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while_loop_last.py
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while_loop_last.py
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#importing packages to read the csv files
import pandas as pd
import numpy as np
import math
#reading the csv files
default_quantity_data_A=pd.read_csv('DEFAULT_A.csv')
default_quantity_data_B=pd.read_csv('DEFAULT_B.csv')
default_quantity_data_C=pd.read_csv('DEFAULT_C.csv')
pizza_data=pd.read_csv('pizza.csv')
burger_data=pd.read_csv('Burger.csv')
non_veg_thali_data=pd.read_csv('Non_veg_thali.csv')
veg_thali_data=pd.read_csv('veg_thali.csv')
dosa_data=pd.read_csv('Dosa.csv')
sandwich_data=pd.read_csv('Sandwich.csv')
pav_bhaji_data=pd.read_csv('Pav_bhaji.csv')
misal_data=pd.read_csv('Misal.csv')
idli_data=pd.read_csv('idli.csv')
kichdi_data=pd.read_csv('kichdi.csv')
#importing the encoding library to convert string values to numeric values
from sklearn.preprocessing import LabelEncoder
labelencoder_X=LabelEncoder()
#importing the decision tree model
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor(random_state = 0)
#importing the train test split
from sklearn.model_selection import train_test_split
#analysis for restaurant A
#pizza analysis for restaurant A
group_data_pizza_A=pizza_data.groupby("Restaurant")
pizza_for_rest_A=group_data_pizza_A.get_group('A')
weekday_input_pizza_A=pizza_for_rest_A[['Weekday']]
weekday_input_pizza_A=weekday_input_pizza_A.apply(labelencoder_X.fit_transform)
pizza_out=pizza_for_rest_A[['Pizza']]
X_train_pizza_A,X_test_pizza_A,Y_train_pizza_A,Y_test_pizza_A = train_test_split(weekday_input_pizza_A,pizza_out,test_size=0.2,random_state=0)
regressor.fit(X_train_pizza_A, Y_train_pizza_A)
'''
The label enocoding for pizza is as follows
Monday-1
Tuesday-5
Wednesday-6
Thursday-4
Friday-0
Saturday-2
Sunday-3
'''
pizza_pred_A_Monday = int(regressor.predict([[1]]))
pizza_pred_A_Tuesday = int(regressor.predict([[5]]))
pizza_pred_A_Wednesday = int(regressor.predict([[6]]))
pizza_pred_A_Thursday = int(regressor.predict([[4]]))
pizza_pred_A_Friday = int(regressor.predict([[0]]))
pizza_pred_A_Saturday = int(regressor.predict([[1]]))
pizza_pred_A_Sunday = int(regressor.predict([[3]]))
#reading the default csv file
weekly_pizza_pred_A= pizza_pred_A_Monday+pizza_pred_A_Tuesday+pizza_pred_A_Wednesday+pizza_pred_A_Thursday+pizza_pred_A_Friday+pizza_pred_A_Saturday+pizza_pred_A_Sunday
default_quantity_data_A_pizza=default_quantity_data_A.iloc[0,].values
pizza_A_list=default_quantity_data_A_pizza.tolist()
pizza_A_list=pizza_A_list[1:]
pizza_A_tomato=pizza_A_list[0]*weekly_pizza_pred_A
pizza_A_onion=pizza_A_list[1]*weekly_pizza_pred_A
pizza_A_capsicum=pizza_A_list[2]*weekly_pizza_pred_A
pizza_A_bread=pizza_A_list[3]*weekly_pizza_pred_A
pizza_A_dough=pizza_A_list[4]*weekly_pizza_pred_A
pizza_A_chicken=pizza_A_list[5]*weekly_pizza_pred_A
pizza_A_cheese=pizza_A_list[6]*weekly_pizza_pred_A
pizza_A_corn=pizza_A_list[7]*weekly_pizza_pred_A
pizza_A_rava=pizza_A_list[8]*weekly_pizza_pred_A
pizza_A_sabudana=pizza_A_list[9]*weekly_pizza_pred_A
pizza_A_masala=pizza_A_list[10]*weekly_pizza_pred_A
pizza_A_vegetables=pizza_A_list[11]*weekly_pizza_pred_A
pizza_A_dal=pizza_A_list[12]*weekly_pizza_pred_A
pizza_A_flour=pizza_A_list[13]*weekly_pizza_pred_A
pizza_A_rice=pizza_A_list[14]*weekly_pizza_pred_A
pizza_A_papad=pizza_A_list[15]*weekly_pizza_pred_A
pizza_A_butter=pizza_A_list[16]*weekly_pizza_pred_A
#Burger Analysis for restaurant A
group_data_burger_A=burger_data.groupby("Restaurant")
burger_for_rest_A=group_data_burger_A.get_group('A')
