Create README.md

README.md

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+# Champion Predictor Model
+
+This repository contains the files for an XGBoost-based Champion Predictor model. The model predicts champions based on input features.
+
+## Files
+
+- **champion_predictor.json**: Serialized XGBoost model saved in JSON format.
+- **label_encoder.joblib**: Label encoder used for encoding and decoding champion names.
+- **training_feature.csv**: Dataset used for training the model.
+
+## How to Use
+
+1. Clone the repository:
+
+   ```bash
+   git clone https://huggingface.co/USERNAME/champion-predictor
+   cd champion-predictor
+   ```
+
+2. Load the model in your Python code:
+
+   ```python
+   import xgboost as xgb
+   import joblib
+   import pandas as pd
+
+   # Load model
+   model = xgb.Booster()
+   model.load_model("champion_predictor.json")
+
+   # Load label encoder
+   label_encoder = joblib.load("label_encoder.joblib")
+
+   # Example usage
+   input_features = pd.read_csv("training_feature.csv").iloc[0:1, :-1]  # Example input
+   prediction = model.predict(xgb.DMatrix(input_features))
+   predicted_label = label_encoder.inverse_transform([prediction.argmax()])
+   print(f"Predicted Champion: {predicted_label[0]}")
+   ```
+
+## Acknowledgments
+
+This model was developed as part of the ID2223 Scalable Machine Learning and Deep Learning course.