Maximizing Advertising ROI through Budget Optimization | Python Tutorial

You can find the dataset here under the Advertising Channels:
absentdata.com...
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In this tutorial, we show you how to use Python to optimize advertising budgets across different channels and find the ideal allocation that maximizes total sales. We start by fitting a linear regression model to the data and extracting the coefficients and intercept. We then define the objective function and the constraint function for the optimization problem, and use the minimize() function from the scipy.optimize library to solve the optimization problem and find the ideal allocation of the budget across the channels.
Finally, we calculate the actual percentage for each channel contribution using the coefficients and the optimized percentages, and interpret the results in light of the data and the optimization problem. By combining linear regression and optimization techniques, we can make data-driven decisions that improve the effectiveness and efficiency of marketing strategies. Don't forget to subscribe for more tutorials on data science and machine learning!
0:20 Code Starts
0:30 Data Exploration and Visuals
2:22 Create a Linear Model
4:56 Optimization Model
#marketing
#datascience
#scipy
#python
#dataanalysis

[@method341]

Thank you Gaelim, keep these coming. No one does these on KZbin!

[@leonardopoveromo8611]

Great tutorial thank you! would be great to see in deep some marketing mix modeling tutorials with diminishing returns with python! thanks for sharing this anyway! 🥰

[@sopravvissut0]

Great job, keep up that excellent work

[@sandrahu7122]

can we get the code?

[@ControlTheGuh]

-3k newspaper?

[@absentdata]

look at the coefficients.

[@ControlTheGuh]

​@@absentdata i did, you have a constrain of 60k, but the first two add up to +60k even when the 3rd is negative and accounted for...I don't get the split in ideal channel contribution percentage

[@sopravvissut0]

shouldn't some non negativity constraints be added? @@absentdata