This is a data cleaning project carried out using SQL. It highlights practical methods to clean, structure, and prepare raw data for meaningful insights.
-- The project applies four key data cleaning steps:
1.Removing duplicates – Ensures each record is unique.
2.Standardizing data – Formats text, dates and other fields consistently.
3.Handling null and blank values – Fills or removes missing data.
4.Removing unnecessary rows or columns – Keeps the dataset relevant and manageable.
--Technologies Used:
1.SQL (Structured Query Language)
2.MySQL (Database management system)
--Conclusion This project demonstrates essential SQL data cleaning techniques for aspiring Data Analysts. The cleaned dataset is ready for further analysis or visualization using tools like Excel, Tableau, Power BI, or Python (Pandas).