weekday_input_burger_A=burger_for_rest_A[['Weekday']]
weekday_input_burger_A=weekday_input_burger_A.apply(labelencoder_X.fit_transform)
burger_out=burger_for_rest_A[['Burger']]
X_train_burger_A,X_test_burger_A,Y_train_burger_A,Y_test_burger_A = train_test_split(weekday_input_burger_A,burger_out,test_size=0.2,random_state=0)
regressor.fit(X_train_burger_A, Y_train_burger_A)
burger_sum=0
for i in range(0,7):
burger_predict_sum=regressor.predict([[i]])
burger_sum=burger_sum+burger_predict_sum
burger_sum=int(burger_sum)
default_quantity_data_A_burger=default_quantity_data_A.iloc[1,].values
burger_A_list=default_quantity_data_A_burger.tolist()
burger_A_list=burger_A_list[1:]
burger_A_tomato=burger_A_list[0]*burger_sum
burger_A_onion=burger_A_list[1]*burger_sum
burger_A_capsicum=burger_A_list[2]*burger_sum
burger_A_bread=burger_A_list[3]*burger_sum
burger_A_dough=burger_A_list[4]*burger_sum
burger_A_chicken=burger_A_list[5]*burger_sum
burger_A_cheese=burger_A_list[6]*burger_sum
burger_A_corn=burger_A_list[7]*burger_sum
burger_A_rava=burger_A_list[8]*burger_sum
burger_A_sabudana=burger_A_list[9]*burger_sum
burger_A_masala=burger_A_list[10]*burger_sum
burger_A_vegetables=burger_A_list[11]*burger_sum
burger_A_dal=burger_A_list[12]*burger_sum
burger_A_flour=burger_A_list[13]*burger_sum
burger_A_rice=burger_A_list[14]*burger_sum
burger_A_papad=burger_A_list[15]*burger_sum
burger_A_butter=burger_A_list[16]*burger_sum
#Non veg thali prediction for resturant A
group_data_nonveg_A=non_veg_thali_data.groupby("Restaurant")
nonveg_for_rest_A=group_data_nonveg_A.get_group('A')
weekday_input_nonveg_A=nonveg_for_rest_A[['Weekday']]
weekday_input_nonveg_A=weekday_input_nonveg_A.apply(labelencoder_X.fit_transform)
nonveg_out=nonveg_for_rest_A[['Non Veg Thali']]
X_train_nonveg_A,X_test_nonveg_A,Y_train_nonveg_A,Y_test_nonveg_A = train_test_split(weekday_input_nonveg_A,nonveg_out,test_size=0.2,random_state=0)
regressor.fit(X_train_nonveg_A, Y_train_nonveg_A)
nonveg_sum=0
for i in range(0,7):
nonveg_predict_sum=regressor.predict([[i]])
nonveg_sum=nonveg_sum+nonveg_predict_sum
nonveg_sum=int(nonveg_sum)
default_quantity_data_A_nonveg=default_quantity_data_A.iloc[2,].values
nonveg_A_list=default_quantity_data_A_nonveg.tolist()
nonveg_A_list=nonveg_A_list[1:]
nonveg_A_tomato=nonveg_A_list[0]*nonveg_sum
nonveg_A_onion=nonveg_A_list[1]*nonveg_sum
nonveg_A_capsicum=nonveg_A_list[2]*nonveg_sum
nonveg_A_bread=nonveg_A_list[3]*nonveg_sum
nonveg_A_dough=nonveg_A_list[4]*nonveg_sum
nonveg_A_chicken=nonveg_A_list[5]*nonveg_sum
nonveg_A_cheese=nonveg_A_list[6]*nonveg_sum
nonveg_A_corn=nonveg_A_list[7]*nonveg_sum
nonveg_A_rava=nonveg_A_list[8]*nonveg_sum
nonveg_A_sabudana=nonveg_A_list[9]*nonveg_sum
nonveg_A_masala=nonveg_A_list[10]*nonveg_sum
nonveg_A_vegetables=nonveg_A_list[11]*nonveg_sum
nonveg_A_dal=nonveg_A_list[12]*nonveg_sum
nonveg_A_flour=nonveg_A_list[13]*nonveg_sum
nonveg_A_rice=nonveg_A_list[14]*nonveg_sum
nonveg_A_papad=nonveg_A_list[15]*nonveg_sum
nonveg_A_butter=nonveg_A_list[16]*nonveg_sum
#Veg Thali analysis for restauarant A
group_data_veg_A=veg_thali_data.groupby("Restaurant")
veg_for_rest_A=group_data_veg_A.get_group('A')
weekday_input_veg_A=veg_for_rest_A[['Weekday']]
weekday_input_veg_A=weekday_input_veg_A.apply(labelencoder_X.fit_transform)
veg_out=veg_for_rest_A[['Veg Thali']]
X_train_veg_A,X_test_veg_A,Y_train_veg_A,Y_test_veg_A = train_test_split(weekday_input_veg_A,veg_out,test_size=0.2,random_state=0)
regressor.fit(X_train_veg_A, Y_train_veg_A)
veg_sum=0
for i in range(0,7):
veg_predict_sum=regressor.predict([[i]])
