Computer & IT · Machine learning and AI · Final year project
Online AI Shopping With M-Wallet System
An AI shopping assistant with a mobile wallet. The user enters what they want, the system finds the best deal and completes the purchase using the integrated wallet.
What is the Online AI Shopping With M-Wallet System project?
An AI shopping assistant with a mobile wallet. The user enters what they want, the system finds the best deal and completes the purchase using the integrated wallet.
Key facts
- Type
- Final year project · Beginner to intermediate
- Suitable for
- Diploma capstone, BCA, B.Sc, B.E / B.Tech final year and MCA
- Build time
- 3 to 4 weeks
- Tech stack
- Machine learning and Web application
- Guidance from
- ₹2,499 (AI / ML Mini Project)
How the Online AI Shopping With M-Wallet System works
- A dataset is collected or downloaded and cleaned: missing values, duplicates and outliers are handled.
- Features are selected and encoded, and the data is split into training and test sets.
- Several algorithms are trained and compared, and the best one is tuned.
- The final model runs behind a simple web form where the user enters values and gets the prediction with its confidence.
Block diagram
User
Student / customerAdminWeb front end
HTML, CSS, JavaScriptBootstrapMachine learning
Pythonscikit-learnFlask or Streamlit for the demoData
MySQLTrained modelTech stack and tools
| Layer | Tools |
|---|---|
| Machine learning | Python, Pandas and NumPy, scikit-learn, Matplotlib, Flask or Streamlit for the demo |
| Web application | PHP (Laravel) or Python (Django), MySQL, HTML, CSS, JavaScript, Bootstrap |
Any of these can be swapped for the stack your college prescribes. Requirements: a laptop with 8 GB RAM, and free, open-source tools only.
How much does this project cost?
| Option | What you get | Cost |
|---|---|---|
| AI / ML Mini Project | Python / Jupyter notebook, Cleaned dataset and trained model, Simple web demo (Streamlit) | ₹2,499 |
| Advanced Plan | 100% working source code, Black book / project report (100–150 pages) PDF, Step-by-step code explanation | ₹9,999 |
| Premium Plan | 100% working source code, GitHub + zip delivery, Black book (100–200 pages) | ₹11,999 |
No GST and no hidden charges. Use the cost calculator for a quote with your deadline and add-ons, or see all plans.
Download the project brief (PDF)
Summary, working, tech stack and viva questions on one branded PDF to show your guide or team. To get it developed, contact us on WhatsApp.
What you submit with this project
- Source code with setup guide and a GitHub repository
- Project report or black book in your university's chapter order (DFD, ER, UML diagrams, test cases)
- Presentation for the review and final viva
- Synopsis for topic approval, if your college asks for one
Computer & IT viva questions on this project
What is the aim of the Online AI Shopping With M-Wallet System project?
An AI shopping assistant with a mobile wallet. The user enters what they want, the system finds the best deal and completes the purchase using the integrated wallet.
Explain the working of your project in short.
A dataset is collected or downloaded and cleaned: missing values, duplicates and outliers are handled. Features are selected and encoded, and the data is split into training and test sets. Several algorithms are trained and compared, and the best one is tuned. The final model runs behind a simple web form where the user enters values and gets the prediction with its confidence.
Which algorithm did you use and why?
We compared a few suitable algorithms (for example logistic regression, decision tree, random forest and SVM) on the same train-test split and chose the one with the best balance of accuracy, recall and speed for this data.
What is the difference between accuracy, precision and recall?
Accuracy is correct predictions over all predictions. Precision is how many predicted positives are truly positive. Recall is how many actual positives were found. When classes are imbalanced, precision and recall matter more than accuracy.
Explain the architecture of your web application.
It follows a three-tier architecture: the browser shows the user interface, the server-side code handles logic and validation, and the MySQL database stores records. Requests travel over HTTP and responses are rendered as HTML or JSON.
How did you prevent SQL injection?
All queries use prepared statements with bound parameters, so user input is never joined into the SQL text. Inputs are also validated on the server.
Which SDLC model did you follow?
We followed an iterative (incremental) model: requirements and a basic design first, then modules built and tested one by one, with the guide's feedback after each review. It suits a project where requirements become clearer as we build.
How did you test the project?
Unit testing of each module, integration testing of the complete flow and system testing with real inputs. Test cases with expected and actual results are listed in the report, including invalid inputs.
What are the limitations and future scope?
The current version is tested on a limited dataset or number of users. Future work includes deploying on the cloud, adding more features or languages, improving accuracy with more data and building a mobile version.
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Want to build Online AI Shopping With M-Wallet System?
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