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Data Cleaning & EDA
Understanding the Structure and Quality of Raw Data
Raw data often behaves like irregular, noisy lines—full of variation, inconsistencies, and hidden patterns.
The purpose of Data Cleaning and Exploratory Data Analysis is to transform this initial chaos into meaningful information. Through structured preprocessing, anomaly detection, and visual exploration, I uncover trends, relationships, and data quality issues that shape the rest of the analytical process.
This section presents my approach to understanding a dataset before deeper modeling or business decision-making begins.
PORTFOLIO 