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Medical_Insurance_Cost_Prediction_Web_App.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Mon Feb  6 09:56:29 2023
+
+@author: HP
+"""
+
+
+import numpy as np
+import pickle
+import streamlit as st
+
+# loading the saved model
+#loaded_model = pickle.load(open('medical_insurance_cost_predictor.sav', 'rb'))
+loaded_model = pickle.load(open('medical_insurance_cost_predictor.h5', 'rb'))
+
+#creating a function for Prediction
+def medical_insurance_cost_prediction(input_data):
+    # changing the input_data to numpy array
+    input_data_as_numpy_array = np.asarray(input_data)
+
+    # reshape the array as we are predicting for one instance
+    input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)
+
+    prediction = loaded_model.predict(input_data_reshaped)
+    print(prediction)
+
+    return prediction
+
+def main():
+    
+    #giving a title
+    #st.title('Medical Insurance Prediction Web App')
+    st.title('It is working!!!')
+    
+    #getting input from the user
+    
+    age = st.text_input('Age')
+    sex = st.text_input('Sex: 0 -> Female, 1 -> Male')
+    bmi = st.text_input('Body Mass Index')
+    children = st.text_input('Number of Children')
+    smoker = st.text_input('Smoker: 0 -> No, 1 -> Yes')
+    region = st.text_input('Region of Living: 0 -> NorthEast, 1-> NorthWest, 2-> SouthEast, 3-> SouthWest')
+    
+    #code for prediction
+    diagnosis = ''
+    
+    # getting the input data from the user
+    #if st.button('Predicted Medical Insurance Cost: '):
+    if st.button('Hit this button!: '):
+        diagnosis = medical_insurance_cost_prediction([age,sex,bmi,children,smoker,region])
+        
+    st.success(diagnosis)
+    
+
+if __name__ == '__main__':
+    main()

medical_insurance_cost_predictor.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:22becafe6edf355298c0e9ebacf23ead2666cca59e9b1ea10ff690a0d8635711
+size 15398473

requirements.txt

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+numpy==1.21.4
+pickle-mixin==1.0.2
+streamlit==1.2.0
+scikit-learn==1.0.1