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  pipeline_tag: tabular-regression
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  tags:
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  - stock-prediction
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- - finance
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  - deep-learning
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- - keras
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- - tensorflow
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  ---
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- # πŸ“ˆ Stock Price Prediction Model
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- This model predicts the closing stock price using historical market data.
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- It uses a deep learning LSTM model trained on Yahoo Finance data.
 
 
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  ## πŸš€ How to Use
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  ```python
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  import requests
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- API_URL = "https://api-inference.huggingface.co/models/YourUserName/YourModelName"
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- headers = {"Authorization": "Bearer YOUR_HUGGINGFACE_TOKEN"}
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  data = {
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- "inputs": "RELIANCE.NS"
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  }
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  response = requests.post(API_URL, headers=headers, json=data)
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  print(response.json())
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- ```
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-
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- ## πŸ”₯ Example
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-
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- **Input:**
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- ```json
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- {"inputs": "RELIANCE.NS"}
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- ```
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-
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- **Output:**
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- ```json
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- {"prediction": 2978.45}
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- ```
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-
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- ## πŸ‘¨β€πŸ’» Author
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- - SelvaprakashV
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- ## πŸ“œ License
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- Apache 2.0
 
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  pipeline_tag: tabular-regression
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  tags:
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  - stock-prediction
 
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  - deep-learning
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+ - finance
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+ - stock-market
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  ---
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+ # πŸ“ˆ Stock Prediction Model
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+ This is a Deep Learning based **Stock Price Prediction** model, trained to forecast future stock prices based on historical data trends.
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+ It uses LSTM (Long Short-Term Memory) networks for time-series analysis.
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+
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+ ---
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  ## πŸš€ How to Use
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+ You can use this model directly with the Hugging Face Inference API:
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+
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  ```python
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  import requests
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+ API_URL = "https://api-inference.huggingface.co/models/SelvaprakashV/stock-prediction-model"
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+ headers = {"Authorization": f"Bearer YOUR_HUGGINGFACE_API_TOKEN"}
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  data = {
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+ "inputs": "POWERGRID.NS" # Replace with your stock symbol
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  }
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  response = requests.post(API_URL, headers=headers, json=data)
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  print(response.json())
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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