ivwhy/champion-predictor-model

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:

   git clone https://huggingface.co/USERNAME/champion-predictor
   cd champion-predictor
   

2. Load the model in your Python code:

   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.