veg_sum=veg_sum+veg_predict_sum
veg_sum=int(veg_sum)
default_quantity_data_A_veg=default_quantity_data_A.iloc[3,].values
veg_A_list=default_quantity_data_A_veg.tolist()
veg_A_list=veg_A_list[1:]
veg_A_tomato=veg_A_list[0]*veg_sum
veg_A_onion=veg_A_list[1]*veg_sum
veg_A_capsicum=veg_A_list[2]*veg_sum
veg_A_bread=veg_A_list[3]*veg_sum
veg_A_dough=veg_A_list[4]*veg_sum
veg_A_chicken=veg_A_list[5]*veg_sum
veg_A_cheese=veg_A_list[6]*veg_sum
veg_A_corn=veg_A_list[7]*veg_sum
veg_A_rava=veg_A_list[8]*veg_sum
veg_A_sabudana=veg_A_list[9]*veg_sum
veg_A_masala=veg_A_list[10]*veg_sum
veg_A_vegetables=veg_A_list[11]*veg_sum
veg_A_dal=veg_A_list[12]*veg_sum
veg_A_flour=veg_A_list[13]*veg_sum
veg_A_rice=veg_A_list[14]*veg_sum
veg_A_papad=veg_A_list[15]*veg_sum
veg_A_butter=veg_A_list[16]*veg_sum
#Dosa analysis for restauarant A
group_data_dosa_A=dosa_data.groupby("Restaurant")
dosa_for_rest_A=group_data_dosa_A.get_group('A')
weekday_input_dosa_A=dosa_for_rest_A[['Weekday']]
weekday_input_dosa_A=weekday_input_dosa_A.apply(labelencoder_X.fit_transform)
dosa_out=dosa_for_rest_A[['Dosa']]
X_train_dosa_A,X_test_dosa_A,Y_train_dosa_A,Y_test_dosa_A = train_test_split(weekday_input_dosa_A,dosa_out,test_size=0.2,random_state=0)
regressor.fit(X_train_dosa_A, Y_train_dosa_A)
dosa_sum=0
for i in range(0,7):
dosa_predict_sum=regressor.predict([[i]])
dosa_sum=dosa_sum+dosa_predict_sum
dosa_sum=int(dosa_sum)
default_quantity_data_A_dosa=default_quantity_data_A.iloc[4,].values
dosa_A_list=default_quantity_data_A_dosa.tolist()
dosa_A_list=dosa_A_list[1:]
dosa_A_tomato=dosa_A_list[0]*dosa_sum
dosa_A_onion=dosa_A_list[1]*dosa_sum
dosa_A_capsicum=dosa_A_list[2]*dosa_sum
dosa_A_bread=dosa_A_list[3]*dosa_sum
dosa_A_dough=dosa_A_list[4]*dosa_sum
dosa_A_chicken=dosa_A_list[5]*dosa_sum
dosa_A_cheese=dosa_A_list[6]*dosa_sum
dosa_A_corn=dosa_A_list[7]*dosa_sum
dosa_A_rava=dosa_A_list[8]*dosa_sum
dosa_A_sabudana=dosa_A_list[9]*dosa_sum
dosa_A_masala=dosa_A_list[10]*dosa_sum
dosa_A_vegetables=dosa_A_list[11]*dosa_sum
dosa_A_dal=dosa_A_list[12]*dosa_sum
dosa_A_flour=dosa_A_list[13]*dosa_sum
dosa_A_rice=dosa_A_list[14]*dosa_sum
dosa_A_papad=dosa_A_list[15]*dosa_sum
dosa_A_butter=dosa_A_list[16]*dosa_sum
#Sandwich analysis for restaurant A
group_data_sandwich_A=sandwich_data.groupby("Restaurant")
sandwich_for_rest_A=group_data_sandwich_A.get_group('A')
weekday_input_sandwich_A=sandwich_for_rest_A[['Weekday']]
weekday_input_sandwich_A=weekday_input_sandwich_A.apply(labelencoder_X.fit_transform)
sandwich_out=sandwich_for_rest_A[['Sandwich']]
X_train_sandwich_A,X_test_sandwich_A,Y_train_sandwich_A,Y_test_sandwich_A = train_test_split(weekday_input_sandwich_A,sandwich_out,test_size=0.2,random_state=0)
regressor.fit(X_train_sandwich_A, Y_train_sandwich_A)
sandwich_sum=0
for i in range(0,7):
sandwich_predict_sum=regressor.predict([[i]])
sandwich_sum=sandwich_sum+sandwich_predict_sum
sandwich_sum=int(sandwich_sum)
default_quantity_data_A_sandwich=default_quantity_data_A.iloc[5,].values
sandwich_A_list=default_quantity_data_A_sandwich.tolist()
sandwich_A_list=sandwich_A_list[1:]
sandwich_A_tomato=sandwich_A_list[0]*sandwich_sum
sandwich_A_onion=sandwich_A_list[1]*sandwich_sum
sandwich_A_capsicum=sandwich_A_list[2]*sandwich_sum
sandwich_A_bread=sandwich_A_list[3]*sandwich_sum
sandwich_A_dough=sandwich_A_list[4]*sandwich_sum
sandwich_A_chicken=sandwich_A_list[5]*sandwich_sum
sandwich_A_cheese=sandwich_A_list[6]*sandwich_sum
sandwich_A_corn=sandwich_A_list[7]*sandwich_sum
sandwich_A_rava=sandwich_A_list[8]*sandwich_sum
sandwich_A_sabudana=sandwich_A_list[9]*sandwich_sum
