Update README.md

README.md

@@ -9,17 +9,35 @@ tags:
 **Model Description**
 
 The magnet Curie temperature (Tc [K]) predictor model has been trained using a supervised learning approach on a specific set of magnet classes having 14:2:1 phases.
+The dataset to train the Tc prediction model is a distinct literature source.
+Further, the Tc values for various 14:2:1 magnet phases at room temperature are considered for dataset creation.
+
 It predicts the Tc value using the chemical composition as a feature.
-E.g: To predict the Tc value Nd2Fe14B1 magnet composition, the features are Nd=2, Fe=14, and B=1.
 
+E.g: To predict the Tc value Nd2Fe14B1 magnet composition, the features are Nd=2, Fe=14, and B=1.
 
 **Application & Limitations**
 
-The trained model is valid for 14:2:1 phases only, which are stoichiometric compositions.
+The trained model is valid for 14:2:1 phases only, which are stoichiometric compositions and the predicted Tc value is in Kelvin and at room temperature.
 
 **Model Plot**
 
 **How to use the trained model for inference**
 
+```python
+import json
+from joblib
+import pandas as pd
+
+Tc_predictor = load('Magnet_Tc_predictor.joblib') # trained model
+config = json.load(open('config.json')) # config file
+features = config['features'] # feature extraction
+
+#data = pd.read_excel("data.xlsx") # read test file with new compositions 
+data = data[features]
+#data.columns = ["feat_" + str(col) for col in data.columns]
 
+Predicted_value = Tc_predictor.predict(data) # predict Tc values
+print("Predicted Tc value is: {0:.2f}'.format(predictions)")
 
+```