Computer & IT · Machine learning and AI · Final year project
Stock Market Analysis and Prediction
A data mining system that analyses how stock prices vary with different factors over the same period and helps new investors decide where to invest.
What is the Stock Market Analysis and Prediction project?
A data mining system that analyses how stock prices vary with different factors over the same period and helps new investors decide where to invest.
Key facts
- Type
- Final year project · Intermediate
- Suitable for
- Diploma capstone, BCA, B.Sc, B.E / B.Tech final year and MCA
- Build time
- 4 to 6 weeks
- Tech stack
- Machine learning, Data mining and analytics and ASP.NET web app
- Guidance from
- ₹2,499 (AI / ML Mini Project)
How the Stock Market Analysis and Prediction 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 / customerAdminMachine learning
Pythonscikit-learnFlask or Streamlit for the demoData
SQLSQL ServerTrained modelTech stack and tools
| Layer | Tools |
|---|---|
| Machine learning | Python, Pandas and NumPy, scikit-learn, Matplotlib, Flask or Streamlit for the demo |
| Data mining and analytics | Python (Pandas), SQL, Power BI / Matplotlib, Apriori / k-means |
| ASP.NET web app | ASP.NET (C#), SQL Server, Visual Studio |
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 Stock Market Analysis and Prediction project?
A data mining system that analyses how stock prices vary with different factors over the same period and helps new investors decide where to invest.
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.
What is the Apriori algorithm?
It finds frequent itemsets in transaction data by building larger itemsets only from smaller ones that are already frequent, and then derives association rules with support and confidence above set thresholds.
What is the difference between ASP.NET Web Forms and MVC?
Web Forms uses server controls and view state with an event model. MVC separates Model, View and Controller, gives full control over HTML and is easier to test.
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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