Customer-Segmentation-Of-Starbucks

Using Supervised and Unsupervised learning

Data Scientist Nanodegree

Project: Data Scientist Capstone

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1. Installation]
2. Project Motivation
3. Run the project
4. Conclusion and Results
5. Acknowledgements

1. Installation

• Python versions 3.*.
• Python Libraries:
  • Pandas.
  • Scikit-learn.
  • numpy.
  • time.
  • matplotlib.
  • seaborn.

2. Project Motivation

Starbucks is a growing brand and I myself not being a coffee lover, i have been inquisitive about what attracts so many customers to the doors of Startbucks everyday with so many updates on instagram aswell. Through this Udacity Nanodegree program, this project motivated me to get a deeper understanding about its customers and promotional campaigns. This project has been a great oppurtunity to study the brand using its real-life data and improve my Data Science skills.

3. Run the project

From the repository,Download the jupyter notebook file - Customer_Segmentation-Starbucks_Capstone.ipynb and the 3 data files.

• Open the ananconda commond windown n open the jupyter browser on your local:http://localhost:8889/tree
• Click on the downloaded jupyter file n run it to see the resuls

4. Conclusion and Results

Conclusion: Segmentation of startbucks Customers: The customers can be segmented depending on various parameters according to the campaign chosen On analysis the data using supervised and unsupervised learning(Kmeans), we can conclude that: Different segments of customers react to offers differently. The count of male customers in low-income level is slightly higher than that of female and other customers Though the aveage salary of femal is greater than that of the male, female spend less on starbucks than male Starbucks has more of the young crowd than those of the aged once. The result of the offer_type was prediced by training a supervised classifier. Results:- Customers are attracted to BOGO and Discount offers more as compared to Informational Offers The buying behaviour of a customer is indepemdent of its annual income Starbucks have more male customers than females and other gender. KNeighborsClassifier turned out to be the best algorithm for this task and predicts customer response with an accuracy rate of almost 93% after hyperarameter tuning. Given the fact that also the same customer will react differently the same offer.

You can check the code on my Gitbub.

5. Acknowledgements

I thank Udacity for such an opportunity and Starbucks for sharing simulated data that mimics customer behavior data.

You can check my blog on medium.