sandwich_A_masala=sandwich_A_list[10]*sandwich_sum
sandwich_A_vegetables=sandwich_A_list[11]*sandwich_sum
sandwich_A_dal=sandwich_A_list[12]*sandwich_sum
sandwich_A_flour=sandwich_A_list[13]*sandwich_sum
sandwich_A_rice=sandwich_A_list[14]*sandwich_sum
sandwich_A_papad=sandwich_A_list[15]*sandwich_sum
sandwich_A_butter=sandwich_A_list[16]*sandwich_sum
#Pav Bhaji analysis for restaurant A
group_data_pavbhaji_A=pav_bhaji_data.groupby("Restaurant")
pavbhaji_for_rest_A=group_data_pavbhaji_A.get_group('A')
weekday_input_pavbhaji_A=pavbhaji_for_rest_A[['Weekday']]
weekday_input_pavbhaji_A=weekday_input_pavbhaji_A.apply(labelencoder_X.fit_transform)
pavbhaji_out=pavbhaji_for_rest_A[['Pav Bhaji']]
X_train_pavbhaji_A,X_test_pavbhaji_A,Y_train_pavbhaji_A,Y_test_pavbhaji_A = train_test_split(weekday_input_pavbhaji_A,pavbhaji_out,test_size=0.2,random_state=0)
regressor.fit(X_train_pavbhaji_A, Y_train_pavbhaji_A)
pavbhaji_sum=0
for i in range(0,7):
pavbhaji_predict_sum=regressor.predict([[i]])
pavbhaji_sum=pavbhaji_sum+pavbhaji_predict_sum
pavbhaji_sum=int(pavbhaji_sum)
default_quantity_data_A_pavbhaji=default_quantity_data_A.iloc[6,].values
pavbhaji_A_list=default_quantity_data_A_pavbhaji.tolist()
pavbhaji_A_list=pavbhaji_A_list[1:]
pavbhaji_A_tomato=pavbhaji_A_list[0]*pavbhaji_sum
pavbhaji_A_onion=pavbhaji_A_list[1]*pavbhaji_sum
pavbhaji_A_capsicum=pavbhaji_A_list[2]*pavbhaji_sum
pavbhaji_A_bread=pavbhaji_A_list[3]*pavbhaji_sum
pavbhaji_A_dough=pavbhaji_A_list[4]*pavbhaji_sum
pavbhaji_A_chicken=pavbhaji_A_list[5]*pavbhaji_sum
pavbhaji_A_cheese=pavbhaji_A_list[6]*pavbhaji_sum
pavbhaji_A_corn=pavbhaji_A_list[7]*pavbhaji_sum
pavbhaji_A_rava=pavbhaji_A_list[8]*pavbhaji_sum
pavbhaji_A_sabudana=pavbhaji_A_list[9]*pavbhaji_sum
pavbhaji_A_masala=pavbhaji_A_list[10]*pavbhaji_sum
pavbhaji_A_vegetables=pavbhaji_A_list[11]*pavbhaji_sum
pavbhaji_A_dal=pavbhaji_A_list[12]*pavbhaji_sum
pavbhaji_A_flour=pavbhaji_A_list[13]*pavbhaji_sum
pavbhaji_A_rice=pavbhaji_A_list[14]*pavbhaji_sum
pavbhaji_A_papad=pavbhaji_A_list[15]*pavbhaji_sum
pavbhaji_A_butter=pavbhaji_A_list[16]*pavbhaji_sum
#Misal analysis for restaurant A
group_data_misal_A=misal_data.groupby("Restaurant")
misal_for_rest_A=group_data_misal_A.get_group('A')
weekday_input_misal_A=misal_for_rest_A[['Weekday']]
weekday_input_misal_A=weekday_input_misal_A.apply(labelencoder_X.fit_transform)
misal_out=misal_for_rest_A[['Misal']]
X_train_misal_A,X_test_misal_A,Y_train_misal_A,Y_test_misal_A = train_test_split(weekday_input_misal_A,misal_out,test_size=0.2,random_state=0)
regressor.fit(X_train_misal_A, Y_train_misal_A)
misal_sum=0
for i in range(0,7):
misal_predict_sum=regressor.predict([[i]])
misal_sum=misal_sum+misal_predict_sum
misal_sum=int(misal_sum)
default_quantity_data_A_misal=default_quantity_data_A.iloc[7,].values
misal_A_list=default_quantity_data_A_misal.tolist()
misal_A_list=misal_A_list[1:]
misal_A_tomato=misal_A_list[0]*misal_sum
misal_A_onion=misal_A_list[1]*misal_sum
misal_A_capsicum=misal_A_list[2]*misal_sum
misal_A_bread=misal_A_list[3]*misal_sum
misal_A_dough=misal_A_list[4]*misal_sum
misal_A_chicken=misal_A_list[5]*misal_sum
misal_A_cheese=misal_A_list[6]*misal_sum
misal_A_corn=misal_A_list[7]*misal_sum
misal_A_rava=misal_A_list[8]*misal_sum
misal_A_sabudana=misal_A_list[9]*misal_sum
misal_A_masala=misal_A_list[10]*misal_sum
misal_A_vegetables=misal_A_list[11]*misal_sum
misal_A_dal=misal_A_list[12]*misal_sum
misal_A_flour=misal_A_list[13]*misal_sum
misal_A_rice=misal_A_list[14]*misal_sum
misal_A_papad=misal_A_list[15]*misal_sum
misal_A_butter=misal_A_list[16]*misal_sum
