Updated README

README.md

@@ -1,3 +1,60 @@
 ---
+tags:
+- tabular
+- regression
+- tabular-regression
+- dota
+---
+
+## Validation Metrics
+
+- R2: 0.63
+- MSE: 2428.91
+- MAE: 34.33
+- RMSE: 49.28
+
+## Usage
+
+```python
+import numpy as np
+from numpy import random
+import pandas as pd
+import onnxruntime as ort
+
+# Load the saved file
+model_path = "rd2l_forest.onnx"
+session = ort.InferenceSession(model_path)
+
+# Define default naming scheme
+input_name = session.get_inputs()[0].name
+output_name = session.get_outputs()[0].name
+
+def prediction(input_data : np.ndarray) -> float
+    """
+    Performs inference on the loaded ONNX model using the provided input data.
+
+    Args:
+        input_data (np.ndarray): An array of size (263,), this represents all of a singular players information
+
+    Returns:
+        float: The predicted cost of the player
+
+    """
+    
+    # Convert to onnx input format and reshape 
+    input_data = input_data.to_numpy(dtype=np.float32).reshape(1, -1)
+
+    # Create prediction
+    predictions = session.run([output_name], {input_name: input_data})
+
+    # Convert to individual value
+    return round(float(predictions[0][0][0]), 2)
+
+sample_df = pd.DataFrame(np.random.rand(263))
+
+prediction(sample_df)
+```
+---
 license: mit
 ---
+