mdified with inversed scaled data

app.py

@@ -82,14 +82,20 @@ for i in range(100,(int(df_final.shape[0])+1) ):
 
 for i in range(100,df_final.shape[0]):
   # x_test.append(df_final[i-100:i])
-  y_test.append(df_final[i,0])
+  y_test.append(df_final[i])
 
 x_test, y_test = np.array(x_test), np.array(y_test)  
 
 y_pred = model.predict(x_test)
 
+y_pred_tmp = y_pred
+y_test_tmp = y_test
+y_pred_tmp = scaler.inverse_transform(y_pred_tmp)
+y_test_tmp = np.reshape(y_test_tmp,(-1,1))
+y_test_tmp = scaler.inverse_transform(y_test_tmp)
+
 scaler_val = scaler.scale_
-scale_factor = 1/0.00615148
+scale_factor = 1/scaler_val
 y_pred = y_pred*scale_factor
 y_test = y_test*scale_factor
 
@@ -104,8 +110,8 @@ plt.legend()
 st.pyplot(fig2)
 
 # also printing the next day closing price
-last_predicted_price = round(float(y_pred[-1]),2)
-actual_closing_price = round(float(y_test[-1]),2)
+last_predicted_price = round(float(y_pred_tmp[-1]),2)
+actual_closing_price = round(float(y_test_tmp[-1]),2)
 
 # ls = "Predicted Upcoming Trading Day Closing Price: " 
 st.subheader("Predicted Upcoming Trading Day Closing Price: ")