#idli analysis for restauarant A
group_data_idli_A=idli_data.groupby("Restaurant")
idli_for_rest_A=group_data_idli_A.get_group('A')
weekday_input_idli_A=idli_for_rest_A[['Weekday']]
weekday_input_idli_A=weekday_input_idli_A.apply(labelencoder_X.fit_transform)
idli_out=idli_for_rest_A[['idli']]
X_train_idli_A,X_test_idli_A,Y_train_idli_A,Y_test_idli_A = train_test_split(weekday_input_idli_A,idli_out,test_size=0.2,random_state=0)
regressor.fit(X_train_idli_A, Y_train_idli_A)
idli_sum=0
for i in range(0,7):
idli_predict_sum=regressor.predict([[i]])
idli_sum=idli_sum+idli_predict_sum
idli_sum=int(idli_sum)
default_quantity_data_A_idli=default_quantity_data_A.iloc[8,].values
idli_A_list=default_quantity_data_A_idli.tolist()
idli_A_list=idli_A_list[1:]
idli_A_tomato=idli_A_list[0]*idli_sum
idli_A_onion=idli_A_list[1]*idli_sum
idli_A_capsicum=idli_A_list[2]*idli_sum
idli_A_bread=idli_A_list[3]*idli_sum
idli_A_dough=idli_A_list[4]*idli_sum
idli_A_chicken=idli_A_list[5]*idli_sum
idli_A_cheese=idli_A_list[6]*idli_sum
idli_A_corn=idli_A_list[7]*idli_sum
idli_A_rava=idli_A_list[8]*idli_sum
idli_A_sabudana=idli_A_list[9]*idli_sum
idli_A_masala=idli_A_list[10]*idli_sum
idli_A_vegetables=idli_A_list[11]*idli_sum
idli_A_dal=idli_A_list[12]*idli_sum
idli_A_flour=idli_A_list[13]*idli_sum
idli_A_rice=idli_A_list[14]*idli_sum
idli_A_papad=idli_A_list[15]*idli_sum
idli_A_butter=idli_A_list[16]*idli_sum
#kichdi analysis for restaurant A
group_data_kichdi_A=kichdi_data.groupby("Restaurant")
kichdi_for_rest_A=group_data_kichdi_A.get_group('A')
weekday_input_kichdi_A=kichdi_for_rest_A[['Weekday']]
weekday_input_kichdi_A=weekday_input_kichdi_A.apply(labelencoder_X.fit_transform)
kichdi_out=kichdi_for_rest_A[['kichdi']]
X_train_kichdi_A,X_test_kichdi_A,Y_train_kichdi_A,Y_test_kichdi_A = train_test_split(weekday_input_kichdi_A,kichdi_out,test_size=0.2,random_state=0)
regressor.fit(X_train_kichdi_A, Y_train_kichdi_A)
kichdi_sum=0
for i in range(0,7):
kichdi_predict_sum=regressor.predict([[i]])
kichdi_sum=kichdi_sum+kichdi_predict_sum
kichdi_sum=int(kichdi_sum)
default_quantity_data_A_kichdi=default_quantity_data_A.iloc[9,].values
kichdi_A_list=default_quantity_data_A_kichdi.tolist()
kichdi_A_list=kichdi_A_list[1:]
kichdi_A_tomato=kichdi_A_list[0]*kichdi_sum
kichdi_A_onion=kichdi_A_list[1]*kichdi_sum
kichdi_A_capsicum=kichdi_A_list[2]*kichdi_sum
kichdi_A_bread=kichdi_A_list[3]*kichdi_sum
kichdi_A_dough=kichdi_A_list[4]*kichdi_sum
kichdi_A_chicken=kichdi_A_list[5]*kichdi_sum
kichdi_A_cheese=kichdi_A_list[6]*kichdi_sum
kichdi_A_corn=kichdi_A_list[7]*kichdi_sum
kichdi_A_rava=kichdi_A_list[8]*kichdi_sum
kichdi_A_sabudana=kichdi_A_list[9]*kichdi_sum
kichdi_A_masala=kichdi_A_list[10]*kichdi_sum
kichdi_A_vegetables=kichdi_A_list[11]*kichdi_sum
kichdi_A_dal=kichdi_A_list[12]*kichdi_sum
kichdi_A_flour=kichdi_A_list[13]*kichdi_sum
kichdi_A_rice=kichdi_A_list[14]*kichdi_sum
kichdi_A_papad=kichdi_A_list[15]*kichdi_sum
kichdi_A_butter=kichdi_A_list[16]*kichdi_sum
total_tom_A=[]
total_tom_A.append(pizza_A_tomato)
total_tom_A.append(burger_A_tomato)
total_tom_A.append(nonveg_A_tomato)
total_tom_A.append(veg_A_tomato)
total_tom_A.append(dosa_A_tomato)
total_tom_A.append(sandwich_A_tomato)
total_tom_A.append(pavbhaji_A_tomato)
total_tom_A.append(misal_A_tomato)
total_tom_A.append(idli_A_tomato)
total_tom_A.append(kichdi_A_tomato)
total_tom_A=sum(total_tom_A)
total_onion_A=[]
total_onion_A.append(pizza_A_onion)
total_onion_A.append(burger_A_onion)
total_onion_A.append(nonveg_A_onion)
total_onion_A.append(veg_A_onion)
total_onion_A.append(dosa_A_onion)
