Data Cleaning and Structuring for AI-Data Cleaning and Structuring

Example

Imputing missing values in a dataset of patient records using the mean value for continuous variables like age and blood pressure, or the mode for categorical variables like gender and diagnosis.

Scenario

In a health analytics project, ensuring that patient records are complete to improve the accuracy of predictive models for patient outcomes.

Example

Identifying and removing duplicate entries in a customer database using unique identifiers like email addresses or customer IDs.

Scenario

In CRM systems, preventing multiple records for a single customer to ensure accurate customer relationship management and reporting.

Example

Removing or capping outliers in financial transaction data based on the interquartile range (IQR) to prevent skewing fraud detection models.

Scenario

In fraud detection systems, enhancing model performance by ensuring that the training data does not include extreme values that are not representative of typical transactions.

Example

Standardizing date formats from various sources to a single format (YYYY-MM-DD) in a consolidated sales report.

Scenario

In business analytics, facilitating accurate and efficient time series analysis of sales data from multiple regions or sources.