This tutorial guides you through harnessing the power of Python visuals in Power BI to perform data analysis and predictive modeling. The initial steps involve loading actual sales and advertisement data, which runs from the start of the year through September, while also including targeted ad spending for the final quarter (October, November, and December). This is set up in a Power BI report, complete with a title and a button slicer for quarter-wise data filtering. The core of the process is the introduction of the first Python visual, where you’ll import essential libraries: Pandas for data manipulation, Matplotlib’s pyplot for visualization, and scikit-learn’s Linear Regression for modeling. The initial Python script focuses on visualizing the actual sales and ad expenditure in an interactive chart, demonstrating how the Power BI context filters the data for the Python visual, allowing the quarter slicer to dynamically update the display.
The second, more advanced Python visual is dedicated to predictive sales forecasting. You’ll reuse the imported libraries and actual sales data to train a Linear Regression model. The model is trained on the actual advertisement and sales figures, enabling it to learn the relationship between ad spend and sales. Then, the trained model is used to predict sales for the entire dataset, including the months with only targeted ad spending (October, November, December). The predicted sales values are then stored and visualized alongside the actual sales data on a combined chart. This powerful setup allows users to immediately compare the model’s predictions with historical sales and see the forecast for the final quarter, all within the interactive Power BI environment.
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