total_onion_A.append(sandwich_A_onion)
total_onion_A.append(pavbhaji_A_onion)
total_onion_A.append(misal_A_onion)
total_onion_A.append(idli_A_onion)
total_onion_A.append(kichdi_A_onion)
total_onion_A=sum(total_onion_A)
total_capsicum_A=[]
total_capsicum_A.append(pizza_A_capsicum)
total_capsicum_A.append(burger_A_capsicum)
total_capsicum_A.append(nonveg_A_capsicum)
total_capsicum_A.append(veg_A_capsicum)
total_capsicum_A.append(dosa_A_capsicum)
total_capsicum_A.append(sandwich_A_capsicum)
total_capsicum_A.append(pavbhaji_A_capsicum)
total_capsicum_A.append(misal_A_capsicum)
total_capsicum_A.append(idli_A_capsicum)
total_capsicum_A.append(kichdi_A_capsicum)
total_capsicum_A=sum(total_capsicum_A)
total_bread_A=[]
total_bread_A.append(pizza_A_bread)
total_bread_A.append(burger_A_bread)
total_bread_A.append(nonveg_A_bread)
total_bread_A.append(veg_A_bread)
total_bread_A.append(dosa_A_bread)
total_bread_A.append(sandwich_A_bread)
total_bread_A.append(pavbhaji_A_bread)
total_bread_A.append(misal_A_bread)
total_bread_A.append(idli_A_bread)
total_bread_A.append(kichdi_A_bread)
total_bread_A=sum(total_bread_A)
total_dough_A=[]
total_dough_A.append(pizza_A_dough)
total_dough_A.append(burger_A_dough)
total_dough_A.append(nonveg_A_dough)
total_dough_A.append(veg_A_dough)
total_dough_A.append(dosa_A_dough)
total_dough_A.append(sandwich_A_dough)
total_dough_A.append(pavbhaji_A_dough)
total_dough_A.append(misal_A_dough)
total_dough_A.append(idli_A_dough)
total_dough_A.append(kichdi_A_dough)
total_dough_A=sum(total_dough_A)
total_chicken_A=[]
total_chicken_A.append(pizza_A_chicken)
total_chicken_A.append(burger_A_chicken)
total_chicken_A.append(nonveg_A_chicken)
total_chicken_A.append(veg_A_chicken)
total_chicken_A.append(dosa_A_chicken)
total_chicken_A.append(sandwich_A_chicken)
total_chicken_A.append(pavbhaji_A_chicken)
total_chicken_A.append(misal_A_chicken)
total_chicken_A.append(idli_A_chicken)
total_chicken_A.append(kichdi_A_chicken)
total_chicken_A=sum(total_chicken_A)
total_cheese_A=[]
total_cheese_A.append(pizza_A_cheese)
total_cheese_A.append(burger_A_cheese)
total_cheese_A.append(nonveg_A_cheese)
total_cheese_A.append(veg_A_cheese)
total_cheese_A.append(dosa_A_cheese)
total_cheese_A.append(sandwich_A_cheese)
total_cheese_A.append(pavbhaji_A_cheese)
total_cheese_A.append(misal_A_cheese)
total_cheese_A.append(idli_A_cheese)
total_cheese_A.append(kichdi_A_cheese)
total_cheese_A=sum(total_cheese_A)
total_corn_A=[]
total_corn_A.append(pizza_A_corn)
total_corn_A.append(burger_A_corn)
total_corn_A.append(nonveg_A_corn)
total_corn_A.append(veg_A_corn)
total_corn_A.append(dosa_A_corn)
total_corn_A.append(sandwich_A_corn)
total_corn_A.append(pavbhaji_A_corn)
total_corn_A.append(misal_A_corn)
total_corn_A.append(idli_A_corn)
total_corn_A.append(kichdi_A_corn)
total_corn_A=sum(total_corn_A)
total_rava_A=[]
total_rava_A.append(pizza_A_rava)
total_rava_A.append(burger_A_rava)
total_rava_A.append(nonveg_A_rava)
total_rava_A.append(veg_A_rava)
total_rava_A.append(dosa_A_rava)
total_rava_A.append(sandwich_A_rava)
total_rava_A.append(pavbhaji_A_rava)
total_rava_A.append(misal_A_rava)
total_rava_A.append(idli_A_rava)
total_rava_A.append(kichdi_A_rava)
total_rava_A=sum(total_rava_A)
total_sabu_A=[]
total_sabu_A.append(pizza_A_sabudana)
total_sabu_A.append(burger_A_sabudana)
total_sabu_A.append(nonveg_A_sabudana)
total_sabu_A.append(veg_A_sabudana)
total_sabu_A.append(dosa_A_sabudana)
total_sabu_A.append(sandwich_A_sabudana)
total_sabu_A.append(pavbhaji_A_sabudana)
total_sabu_A.append(misal_A_sabudana)
total_sabu_A.append(idli_A_sabudana)
