Simple Sales Prediction Using Python on Power BI

Created By: Muditha Pelpola
LinkedIn: https://www.linkedin.com/in/muditha-pelpola/

In this video session , it is discussed about how to build a simple machine learning model using Python on Power BI.

Here simple liner regression algorithm has been used to build the machine learning model.
Linear regression is a basic and commonly used type of predictive analysis. Simple linear regression is useful for finding relationship between two continuous variables. One is predictor or independent variable and other is response or dependent variable. I took user story as Sales Vs Marketing relationship. Sales is the predictor or independent variable and Marketing Expenses took as response or dependent variable . As usually more allocation for marketing expenses , simply , we can assume that good marketing program like adverting or promotions can effect to increase sales or achieve sales targets .

Sklearn library has been used in this session to build predictive machine learning model on Jupiter notebooks .
Class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=None)
Link : https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html

Python source code ( that used to build machine learning model ) has been used as source for Power BI . (Linked to Other sources in Power BI) and linked with Calendar or Date dimension and Marketing Expenses Range (Adjustment) to visualize the predicted data.