumes.
● Inconsistent data quality leading to inaccurate reporting and analysis.
● Complexity in transforming raw data into a structured and normalized
database format.
-- OBJECTIVES --
○ Implement an automated data exploration and cleaning solution
○ Normalize the dataset into a suitable database
○ Load the cleaned and normalized dataset into a database for futther analysis