app

Decision_Tree.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:15e19bf1a48e7158163473af66ab56d3216f4141cf6119013fd2fdec56169282
+size 16075909

LGBM.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c1a1f8fb981a0fa244f0f9461ad2f3a451d051609df023a82cd3f32171ed4ed2
+size 2778630

Lasso.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8cae9f7ca9ee23839a66e47775f67bb79f34fb052a340b929e76e059f494805e
+size 2147

Logistic_Regression.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:663c195b2d85045a5a3e2421fb9757300d9fcd0d3d48c2849e5f8c52edf0e025
+size 292176

Neural_Network.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:430c352614a9a4d2dc79e3eac74dd0f49cc4be01fa6f43afed7ed23fdcdfa981
+size 1830284

Pycaret_Best.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f995fdb8f196096f1d6e9480ad35b60af480702ba5624adc50ad799c157b02c
+size 112720

Random_Forest.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:64da1f6e7a4ae0019963f534c153edfc2202fc2cfae9720dff29a989d5ebb4f2
+size 1087171329

Support_Vector_Machine.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0d99ed01a0617f698f391d43d3c0828791bed66951654982a2d6341917c72f30
+size 735512

app.py

@@ -26,7 +26,7 @@ with col1:
 with col2:
      st.title('Customer Value Prediction Model')
 
-train_df,test_df = model_value_prediction.get_data()
+train_df= model_value_prediction.get_data()
 
 
 option2 = st.selectbox(
@@ -80,6 +80,7 @@ elif option3 == 'PandasProfiling':
 
 
 model_names = [
+     "LGBM",
      "Logistic_Regression",
      "Support_Vector_Machine",
      "Support_Vector_Machine_Optimized",
@@ -87,7 +88,6 @@ model_names = [
      "Neural_Network",
      "Random_Forest",
      "Pycaret_Best",
-     "LGBM",
      "Lasso"
      ]
 
@@ -96,19 +96,20 @@ option = st.selectbox(
      'Select a model to be used',
      model_names
      )
-tr_df,te_df=model_value_prediction.important_feat(train_df,test_df,option)
+tr_df=model_value_prediction.important_feat(train_df,option)
 model = pickle.load(open(option+'.pkl', 'rb'))
 
 st.write("Model Loaded :  ", option)
 
-train_X,test_X,train_y,dev_X,val_X,dev_y,val_y,test_y= model_value_prediction.preprocess_inputs(tr_df, te_df,option)
+train_X,test_X,train_y,dev_X,val_X,dev_y,val_y,test_y= model_value_prediction.preprocess_inputs(tr_df,option)
 
 model = model_value_prediction.train(tr_df,option)
-y_pred = model.predict(test_X)
+y_pred = model_value_prediction.predict(test_X,model,option)
 if option=="LGBM":
-    st.write("LGBM Score:",model.score(test_y, y_pred))
+    st.write("LGBM Score:",metrics.mean_squared_error(test_y, y_pred,squared=False))
 elif option=="Pycaret_Best":
     predict_model(model)
     st.write("Pycaret_Best Score:",pull())
 else:
-    st.write("RMSE Score:",metrics.mean_squared_log_error(test_y, y_pred,squared=False))
+    st.write("RMSLE Score:",metrics.mean_squared_error(test_y, y_pred,squared=False))
+    st.write("Poisson Score:",metrics.mean_tweedie_deviance(test_y, y_pred))