This project focuses on the process of cleaning and transforming raw data to ensure accuracy, consistency, and usability for subsequent analysis. Utilizing a dataset from various sources, the project highlights the importance of data quality in deriving meaningful insights and making informed decisions.
- Data Cleaning: Address and resolve issues such as missing values, duplicates, and inconsistencies within the dataset.
- Data Transformation: Convert raw data into a structured and analysis-ready format, including normalization and standardization.
- Enhanced Data Quality: Prepare the dataset for further analysis by ensuring it meets high standards of quality and reliability.
- Data Import: Loaded datasets from multiple sources into the analysis environment.
- Issue Identification: Detected and documented problems such as missing entries, outliers, and format inconsistencies.
- Data Correction: Applied techniques to handle missing values, remove duplicates, and standardize data formats.
- Transformation: Performed data normalization, aggregation, and enrichment to enhance dataset usability.
- Programming Languages: SQL, Python
- Libraries & Tools: Pandas, NumPy, Excel
- Data Cleaning Techniques: Handling missing values, data imputation, outlier detection, and data normalization
- Improved Data Quality: Successfully cleaned and transformed the dataset, making it ready for accurate analysis and decision-making.
- Enhanced Usability: The cleaned data is now well-structured and reliable, providing a solid foundation for further analytical tasks.
This project demonstrates the critical role of data cleaning and transformation in ensuring high-quality, reliable datasets. By addressing common data issues and preparing the dataset for analysis, this project highlights the essential steps in the data preparation process, setting the stage for effective data analysis and insight generation.