Records Processed
20,000+
Primary Tool
Python (Pandas)
Skills Highlighted
ETL, Imputation, Feature Engineering
The Data Quality Challenge
The raw global internet usage dataset contained a high volume of missing values (approx. 18% nulls), inconsistent formatting, and duplicate records across key metrics (e.g., 'Connectivity Score'). This required a scripted, repeatable process to transform the data into a reliable format for subsequent statistical modeling.
The Python/Pandas Solution
I developed a robust Python notebook to handle the entire cleaning pipeline using the following techniques:
- Missing Value Imputation: Applied **Median Imputation** on numerical features to maintain data distribution integrity while filling gaps.
- Irrelevant Feature Removal: Automatically identified and dropped low-variance and highly correlated features to reduce dimensionality.
- Categorical Encoding: Converted sparse text fields into numerical representations ready for ML consumption, ensuring data type consistency.
Result & Technical Impact
The workflow reduced the data's error rate to **less than 1%** and resulted in a clean, production-ready dataset. This project demonstrates proficiency in building **repeatable and documented ETL processes**, a critical skill for any analytical pipeline.
Visualization: Snippet of the Python Code.