total_sabu_A.append(kichdi_A_sabudana)
total_sabu_A=sum(total_sabu_A)
total_masala_A=[]
total_masala_A.append(pizza_A_masala)
total_masala_A.append(burger_A_masala)
total_masala_A.append(nonveg_A_masala)
total_masala_A.append(veg_A_masala)
total_masala_A.append(dosa_A_masala)
total_masala_A.append(sandwich_A_masala)
total_masala_A.append(pavbhaji_A_masala)
total_masala_A.append(misal_A_masala)
total_masala_A.append(idli_A_masala)
total_masala_A.append(kichdi_A_masala)
total_masala_A=sum(total_masala_A)
total_veggies_A=[]
total_veggies_A.append(pizza_A_vegetables)
total_veggies_A.append(burger_A_vegetables)
total_veggies_A.append(nonveg_A_vegetables)
total_veggies_A.append(veg_A_vegetables)
total_veggies_A.append(dosa_A_vegetables)
total_veggies_A.append(sandwich_A_vegetables)
total_veggies_A.append(pavbhaji_A_vegetables)
total_veggies_A.append(misal_A_vegetables)
total_veggies_A.append(idli_A_vegetables)
total_veggies_A.append(kichdi_A_vegetables)
total_veggies_A=sum(total_veggies_A)
total_dal_A=[]
total_dal_A.append(pizza_A_dal)
total_dal_A.append(burger_A_dal)
total_dal_A.append(nonveg_A_dal)
total_dal_A.append(veg_A_dal)
total_dal_A.append(dosa_A_dal)
total_dal_A.append(sandwich_A_dal)
total_dal_A.append(pavbhaji_A_dal)
total_dal_A.append(misal_A_dal)
total_dal_A.append(idli_A_dal)
total_dal_A.append(kichdi_A_dal)
total_dal_A=sum(total_dal_A)
total_flour_A=[]
total_flour_A.append(pizza_A_flour)
total_flour_A.append(burger_A_flour)
total_flour_A.append(nonveg_A_flour)
total_flour_A.append(veg_A_flour)
total_flour_A.append(dosa_A_flour)
total_flour_A.append(sandwich_A_flour)
total_flour_A.append(pavbhaji_A_flour)
total_flour_A.append(misal_A_flour)
total_flour_A.append(idli_A_flour)
total_flour_A.append(kichdi_A_flour)
total_flour_A=sum(total_flour_A)
total_rice_A=[]
total_rice_A.append(pizza_A_rice)
total_rice_A.append(burger_A_rice)
total_rice_A.append(nonveg_A_rice)
total_rice_A.append(veg_A_rice)
total_rice_A.append(dosa_A_rice)
total_rice_A.append(sandwich_A_rice)
total_rice_A.append(pavbhaji_A_rice)
total_rice_A.append(misal_A_rice)
total_rice_A.append(idli_A_rice)
total_rice_A.append(kichdi_A_rice)
total_rice_A=sum(total_rice_A)
total_papad_A=[]
total_papad_A.append(pizza_A_papad)
total_papad_A.append(burger_A_papad)
total_papad_A.append(nonveg_A_papad)
total_papad_A.append(veg_A_papad)
total_papad_A.append(dosa_A_papad)
total_papad_A.append(sandwich_A_papad)
total_papad_A.append(pavbhaji_A_papad)
total_papad_A.append(misal_A_papad)
total_papad_A.append(idli_A_papad)
total_papad_A.append(kichdi_A_papad)
total_papad_A=sum(total_papad_A)
total_butter_A=[]
total_butter_A.append(pizza_A_butter)
total_butter_A.append(burger_A_butter)
total_butter_A.append(nonveg_A_butter)
total_butter_A.append(veg_A_butter)
total_butter_A.append(dosa_A_butter)
total_butter_A.append(sandwich_A_butter)
total_butter_A.append(pavbhaji_A_butter)
total_butter_A.append(misal_A_butter)
total_butter_A.append(idli_A_butter)
total_butter_A.append(kichdi_A_butter)
total_butter_A=sum(total_butter_A)
#Analysis for restaurant B
#pizza analysis for group B
group_data_pizza_B=pizza_data.groupby("Restaurant")
pizza_for_rest_B=group_data_pizza_B.get_group('B')
weekday_input_pizza_B=pizza_for_rest_B[['Weekday']]
weekday_input_pizza_B=weekday_input_pizza_B.apply(labelencoder_X.fit_transform)
pizza_out_B=pizza_for_rest_B[['Pizza']]
X_train_pizza_B,X_test_pizza_B,Y_train_pizza_B,Y_test_pizza_B = train_test_split(weekday_input_pizza_B,pizza_out_B,test_size=0.2,random_state=0)
regressor.fit(X_train_pizza_B, Y_train_pizza_B)
pizza_sum_B=0
for i in range(0,7):
