Data Preprocessing with AI

Before analysis, raw data must be prepared through preprocessing. AI simplifies this process by automating many of the tasks involved:

Data Cleaning: AI algorithms detect and correct errors, remove duplicates, and handle missing values without manual intervention.

Data Transformation: Converting raw data into a usable format, including normalization, standardization, and encoding categorical variables.

Anomaly Detection: AI models identify outliers and inconsistencies that could distort analysis results.

Feature Engineering: AI suggests new features that can improve the performance of predictive models by identifying important variables and relationships in the data.

Automating data preprocessing ensures cleaner datasets, which leads to more accurate analysis and reliable insights.