---
title: Champion Predictor Model
author: Group 43 ID2223 HT24
description: A repository containing an XGBoost-based Champion Predictor model to predict champions based on input features.
---

# 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.