Update README.md

README.md

@@ -94,11 +94,13 @@ E.g: To predict the Tc value Nd2Fe14B1 magnet composition, the features are Nd=2
 
 **Application & Limitations**
 
+Input feature as the chemical composition of the test sample should match the sequence of the features described in the config file.
+
 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 pipeline**
 
-The voting regressor to predict the Tc combines the following four base models and equal weight is assigned to each base models.
+The voting regressor to predict the Tc combines the following four base models and equal weight is assigned to each base model.
 
 1. Extra tree regressor (ET)
 2. Extreme gradient boosting (XGB)
@@ -117,9 +119,8 @@ 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 = 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)")