Stock Price Prediction Using Python
Stock Price Prediction Using Python is a machine learning-based web application designed to predict short-term stock prices using advanced machine learning techniques. The project provides a professional platform where users can analyze stock market trends, visualize historical prices, study technical indicators, and generate data-driven stock price predictions.
The application is developed using Python and combines machine learning, data visualization, authentication, APIs, and modern web technologies to provide a complete stock price prediction platform. The system is designed as a containerized web application using Docker and provides users with a dashboard for tracking predictions and preferences.
This project is useful for students, developers, and machine learning learners who want to understand how stock market data can be processed and used with different machine learning models for prediction.
Table of Contents
Stock Price Prediction – Project Overview
The Stock Price Prediction project focuses on short-term stock price forecasting. It allows users to work with stock market data, visualize historical price movements, analyze technical indicators, and generate predictions using multiple machine learning algorithms.
The platform also includes user authentication, subscription plans, portfolio management, stock price alerts, market-news sentiment analysis, and an interactive dashboard. Stock market data is provided through the Yahoo Finance API.
| Project Detail | Information |
|---|---|
| Project Name | Stock Price Prediction |
| Language Used | Python |
| Database | Not Applicable / NA |
| Project Type | Web Application |
Available Features
The Stock Price Prediction application contains several features designed to provide a complete environment for stock analysis and machine learning-based prediction.
1. User Authentication & Secure Login
The platform provides user authentication and secure login functionality. Users can access the application through an authenticated account and use the features available to them according to their subscription plan.
2. Tiered Subscription Plans
The project includes multiple subscription plans with integrated Stripe Payments. Each plan provides different prediction limits, forecast periods, and access to machine learning models.
The available subscription tiers are:
- Free: 5 predictions per day, 7-day forecast, and Linear Regression model only.
- Basic: 20 predictions per day, 14-day forecast, Linear Regression and Random Forest.
- Professional: 50 predictions per day, 30-day forecast, 4 models, and Priority Support.
- Enterprise: 200 predictions per day, 60-day forecast, all models, Premium Support, and Bulk API access.
3. Portfolio Management Dashboard
The application provides a Portfolio Management Dashboard where users can manage and track their portfolio-related information through the platform.
4. Custom Stock Price Alerts
Users can create custom stock price alerts to monitor selected stock price movements. This feature helps users keep track of stock prices according to their selected requirements.
5. AI-Powered Sentiment Analysis
The platform provides AI-powered sentiment analysis for market news. This allows the application to analyze market-related news as part of the stock analysis process.
6. Interactive Historical Price Charts
The project includes interactive historical price charts for visualizing stock price movements. These charts help users analyze historical market data and understand price trends.
Technical Indicators
The Stock Price Prediction application provides several technical indicators that can be used for analyzing stock market data.
- Bollinger Bands
- MACD (Moving Average Convergence Divergence)
- RSI (Relative Strength Index)
- SMA (Simple Moving Average)
- EMA (Exponential Moving Average)
These indicators are included as part of the platform’s stock analysis functionality and can be used alongside historical price charts and machine learning predictions.
Machine Learning Models
The project uses multiple machine learning algorithms for stock price prediction. Using different models allows the platform to provide different approaches to analyzing stock market data.
1. Linear Regression
Linear Regression is one of the machine learning models available in the application. It is included in the Free subscription plan and provides a basic approach to stock price prediction.
2. Random Forest
Random Forest is another machine learning model available in the platform. It is included with the Basic plan along with Linear Regression.
3. Extra Trees
The project also includes the Extra Trees machine learning algorithm as one of the available prediction models.
4. K-Nearest Neighbors
K-Nearest Neighbors (KNN) is included as another machine learning model used within the stock price prediction platform.
5. XGBoost
XGBoost is also included among the machine learning models available in the project. The platform supports multiple models for stock price prediction and provides access to these models according to the subscription tier.
RESTful API
The project provides a RESTful API for full platform access. The API allows the platform’s functionality to be accessed programmatically and forms an important part of the application’s backend architecture.
User Dashboard
The application includes a dedicated User Dashboard where users can track their predictions and preferences. The dashboard provides a central location for users to interact with the prediction platform and manage their available functionality.
Technology Stack
The Stock Price Prediction project uses several technologies for backend development, frontend interaction, machine learning, visualization, authentication, caching, deployment, and payment processing.
| Technology | Purpose |
|---|---|
| Python | Backend and application development |
| FastAPI | Backend API framework |
| Streamlit | Frontend application interface |
| Scikit-learn | Machine learning |
| XGBoost | Machine learning and prediction |
| Plotly | Data visualization |
| Redis | Caching |
| JWT | Authentication |
| Docker | Containerized deployment |
| Docker Compose | Container management |
| Stripe | Payment gateway |
| Yahoo Finance API | Stock data provider |
Subscription Plans
The application provides four subscription tiers. Each plan offers different limits for daily predictions, forecast periods, and machine learning model access.
| Plan | Features |
|---|---|
| Free | 5 predictions/day, 7-day forecast, Linear Regression model only |
| Basic | 20 predictions/day, 14-day forecast, Linear Regression + Random Forest |
| Professional | 50 predictions/day, 30-day forecast, 4 models, Priority Support |
| Enterprise | 200 predictions/day, 60-day forecast, all models, Premium Support, Bulk API access |
Screenshot:-
How to Download
Get This Project
The complete package is available so you can run, study, and submit it with confidence. It includes:
- Full Source Code
- Project Report
- Synopsis
- PPT Presentation
For any queries or a quick response, reach out on WhatsApp: +91 79834 34684
Modern Containerized Deployment
The project supports modern containerized deployment using Docker and Docker Compose. The application uses FastAPI for its backend architecture and can be deployed using container-based development and deployment practices.
This structure provides a modern approach to running the different components of the stock prediction platform.
Stock Data Provider
The application uses the Yahoo Finance API as its stock data provider. Stock market information obtained through the data provider can be used by the application for historical price visualization, technical analysis, and machine learning-based prediction.
Who Can Use This Project?
The Stock Price Prediction project is suitable for students and developers interested in Python, Machine Learning, Data Science, stock market analysis, and web application development.
It provides practical exposure to machine learning models, technical indicators, data visualization, REST APIs, authentication, subscription plans, payment integration, and containerized deployment.
Conclusion
Stock Price Prediction Using Python is a comprehensive machine learning web application that combines stock market analysis with multiple prediction models. The platform provides interactive historical charts, technical indicators, stock price alerts, AI-powered market-news sentiment analysis, portfolio management, and a user dashboard.
With technologies such as Python, FastAPI, Streamlit, Scikit-learn, XGBoost, Plotly, Redis, JWT, Docker, Stripe, and Yahoo Finance API, the project demonstrates how machine learning can be integrated into a modern web-based stock prediction platform.
The availability of different subscription tiers and multiple machine learning models makes the project a practical example for learning how a professional stock prediction application can be structured.
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