This project focuses on analyzing and cleaning a customer dataset to ensure data quality, accuracy, and consistency before further analysis. The dataset contains information such as customer names, ages, email addresses, and purchase history.
Using Python and Pandas, this project demonstrates practical data cleaning techniques that prepare raw customer data for analytics, visualization, and business insights.
- Identify and handle missing values appropriately.
- Remove duplicate records to maintain data uniqueness.
- Standardize data types for accurate computations.
- Extract meaningful features from existing data fields.
- Prepare a structured, analysis-ready dataset for downstream use.
| Category | Tools / Libraries |
|---|---|
| Programming Language | Python |
| Data Manipulation | Pandas, NumPy |
| Data Validation | Regex, datetime |
| File Handling | CSV |
- Removed all duplicates and standardized column data types.
- Added derived features such as Domain and Total Purchases to enrich the dataset.
- Ensured 100% completeness across critical fields for analytics readiness.
- Delivered a clean, structured dataset ideal for visualization or predictive modeling.
- Strengthened data wrangling skills using Pandas.
- Practiced real-world cleaning techniques for customer datasets.
- Demonstrated the importance of feature engineering in data preparation.