Business Description

Data Transformation: Using Power Query to reshape data, create calculated columns, and establish relationships between tables.
Feature Engineering: Creating new variables (or features) that can help improve model accuracy. For instance, transforming time-based data into cyclical data to capture seasonality.
Data Partitioning: Dividing the data into training and testing sets, allowing analysts to validate the model performance.
By focusing on data preparation, analysts can create a solid foundation for a reliable predictive model.

Building Predictive Models in Power BI
Power BI offers multiple methods for building predictive models. The choice of method depends on the complexity of the prediction and the data available. Common approaches include:

a. Using the Forecast Visual
The Forecast visual in Power BI is excellent for time-series analysis. This visual uses exponential smoothing algorithms to predict future values based on historical trends. While limited in customization, it effective for basic forecasting needs like sales trends and demand prediction.

b. Integrating Azure Machine Learning Models
For more sophisticated predictive needs, Microsoft Power BI integrates seamlessly with Azure Machine Learning. Power BI Data Analysts can:

Use pre-built machine learning models in Azure, such as classification, regression, and anomaly detection.
Deploy custom models created in Azure Machine Learning and connect them to Power BI through a web service.
Leverage these models to make predictions on new data in real time, making them ideal for applications like fraud detection or personalized recommendations.


VISIT FOR MORE INFORMATION >>>>>>>>>
https://dumpsarena.com/microsoft-dumps/pl-300/

• Reviews & Ratings
• Write a Review