Data Science

Data Wrangling and Preprocessing

Data wrangling involves preparing raw data for analysis by cleaning, transforming, and structuring it effectively.

Key Preprocessing Steps

• Handling Missing Values: Using imputation techniques like mean substitution or predictive modeling.
• Data Normalization: Scaling features to standard ranges for consistency.
• Feature Encoding: Converting categorical variables into numerical representations.
• Outlier Detection: Identifying anomalies using methods like IQR or Z-score.