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README.md

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 # Purchasing-Pattern-in-Starbucks
-this is a trial
+## 📄 Abstract
+The data simulates how people make purchasing decisions and how those decisions are influenced by promotional offers.
+Each person in the simulation has some hidden traits that influence their purchasing patterns and are associated with their observable traits. People produce various events, including receiving offers, opening offers, and making purchases.
+As a simplification, there are no explicit products to track. Only the amounts of each transaction or offer are recorded.
+There are three types of offers that can be sent: buy-one-get-one (BOGO), discount, and informational. In a BOGO offer, a user needs to spend a certain amount to get a reward equal to that threshold amount. In a discount, a user gains a reward equal to a fraction of the amount spent. In an informational offer, there is no reward, but neither is there a requisite amount that the user is expected to spend. Offers can be delivered via multiple channels.
+This was a prompt description about the data and the scenario on which we will be working on.
+
+## 🎯 Objective 
+We aim to create a web-app which will be used to predict the best possible offer that would attract a customer on the basis of his description. We also aim at delivering certain graphs which will help us understand the purchasing patterns of the customers.
+
+### 📍 Major Checkpoints and Pipelines 
+- ⛳ Data Science
+   - [ ] Data cleaning and pipelining
+   - [ ] Building a model
+   - [ ] Training the model
+   - [ ] Testing the model
+   - [ ] Improvising the model
+   - [ ] Saving the model in a pickle file extension
+- ⛳ Creation of API
+   - [ ] Importing the Pickle file
+   - [ ] Run FLask and request predictions
+   - [ ] Testing the API
+- ⛳ Web Development
+   - [ ] Front End
+      - [ ] Landing page
+      - [ ] Input Form
+      - [ ] Visualization of graphical data
+   - [ ] Back End
+      - [ ] Integrating the API with the web-app
+      - [ ] Calling of responses for the input.
+
+### 📚 Tech stack
+- <code><img height="35" src="https://raw.githubusercontent.com/github/explore/80688e429a7d4ef2fca1e82350fe8e3517d3494d/topics/html/html.png"></code> HTML
+- <code><img height="35" src="https://raw.githubusercontent.com/github/explore/80688e429a7d4ef2fca1e82350fe8e3517d3494d/topics/css/css.png"></code> CSS
+- <code><img height="35" src="https://raw.githubusercontent.com/github/explore/80688e429a7d4ef2fca1e82350fe8e3517d3494d/topics/python/python.png"></code> Python
+- <code><img height="35" src="https://raw.githubusercontent.com/github/explore/80688e429a7d4ef2fca1e82350fe8e3517d3494d/topics/git/git.png"></code> GIT
+- <code><img height="35" src="https://github.com/edent/SuperTinyIcons/blob/master/images/svg/github.svg"></code> Github
+- <code><img height="35" src="https://raw.githubusercontent.com/github/explore/80688e429a7d4ef2fca1e82350fe8e3517d3494d/topics/flask/flask.png"></code> Flask
+- <code><img height="35" src="https://raw.githubusercontent.com/github/explore/80688e429a7d4ef2fca1e82350fe8e3517d3494d/topics/nodejs/nodejs.png"></code> NodeJS
+
+## <img height="35" src="https://i.pinimg.com/736x/e0/de/4f/e0de4f8157d0b0a9eff348231ae7de07.jpg"> Endgame
+Finally, we will be hosting the website where the owner of the shop will provide the needful information. Consequetly, we will display the graphical representation of the purchasing pattern of that particular customer and recommend the type of offer to be sent in order to optimize the sales.