📈 Data Automation Demo: Messy CSV to Clean SQL

This repository demonstrates the process of taking inconsistent, unstructured data from a client-provided CSV file and transforming it into clean, reliable SQL INSERT statements ready for direct database ingestion.

💡 Problem Solved

Clients often receive data from different sources (marketing tools, legacy systems, external vendors) with inconsistent formatting:

• Mixed date formats (MM/DD/YY vs. DD-MM-YYYY vs. YYYY-MM-DD)
• Non-standard email addresses (AT instead of @)
• Unnecessary text prefixes in IDs (ID1234)
• Inconsistent number formatting.

⚙️ The Solution (data_cleaner.py)

This Python script, using the pandas library, performs the following cleaning steps:

1. Date Standardization: Converts mixed date formats into a unified YYYY-MM-DD standard.
2. Email Standardization: Uses regex to replace non-standard characters (e.g., AT) with the standard @.
3. ID Normalization: Removes text prefixes (ID) and converts the field to a clean integer.
4. Price Cleaning: Ensures all price data is a reliable float (.00) format.

▶️ Output Example

The script's final output is a series of clean SQL statements ready to be executed:

INSERT INTO customer_orders (customer_name, customer_email, order_id, total_price, sale_date) VALUES ('ACME, Inc.', 'support@acme.com', '9876', '123.55', '2025-12-14');
INSERT INTO customer_orders (customer_name, customer_email, order_id, total_price, sale_date) VALUES ('Globex CORP', 'globex@corp.org', '5544', '321.00', '2025-10-12');
-- etc.