- Installation
- Project Motivation
- Dataset
- File Descriptions
- Results
- Blog Post
- Licensing, Authors, and Acknowledgements
Python3 should be installed Python Libraries:
- Pandas.
- Scikit-learn.
- numpy.
- time.
- matplotlib.
- seaborn
- json
It is a capstone project through which I am going to determine that how Starbucks customers use the app, and how much effective their offers system.In addition, I will try to find who should the app target in terms of promotions. The datasets I am going to use for this project contains simulated data that depicts about the customer behavior on the Starbucks rewards mobile app. From this project we can get a clear understanding of the costumers' behavior to help in making better decisions.
"Offering resources to every customer is waste of money and time". We want to give only those who will be able to complete the offer recommended by us. It is like before offer to someone we know he/she probably will not be able to complete it is a waste of time and resources that can be given to someone who we know will complete it. I will approach this problem through CRISP-DM process by first cleaning up the data, then doing some exploratory analysis and see who are my most valuable customers after that I will create a model to help us predicting feature customers and which type of offer should we give them. My goal for this project is predicting which kind of offers, Buy One Get One Free (BOGO), Discount or informational is better to give a current customer by only knowing his/her age, gender, income and the amount they are paying.
As dataset size is more than 25MB.So,it is not possible to upload it here.But if you want to download the dataset then I uploded to google drive you can download it from here
There is one notebook file that have all the work related to the above questions. The data is not available but I showed the data frames and it have some of the data in it. Markdown cells were used to assist in walking through the thought process for individual steps.
From that analysis I did in this project. I found out that most favorite type of offers are Buy One Get One (BOGO) offers and Discount offers. I digged deep to see who and what type of customers we have and noticed that Females tend to complete offers more than males with 56% completion of the offers they received. Where Males completed only 43.18 from the offers they received. But our current data shows that we gave males more offers since they have more transactions than females with total number of 72794 transactions, where females only had 49382 transactions. In conclusion, the company should give more offers to Females than Males since they have more completed offers. And they should focus more on BOGO and Discount offers since they are the one that tend to make customers buy more.
Blog post here
The data was given by Starbucks and Udacity. Feel free to ask me anything about the code @manendranathshukla
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