Marketing Analytics Project for Data Science : Regression Analysis for Market Mix Modelling (with full Code)
🌟 Get ahead of the Competition in the Data Science and Data Analytics Career field. Learn how to perform a Market Mix Modelling using Data Science Regression techniques! 📊
💡 Marketing Analytics Project: Determining which Marketing Channels are giving your Business the largest bang for your Buck. 📈
[with full Code and Video Explainers]
In this Project, you will perform detailed Exploratory Data Analysis and Preprocessing on Marketing Channel expense Data. You will learn to use multiple Regression Modelling techniques like Support vector Regressor, AdaBoostRegressor, XGBRegressor, RandomForestRegressor, DecisionTreeRegressor, GradientBoostingRegressor, KNeighborsRegressor, and MLPRegressor, etc. You will also learn to Optimise your Regression Models using powerful techniques like Grid Search Cross Validation before going finishing the project with Feature Importance Analysis and Feature Elasticity Analysis. This project will give you a head-start on your Marketing Analytics using Data Science Journey. 🚀
Market Mix Modelling, Marketing Analytics Using Data Science Project fo Student: Predict the Best Marketing Channel for your Business using Data Science project for beginners, Regression Analysis | Marketing Analytics Project for Data Science
Finding the right Marketing Channels for your Business and using them effectively is a big Challenge for all Businesses. However, it’s not a straightforward task to find out which Channel is giving the most bang for the buck. At any point in time, Businesses are investing marketing dollars on a multitude of Channels and the challenge lies in isolating the ones that are delivering most.
In this Project Module, you will learn how to Predict the Marketing Channels that are Influencing your Sales the most.
The Project uses the power of Data Science, with a number of Regression Algorithms namely Support vector Regressor, AdaBoost Regressor, XGB Regressor, Random Forest Regressor, Decision Tree Regressor, Gradient Boosting Regressor, KN eighbors Regressor, and ML PRegressor. We will perform a number of Model Optimisation Techniques and find out which Regressor is giving us the best results to predict Sales Revenue outcomes. Finally, we will find out the significant Influencing Features from the best Regressor.
This Data Science, Regression market analysis of project exposes you to the following Concepts:
Use of a multitude of Regression Algorithms, Data Analysis and Preprocessing Techniques, Model Optimisation Techniques, finding Feature Importances and so on.
FAQs: Marketing Analytics Project
Q. What is marketing analytics?
Ans: Marketing analytics project is the practice of measuring, managing, and analyzing marketing performance to maximize its effectiveness and optimize return on investment (ROI). It involves understanding data from various marketing channels and using it to guide marketing strategies and decisions.
Q. How does marketing analytics project benefit a business?
Ans: Marketing analytics provides insights into customer behavior, preferences, and trends. This enables businesses to tailor their marketing strategies, improve customer engagement, and make data-driven decisions. It also helps in tracking the ROI of marketing efforts and adjusting strategies for better performance.
Q. What skills are essential for a marketing analytics project professional?
Ans: A marketing analytics project professional should have a strong foundation in statistical analysis, data interpretation, and marketing principles. Proficiency in data visualization tools and analytics software is also crucial. Additionally, good communication skills are essential to convey insights and recommendations effectively.
Target audiences
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