Data Cleaning & Preprocessing

Performed comprehensive data cleaning and preprocessing to transform raw datasets into accurate, consistent, and analysis-ready data.

* Removed duplicate records and handled missing values using context-appropriate imputation techniques.

* Identified and corrected inconsistent, misspelled, and duplicated text values while standardizing categorical data.

* Standardized date, numeric, and formatting conventions across the dataset.

* Detected and investigated outliers, invalid values, and data inconsistencies that could affect analytical results.

* Validated relationships and consistency between related variables to identify potential data-quality issues.

* Reorganized columns and structured the dataset into a clean, consistent, and analysis-ready format.

* Applied data validation and quality checks to improve reliability and support downstream analysis and machine learning workflows.