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app.py.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+#Importing the dependencies
+import pandas as pd
+import numpy as np 
+import streamlit as st
+
+
+# Loading the Dataset
+RtData = pd.read_csv('RestaurantRatingData.csv', encoding='latin')
+# Selecting the restaurants located in India
+RtData = RtData[(RtData.Currency == "Indian Rupees(Rs.)")]
+# Removing the data where Average cost is 0
+RtData = RtData.loc[(RtData['Average Cost for two'] > 0)]
+
+
+# Deleting those columns which are not useful in predictive analysis because these variables are qualitative
+UselessColumns = ['Restaurant ID', 'Restaurant Name','City','Address',
+                  'Locality', 'Locality Verbose','Cuisines']
+RtData = RtData.drop(UselessColumns,axis=1)
+RtData.head(5)
+
+RtData.rename(columns={'Has Table booking': 'Has_Table_booking', 'Has Online delivery' : 'Has_Online_delivery', 'Average Cost for two':'Average_Cost_for_two', 'Price range':'Price_range'}, inplace=True)
+
+# Finding nearest values to 4000 mark 
+RtData['Votes'][RtData['Votes']<4000].sort_values(ascending=False)
+
+# Above result shows the nearest logical value is 3986, hence, replacing any value above 4000 with it.
+# Replacing outliers with nearest possibe value
+RtData['Votes'][RtData['Votes']>4000] =3986
+
+# Above result shows the nearest logical value is 8000, hence, replacing any value above 50000 with it.
+## Replacing outliers with nearest possibe value
+RtData['Average_Cost_for_two'][RtData['Average_Cost_for_two']>50000] = 8000
+
+#Final Selected Predictors
+SelectedColumns=['Votes','Average_Cost_for_two','Has_Table_booking',
+                 'Has_Online_delivery','Price_range']
+
+# Selecting final columns
+DataForML=RtData[SelectedColumns]
+
+# Converting the binary nominal variable sex to numeric
+DataForML['Has_Table_booking'].replace({'Yes':1, 'No':0}, inplace=True)
+DataForML['Has_Online_delivery'].replace({'Yes':1, 'No':0}, inplace=True)
+
+# Treating all the nominal variables at once using dummy variables
+DataForML_Numeric=pd.get_dummies(DataForML)
+
+# Adding Target Variable to the data
+DataForML_Numeric['Rating']=RtData['Rating']
+
+# Printing sample rows
+DataForML_Numeric.head()
+
+# Separate Target Variable and Predictor Variables
+TargetVariable='Rating'
+Predictors=['Votes', 'Average_Cost_for_two', 'Has_Table_booking',
+           'Has_Online_delivery', 'Price_range']
+
+X=DataForML_Numeric[Predictors].values
+y=DataForML_Numeric[TargetVariable].values
+
+# Split the data into training and testing set
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=428)
+
+# XGBOOST Model
+# Xtreme Gradient Boosting (XGBoost)
+from xgboost import XGBRegressor
+RegModel=XGBRegressor(max_depth=2, learning_rate=0.1, verbosity = 0, silent=True, n_estimators=1000, objective='reg:linear', booster='gbtree')
+
+# Printing all the parameters of XGBoost
+print(RegModel)
+
+# Creating the model on Training Data
+XGB=RegModel.fit(X_train,y_train)
+prediction=XGB.predict(X_test)
+
+@st.cache()
+
+# Defining the function which will make the prediction using the data which the user inputs 
+ 
+
+def prediction(Votes, Average_Cost_for_two, Has_Table_booking, Has_Online_delivery, Price_range):   
+    pred = None
+   
+    if Has_Table_booking  == "No":
+        Has_Table_booking = 0
+    else:
+        Has_Table_booking = 1
+ 
+    if Has_Online_delivery == "No":
+        Has_Online_delivery = 0
+    else:
+        Has_Online_delivery = 1
+ 
+      
+     # Making predictions 
+    pred_inputs = XGB.predict(pd.DataFrame([[Votes, Average_Cost_for_two, Has_Table_booking, Has_Online_delivery, Price_range]]))
+
+    
+    if pred_inputs[0] <= 2:
+        pred = 'It is a Low Rated Restaurant.'
+    elif ((pred_inputs[0] >= 3) and (pred_inputs[0] <= 4)):
+        pred = 'It is a Decent Rated Restaurant'
+    elif pred_inputs[0] >= 4:
+        pred = 'It is a High Rated Restaurant'
+
+    return pred
+
+        
+    
+
+def main():       
+    # front end elements of the web page 
+    html_temp = """ 
+    <div style ="background-color:orange;padding:13px"> 
+    <h1 style ="color:black;text-align:center;"> Restaurant Rating
+     Prediction App</h1>
+     <h8 style ="color:black;text-align:center;"> The data from an online food app, 
+     which needs assistance in predicting the future success or failure of a business (restaurant),
+      has been used in this case study. Such that they can choose whether to delete the restaurant 
+      from their app or keep it. They have provided information from of 8643 eateries from different 
+      states of India that are currently accessible on their app. It contains details about the 
+      restaurants, including the overall rating. Below I have developed a machine learning model 
+      that can predict a restaurant's rating based on its attributes.</h8> 
+    </div> 
+    """
+    
+     # Display dataset when check box is ON
+    if st.checkbox('View dataset in table data format'):
+       st.dataframe(RtData)
+
+    # display the front end aspect
+    st.markdown(html_temp, unsafe_allow_html = True) 
+      
+    # following lines create boxes in which user can enter data required to make prediction 
+    Votes = st.number_input("No. of Votes (Range between 0 to 2500)")
+    Average_Cost_for_two= st.number_input("Cost of 2 person between 50 to 8000 (Indian Rupees(Rs.))")
+    Price_range = st.number_input("Price Range between 1(Inexpensive) to 4(Most Expensive)")
+    Has_Table_booking= st.selectbox(' Has Table Booking',("Yes","No"))
+    Has_Online_delivery= st.selectbox(' Has Online Delivery',("Yes","No"))
+    result =""
+
+
+    # when 'Predict' is clicked, make the prediction and store it 
+    if st.button("Predict"): 
+        result = prediction(Votes, Average_Cost_for_two, Has_Table_booking, Has_Online_delivery, Price_range) 
+        st.success('Final Decision: {}'.format(result))
+        
+     
+if __name__=='__main__': 
+    main()
+