Data Cleaning & Preprocessing

Description

Data Cleaning and Preprocessing involve preparing raw datasets for accurate and efficient analysis by addressing issues such as inconsistencies, duplicates, missing values, and formatting errors. This process includes standardizing data structures, handling outliers, normalizing variables, and integrating multiple data sources to ensure data quality and reliability. Effective preprocessing is essential for improving model performance, reducing biases, and enabling meaningful insights in subsequent analytical workflows.

Packaging Instructions

Instructions:

Submit raw data with any known issues (missing values, duplicates, etc.).
Specify preferred handling of missing/incorrect values.
Indicate whether standardization or normalization is required.

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