This project focuses on cleaning and optimizing a raw dataset stored in a MySQL database to improve data quality, consistency, and usability for analysis and reporting. The primary objective is to identify and resolve data integrity issues such as missing values, duplicate records, inconsistent formatting, and invalid entries.

Key tasks included:

Data Audit: Analyzed the structure and content of the database to identify anomalies and inconsistencies.

Standardization: Ensured uniform formatting of fields such as dates, text casing, and numerical precision.

Handling Nulls and Missing Values: Replaced or removed null entries based on contextual relevance and business rules.

Duplicate Removal: Detected and eliminated duplicate rows using SQL queries and primary key constraints.

Referential Integrity Checks: Verified and corrected foreign key relationships across tables.

Optimization: Added indexes, adjusted data types, and optimized queries to enhance performance.

Tools and Technologies:

MySQL

SQL (Structured Query Language)

Workbench or command-line interface for database interaction

Outcome: The result is a cleaned, structured, and efficient MySQL database that supports accurate data analysis and reliable business decision-making.