feat(Models): added MAE & RMSE metrics, fixed NaN & Inf values in data,

utils/evaluate.py

@@ -0,0 +1,9 @@
+from math import sqrt
+from sklearn.metrics import mean_squared_error, mean_absolute_error
+
+def print_metrics(y_true, y_pred):
+    rmse = sqrt(mean_squared_error(y_true, y_pred))
+    print("RMSE: %.2f" % rmse)
+
+    mae = mean_absolute_error(y_true, y_pred)
+    print("MAE: %.2f" % mae)