https://github.com/darshan1924/stock-price-pridiction
Stock Price Predictor App: A machine learning-powered application built with Streamlit to analyze historical stock data, visualize trends (e.g., moving averages), and predict future stock prices using an LSTM model. It fetches real-time data via Yahoo Finance and provides interactive visualizations for informed decision-making.
https://github.com/darshan1924/stock-price-pridiction
data-visualization jupyter-notebook kera lstm-neural-networks machine-learning python3 stock-price-prediction streamlit yahoo-finance-api
Last synced: 6 months ago
JSON representation
Stock Price Predictor App: A machine learning-powered application built with Streamlit to analyze historical stock data, visualize trends (e.g., moving averages), and predict future stock prices using an LSTM model. It fetches real-time data via Yahoo Finance and provides interactive visualizations for informed decision-making.
- Host: GitHub
- URL: https://github.com/darshan1924/stock-price-pridiction
- Owner: darshan1924
- Created: 2025-02-12T16:41:20.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-13T09:47:18.000Z (8 months ago)
- Last Synced: 2025-02-13T10:32:35.925Z (8 months ago)
- Topics: data-visualization, jupyter-notebook, kera, lstm-neural-networks, machine-learning, python3, stock-price-prediction, streamlit, yahoo-finance-api
- Language: Jupyter Notebook
- Homepage:
- Size: 662 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
### 📈 Stock Price Predictor App
### 🚀 Overview
The Stock Price Predictor App is a machine learning-powered application designed to analyze historical stock data, visualize trends, and predict future stock prices. Built with Streamlit, it leverages Yahoo Finance for real-time data retrieval and a Keras-based LSTM model for accurate short-term predictions. This app bridges the gap between financial analysis and machine learning, offering users a seamless experience for stock market insights.
### 📌 Key Features
Real-Time Stock Data: Fetch live stock data using the Yahoo Finance API (yfinance).
Technical Indicators: Visualize Moving Averages (100, 200, and 250 days) for trend analysis.
Stock Price Prediction: Predict stock prices for the next 10 or 20 days using a trained LSTM model.
Comparison of Trends: Display original vs predicted prices for better decision-making.
Interactive Visualizations: Use Matplotlib and Streamlit for dynamic and user-friendly charts.
### 🛠️ Tech Stack
Frontend: Streamlit (for building the user interface)
Backend: Python
Machine Learning: Keras, TensorFlow (LSTM model for time-series forecasting)
Data Retrieval: Yahoo Finance API (yfinance)
Visualization: Matplotlib, Pandas
### 📂 Project Structure
Stock_Price_Predictor/
│── stock_predictor.py # Main Streamlit application
│── Stock market Youtube.ipynb # Jupyter Notebook for ML analysis
│── Latest_stock_price_model.keras # Pretrained LSTM model
│── requirements.txt # Python dependencies
│── README.md # Documentation
│── .gitignore # Ignore unnecessary files
### 🎯 How to Run Locally
### 1️⃣ Clone the Repository
git clone
cd Stock-Price-Predictor
### 2️⃣ Create & Activate Virtual Environment (Recommended)
python -m venv venv
source venv/bin/activate # On macOS/Linux
venv\Scripts\activate # On Windows
### 3️⃣ Install Dependencies
pip install -r requirements.txt
### 4️⃣ Run the Application
streamlit run stock_predictor.py
The app will be available at http://localhost:8501/.
### 📝 Running the Jupyter Notebook
The Jupyter Notebook (Stock market Youtube.ipynb) provides an in-depth analysis of stock market trends and predictions. To run it:
Open the notebook locally:
jupyter notebook
Alternatively, upload it to Google Colab for cloud-based execution.
### 👨💻 Darshan Chavda