INTRODUCTION OF PROJECT
Starbucks have an mobile application which has various function and is used by the people to order online coffee via the app for pickup, pay for the purchase via the app and collect reward points. This app also provide a membership “My Starbucks Rewards™ membership”, after paying through the app the user receives free Stars/Bonus points that can be used to redeem a free drink of the user’s choice. This app also offers various promotions to the users which includes Discount in a discount, a user gains a reward equal to a fraction of the amount spent on drinks ,BOGO (Buy One Get One Free) in a BOGO offer, a user needs to spend a certain amount to get a reward equal to that threshold amount and Informational offer which basically includes any release of new product and there is no reward, but neither is there a requisite amount that the user is expected to spend. In this project the basic task is to use the data to identify which groups of people are most responsive to each type of offer, and how best to present each type of offer.
PROBLEM STATEMENT We will be exploring the Starbuck’s Dataset in which we will determine how people make purchasing decisions and how those decisions are influenced by promotional offers.The task is to combine transaction, demographic and offer data to determine which demographic groups respond best to which offer type. Every offer has a validity period before the offer expires. As an example, a BOGO offer might be valid for only 5 days. You'll see in the data set that informational offers have a validity period even though these ads are merely providing information about a product.
Libraries use are Numpy Pandas math json matplotlib time seaborn SKLEARN
Here’s the basic outline of the approach used in the project:-
- Data Exploration and Pre-processing :- i) cleaning the data ii) processing the data and merging data from offer portfolio, customer profile, and transaction for analysing. 2)Perform Exploratory Data Analysis on the Data.
- Building different machine learning model to determine which is the most suitable.
- Using Evaluation Metric for determination of best model
- Summarize the prediction