diff --git a/api.py b/api.py
index a206495..89bf4c6 100644
--- a/api.py
+++ b/api.py
@@ -30,7 +30,7 @@
 # Load our model into memory.
 # Please update this path to reflect your own trained model.
 static_model = load_model(
-    path_to_model='assets/trained-models/apples_simple_lm_regression.pkl')
+    path_to_model='assets/trained-models/mlr_model.pkl')
 
 print ('-'*40)
 print ('Model succesfully loaded')
diff --git a/assets/trained-models/mlr_model.pkl b/assets/trained-models/mlr_model.pkl
new file mode 100644
index 0000000..49209e6
Binary files /dev/null and b/assets/trained-models/mlr_model.pkl differ
diff --git a/model.py b/model.py
index d72c14b..391f98b 100644
--- a/model.py
+++ b/model.py
@@ -48,7 +48,7 @@ def _preprocess_data(data):
     # Convert the json string to a python dictionary object
     feature_vector_dict = json.loads(data)
     # Load the dictionary as a Pandas DataFrame.
-    feature_vector_df = pd.DataFrame.from_dict([feature_vector_dict])
+    df_train = pd.DataFrame.from_dict([feature_vector_dict])
 
     # ---------------------------------------------------------------
     # NOTE: You will need to swap the lines below for your own data
@@ -60,13 +60,25 @@ def _preprocess_data(data):
 
     # ----------- Replace this code with your own preprocessing steps --------
     
-
-    feature_vector_df = feature_vector_df[(feature_vector_df['Commodities'] == 'APPLE GOLDEN DELICIOUS')]
-    predict_vector = feature_vector_df[['Total_Qty_Sold','Stock_On_Hand']]
+    #Filtering Commodities for APPLE GOLDEN DELICIOUS
+    df_train = df_train[df_train['Commodities']=='APPLE GOLDEN DELICIOUS']   
+    
+    #Removing negative values
+    df_train = df_train._get_numeric_data()
+    df_train[df_train < 0] = 0
+        
+    #Removing all infinity and 'not a number' values
+    df_train = df_train.replace([np.inf, -np.inf, 0], np.nan).dropna(axis=0)
+    
+    #Creating data sets
+    train = df_train[['Weight_Kg','Low_Price', 'High_Price', 'Sales_Total', 'Total_Qty_Sold','Total_Kg_Sold']]
+    
+    #feature_vector_df = feature_vector_df[(feature_vector_df['Commodities'] == 'APPLE GOLDEN DELICIOUS')]
+    #predict_vector = feature_vector_df[['Total_Qty_Sold','Stock_On_Hand']]
                                 
     # ------------------------------------------------------------------------
 
-    return predict_vector
+    return train
 
 def load_model(path_to_model:str):
     """Adapter function to load our pretrained model into memory.
diff --git a/utils/request.py b/utils/request.py
index 79cfe84..0d9773f 100644
--- a/utils/request.py
+++ b/utils/request.py
@@ -36,7 +36,7 @@
 # replace the URL below with its public IP:
 
 # url = 'http://{public-ip-address-of-remote-machine}:5000/api_v0.1'
-url = 'http://127.0.0.1:5000/api_v0.1'
+url = 'http://34.245.158.7:5000/api_v0.1'
 
 # Perform the POST request.
 print(f"Sending POST request to web server API at: {url}")