Data Preparation

Data cleansing is the process of identifying and correcting errors or inconsistencies in data sets.

Data preprocessing is the process of cleaning and transforming raw data into a usable format for analysis and machine learning.

Data wrangling is the process of cleaning and transforming raw data into a usable format for analysis.

Example Selection is the process of choosing specific data points for training AI models.

Human annotation is the process of labeling data by humans to improve AI model training and performance.

Model Preparation involves organizing and refining data for effective AI model training and evaluation.

Oversampling techniques are methods used to address class imbalance in datasets by increasing the number of instances in the minority class.