ShopAI : AI-based Online Shopping with Product Recommendations Test phase, srs, design phase and source code final deliverable
Domain / Category Data Science/Machine Learning
Problem:
There are thousands of Products available on the internet. Some time the user does not get to search the exact product according to his taste. So we need to develop a model which can recommend the products to the user based on his previous interests. First you need to know the about user’s age, region and taste in order to recommend the product having higher chances of getting sales.
In this model we shall be using content based filtering. This model shall be analyzing large amount of information depicting user behavior and preferences to be used to predict what the user is going to do w.r.t the similarity with previous purchases.
Content based filtering is divided into three steps
- Develop a user model
- Find the similarities l Generate recommendation
Functional Requirements:
There are eight major tasks you will typically perform when developing a Recommendation system (module 6). Tasks (b-i) should be implemented iteratively while developing the system.
- Define the problem and select the dataset(s)
- Data Analysis and Pre-processing
- Feature Extraction
- User Model Development
- Find similarities in user preference with with the help of Nearest Neighbours
- Generate Recommendations
- Build system
- Test System
- Tune System
The program should have the machine learning approach to execute model detection.
Expected System Modules:
- User Management: Users should be able to create an account on the online shopping store with their personal details, such as name, address, email, and phone number. The admin should be able to manage user accounts, including creating, editing, and deleting user accounts.
- Product Catalog: The online shopping store should have a product catalog with details such as product name, description, price, availability, and images. Users should be able to search for products by keywords and filter the results based on categories, brands, price range, etc.
- Shopping Cart and Checkout Process: The online shopping store should have a shopping cart that allows users to add products to their cart, view the cart, and make changes to the quantity or remove products. The checkout process should guide users through the payment and shipping information, including the selection of a payment method and entering billing and shipping details.
- Order Management and Tracking: The admin should be able to manage orders, including viewing and updating order.
- Customer Support: The online shopping store should have a customer support system that allows users to contact the store for help, such as email or phone.
- Product Recommendations: The online shopping store should have Content-based filtering that recommends items to users based on their past preferences and behavior, as well as the attributes of the items themselves. For example, if a user has previously purchased an item a content-based filtering algorithm may recommend other items to that user based on their attributes etc. This approach focuses on the content of the items being recommended and attempts to find items that are similar to ones the user has already purchase.
- Guest Checkout: The online shopping store should allow guests to checkout without having to register an account. Guests should be able to enter their billing and shipping details and complete the purchase.
Dataset:
https://www.kaggle.com/datasets/mukuldeshantri/ecommerce-fashion-dataset
Useful Resources:
Paper:
http://www.inf.unibz.it/~ricci/ISR/papers/pazzani07.pdf
Tutorials:
https://towardsdatascience.com/hands-on-content-based-recommender-system-using-python-
1d643bf314e4
https://medium.com/analytics-vidhya/content-based-recommender-systems-in-python-
2b330e01eb80
https://www.analyticsvidhya.com/blog/2022/08/building-a-content-based-recommendationsystem/ https://www.datacamp.com/tutorial/recommender-systems-python
Tools
Python (Django)
Jupyter Notebook (Model Development)
Anaconda
Supervisor
Name: Muhammad Kaleemullah
