https://github.com/mehraaaaa24/stockpriceprediction
A real-time web app that predicts stock prices using an LSTM model, visualizes data with moving averages, and allows user input for stock symbols.
https://github.com/mehraaaaa24/stockpriceprediction
jupyter-notebook keras lstm-neural-networks matplotlib numpy pandas python streamlit tensorflow yfinance
Last synced: about 1 month ago
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A real-time web app that predicts stock prices using an LSTM model, visualizes data with moving averages, and allows user input for stock symbols.
- Host: GitHub
- URL: https://github.com/mehraaaaa24/stockpriceprediction
- Owner: mehraaaaa24
- Created: 2024-09-18T05:54:45.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-09-18T06:44:05.000Z (about 1 year ago)
- Last Synced: 2025-04-05T20:32:05.524Z (7 months ago)
- Topics: jupyter-notebook, keras, lstm-neural-networks, matplotlib, numpy, pandas, python, streamlit, tensorflow, yfinance
- Language: Jupyter Notebook
- Homepage:
- Size: 2.05 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Stock Market Predictor
This project leverages machine learning to predict stock prices using **LSTM (Long Short-Term Memory)** neural networks. The model is trained on historical stock data and visualized using various technical indicators such as moving averages (MA50, MA100, MA200). The application is deployed as a real-time web app using **Streamlit**, allowing users to input a stock symbol and view the model's predictions alongside actual stock prices.
### Tech Stack:
- **Python**: Core programming language
- **TensorFlow & Keras**: For building and training the neural network model
- **Pandas**: For data manipulation and preprocessing
- **NumPy**: For numerical operations
- **yfinance**: To fetch historical stock data
- **Matplotlib**: For visualizing stock trends and predictions
- **MinMaxScaler (from sklearn)**: For data normalization
- **Streamlit**: For creating a real-time web application interface
- **Jupyter Notebook**: For development and experimentation
### Features:
- Predict stock prices using a pre-trained LSTM model
- Compare real prices with predictions
- Visualize stock prices alongside moving averages (MA50, MA100, MA200)
- Real-time data input via Streamlit web interface