stock model

model_card.yaml

@@ -0,0 +1,81 @@
+---
+model_name: "Stock Price Predictor (XGBoost)"
+version: "1.0"
+model_type: "XGBoost Regressor"
+description: |
+  This model predicts future stock prices based on historical data.
+  It is trained on Google stock price data (2020-2025) and forecasts 
+  the Adjusted Closing Price (`Adj Close`).
+
+  The model uses various stock indicators (Open, Close, High, Low, Volume)
+  and time-based features (Year, Day, Month, Weekday) with cyclic encoding.
+  
+  **Ideal Use Case**: Time-series forecasting for stock prices.
+  
+license: "MIT"
+dataset: 
+  name: "Stock Price Data (2020-2025)"
+  source: "Kaggle - https://www.kaggle.com/datasets/mzohaibzeeshan/google-stock-price-data-2020-2025-googl"
+  
+architecture: 
+  framework: "XGBoost"
+  encoding: "Sin-Cos Encoding for Month/Weekday, One-Hot Encoding for categorical data"
+
+metrics:
+  MAE: 0.212
+  RMSE: 0.357
+  R2_Score: 0.9998
+
+deployment:
+  - huggingface: "https://huggingface.co/your_username/google-stock-predictor"
+  - github: "https://github.com/your_username/google-stock-predictor"
+  - api: "FastAPI/Gradio"
+
+usage:
+  installation: |
+    # Install dependencies
+    pip install xgboost pandas numpy joblib transformers torch
+  load_model_torch: |
+    import joblib
+    import torch
+    
+    model = joblib.load("xgboost_stock_model.pkl")
+    
+    def predict_stock_price(features):
+        features = torch.tensor(features, dtype=torch.float32)
+        prediction = model.predict(features.numpy().reshape(1, -1))
+        return prediction[0]
+    
+    sample_input = [200.5, 201.2, 199.8, 200.0, 1500000, 2025, 1, 0.0, 1.0, 0.5, 0.866]
+    predicted_price = predict_stock_price(sample_input)
+    print(f"📈 Predicted Stock Price: {predicted_price}")
+  load_model_transformers: |
+    from transformers import PreTrainedModel, PretrainedConfig
+    import joblib
+    import torch
+    
+    model = joblib.load("xgboost_stock_model.pkl")
+    
+    class StockPricePredictorConfig(PretrainedConfig):
+        model_type = "xgboost_stock_predictor"
+    
+    class StockPricePredictor(PreTrainedModel):
+        config_class = StockPricePredictorConfig
+    
+        def __init__(self, config):
+            super().__init__(config)
+            self.model = model  
+    
+        def forward(self, input_features):
+            input_features = input_features.numpy().reshape(1, -1)
+            prediction = self.model.predict(input_features)
+            return torch.tensor(prediction, dtype=torch.float32)
+    
+    config = StockPricePredictorConfig()
+    predictor = StockPricePredictor(config)
+    
+    sample_input = torch.tensor([[200.5, 201.2, 199.8, 200.0, 1500000, 2025, 1, 0.0, 1.0, 0.5, 0.866]], dtype=torch.float32)
+    predicted_price = predictor.forward(sample_input)
+    print(f"📈 Predicted Stock Price (Transformers): {predicted_price.item()}")
+
+---