AI-Powered Microsoft Stock Price Predictor - Machine Learning Final Year Project with Source Code
Advanced AI-driven stock prediction system using Linear Regression, LSTM, and XGBoost models to forecast Microsoft stock prices with 99.99% accuracy. Complete with interactive dashboards, technical indicators, and REST API integration.
Technology Used
Python | Flask | Pandas | NumPy | Scikit-learn | TensorFlow | Keras | XGBoost | Plotly | Bootstrap 5 | HTML5 | CSS3 | JavaScript | Gunicorn | Joblib
Project Files
Project Overview
The AI-Powered Microsoft Stock Price Predictor is a comprehensive machine learning final year project that demonstrates advanced data science techniques for financial forecasting. This professional-grade application combines multiple AI algorithms to predict MSFT stock prices with exceptional accuracy, making it an ideal choice for computer science and data science students seeking an impactful final year project with complete source code and documentation.
Advanced Project Features
- Multi-Model AI Architecture: Implements six different machine learning models including Linear Regression, Random Forest, XGBoost, Support Vector Regression, Gradient Boosting, and LSTM neural networks for comprehensive price prediction analysis.
- Real-Time Technical Analysis: Calculates and displays critical stock market indicators including RSI (Relative Strength Index), MACD (Moving Average Convergence Divergence), multiple moving averages (MA7, MA21, MA50), and volatility metrics.
- Interactive Data Visualizations: Features dynamic Plotly-powered charts including candlestick patterns, volume analysis, price trends, and technical indicator overlays for professional market analysis.
- Feature Engineering Excellence: Incorporates 24+ engineered features including lag variables, rolling statistics, momentum indicators, and trend analysis parameters for superior prediction accuracy.
- RESTful API Integration: Provides JSON endpoints for seamless integration with external applications, enabling programmatic access to predictions and stock statistics.
- Professional Web Interface: Stunning Microsoft-branded UI with dark mode, glassmorphism effects, responsive Bootstrap 5 layout, and smooth animations for exceptional user experience.
- Model Performance Tracking: Comprehensive evaluation metrics with R-squared scores exceeding 0.9999 for Linear Regression, demonstrating production-ready accuracy levels.
- Historical Data Analysis: Processes extensive historical MSFT stock data to identify patterns, trends, and seasonal variations for informed predictions.
Technical Implementation Details
This project showcases professional software development practices with a well-structured Flask application architecture. The backend implements robust data processing pipelines using Pandas and NumPy, while Scikit-learn handles model training and evaluation. The LSTM deep learning model utilizes TensorFlow/Keras for sequential pattern recognition in time-series data.
The frontend leverages modern web technologies including Bootstrap 5 for responsive design, Plotly.js for interactive visualizations, and custom CSS3 animations. The application follows MVC architecture principles with clear separation between data models, business logic, and presentation layers.
Real-World Applications
- Financial Portfolio Management: Assist investors and portfolio managers in making data-driven decisions about MSFT stock investments based on AI predictions.
- Trading Algorithm Development: Serve as foundation for automated trading systems and algorithmic trading strategies in financial markets.
- Risk Assessment Tools: Help financial analysts evaluate market volatility and potential risks associated with stock investments.
- Educational Platform: Teach students and beginners about machine learning applications in finance and quantitative analysis.
- Research and Analysis: Support academic research in financial forecasting, time-series analysis, and AI applications in economics.
- Market Intelligence: Provide insights for financial news platforms and investment advisory services.
Learning Outcomes for Students
By working with this final year project, students will gain hands-on experience in multiple cutting-edge technologies and methodologies. You will master machine learning algorithms, understand time-series forecasting techniques, learn professional web development practices with Flask, and develop skills in data visualization and API design. The project covers the complete software development lifecycle from data preprocessing to deployment, making it an excellent portfolio piece for job interviews.
Additionally, students will understand financial domain concepts, technical analysis indicators, and how AI transforms traditional financial analysis. The comprehensive codebase includes detailed comments and documentation, making it easy to understand, customize, and extend for specific academic requirements.
Why Choose This Project
- Complete source code with detailed documentation and comments
- Professional-grade UI matching industry standards
- Multiple AI models for comparative analysis
- Ready-to-use dataset with historical stock data
- Comprehensive project report and presentation materials available
- Easy setup with clear installation instructions
- Scalable architecture for adding new features
- Suitable for CSE, IT, AI/ML, and Data Science branches
- Demonstrates both frontend and backend development skills
- Perfect for academic presentations and demonstrations
Customization Options Available
At CodeAj Marketplace, we offer extensive customization services for this project. You can request modifications to track different stocks beyond Microsoft, integrate additional machine learning models, add cryptocurrency price prediction capabilities, implement real-time data feeds from financial APIs, or customize the UI to match your institution's requirements. Our expert team can help you extend the project with advanced features like sentiment analysis from news articles, portfolio optimization algorithms, or multi-stock comparison dashboards.
Project Deliverables
- Complete Python source code with Flask application
- Pre-trained machine learning models (PKL and H5 files)
- Historical MSFT stock dataset (CSV format)
- Requirements.txt with all dependencies
- Professional HTML/CSS/JavaScript frontend
- Database schema and configuration files
- Setup and installation guide
- User manual and technical documentation
- Project report (optional add-on)
- PowerPoint presentation (optional add-on)
- Research paper format document (optional add-on)
Extra Add-Ons Available – Elevate Your Project
Add any of these professional upgrades to save time and impress your evaluators.
Project Setup
We'll install and configure the project on your PC via remote session (Google Meet, Zoom, or AnyDesk).
Source Code Explanation
1-hour live session to explain logic, flow, database design, and key features.
Want to know exactly how the setup works? Review our detailed step-by-step process before scheduling your session.
₹999
Custom Documents (College-Tailored)
- Custom Project Report: ₹1,200
- Custom Research Paper: ₹800
- Custom PPT: ₹500
Fully customized to match your college format, guidelines, and submission standards.
Project Modification
Need feature changes, UI updates, or new features added?
Charges vary based on complexity.
We'll review your request and provide a clear quote before starting work.