pizza_pred_B=regressor.predict([[i]])
pizza_sum_B=pizza_sum_B+pizza_pred_B
pizza_sum_B=int(pizza_sum_B)
default_quantity_data_B_pizza=default_quantity_data_B.iloc[0,].values
pizza_B_list=default_quantity_data_B_pizza.tolist()
pizza_B_list=pizza_B_list[1:]
pizza_B_tomato=pizza_B_list[0]*pizza_sum_B
pizza_B_onion=pizza_B_list[1]*pizza_sum_B
pizza_B_capsicum=pizza_B_list[2]*pizza_sum_B
pizza_B_bread=pizza_B_list[3]*pizza_sum_B
pizza_B_dough=pizza_B_list[4]*pizza_sum_B
pizza_B_chicken=pizza_B_list[5]*pizza_sum_B
pizza_B_cheese=pizza_B_list[6]*pizza_sum_B
pizza_B_corn=pizza_B_list[7]*pizza_sum_B
pizza_B_rava=pizza_B_list[8]*pizza_sum_B
pizza_B_sabudana=pizza_B_list[9]*pizza_sum_B
pizza_B_masala=pizza_B_list[10]*pizza_sum_B
pizza_B_vegetables=pizza_B_list[11]*pizza_sum_B
pizza_B_dal=pizza_B_list[12]*pizza_sum_B
pizza_B_flour=pizza_B_list[13]*pizza_sum_B
pizza_B_rice=pizza_B_list[14]*pizza_sum_B
pizza_B_papad=pizza_B_list[15]*pizza_sum_B
pizza_B_butter=pizza_B_list[16]*pizza_sum_B
#Burger Analysis for restaurant B
group_data_burger_B=burger_data.groupby("Restaurant")
burger_for_rest_B=group_data_burger_B.get_group('B')
weekday_input_burger_B=burger_for_rest_B[['Weekday']]
weekday_input_burger_B=weekday_input_burger_B.apply(labelencoder_X.fit_transform)
burger_out_B=burger_for_rest_B[['Burger']]
X_train_burger_B,X_test_burger_B,Y_train_burger_B,Y_test_burger_B = train_test_split(weekday_input_burger_B,burger_out_B,test_size=0.2,random_state=0)
regressor.fit(X_train_burger_B, Y_train_burger_B)
burger_sum_B=0
for i in range(0,7):
burger_predict_sum=regressor.predict([[i]])
burger_sum_B=burger_sum_B+burger_predict_sum
burger_sum_B=int(burger_sum_B)
default_quantity_data_B_burger=default_quantity_data_B.iloc[1,].values
burger_B_list=default_quantity_data_B_burger.tolist()
burger_B_list=burger_B_list[1:]
burger_B_tomato=burger_B_list[0]*burger_sum_B
burger_B_onion=burger_B_list[1]*burger_sum_B
burger_B_capsicum=burger_B_list[2]*burger_sum_B
burger_B_bread=burger_B_list[3]*burger_sum_B
burger_B_dough=burger_B_list[4]*burger_sum_B
burger_B_chicken=burger_B_list[5]*burger_sum_B
burger_B_cheese=burger_B_list[6]*burger_sum_B
burger_B_corn=burger_B_list[7]*burger_sum_B
burger_B_rava=burger_B_list[8]*burger_sum_B
burger_B_sabudana=burger_B_list[9]*burger_sum_B
burger_B_masala=burger_B_list[10]*burger_sum_B
burger_B_vegetables=burger_B_list[11]*burger_sum_B
burger_B_dal=burger_B_list[12]*burger_sum_B
burger_B_flour=burger_B_list[13]*burger_sum_B
burger_B_rice=burger_B_list[14]*burger_sum_B
burger_B_papad=burger_B_list[15]*burger_sum_B
burger_B_butter=burger_B_list[16]*burger_sum_B
#Non veg analysis for restaurant B
group_data_nonveg_B=non_veg_thali_data.groupby("Restaurant")
nonveg_for_rest_B=group_data_nonveg_B.get_group('B')
weekday_input_nonveg_B=nonveg_for_rest_B[['Weekday']]
weekday_input_nonveg_B=weekday_input_nonveg_B.apply(labelencoder_X.fit_transform)
nonveg_out_B=nonveg_for_rest_B[['Non Veg Thali']]
X_train_nonveg_B,X_test_nonveg_B,Y_train_nonveg_B,Y_test_nonveg_B = train_test_split(weekday_input_nonveg_B,nonveg_out_B,test_size=0.2,random_state=0)
regressor.fit(X_train_nonveg_B, Y_train_nonveg_B)
nonveg_sum_B=0
for i in range(0,7):
nonveg_predict_sum=regressor.predict([[i]])
nonveg_sum_B=nonveg_sum_B+nonveg_predict_sum
nonveg_sum_B=int(nonveg_sum_B)
default_quantity_data_B_nonveg=default_quantity_data_B.iloc[2,].values
nonveg_B_list=default_quantity_data_B_nonveg.tolist()
nonveg_B_list=nonveg_B_list[1:]
nonveg_B_tomato=nonveg_B_list[0]*nonveg_sum_B
nonveg_B_onion=nonveg_B_list[1]*nonveg_sum_B
nonveg_B_capsicum=nonveg_B_list[2]*nonveg_sum_B
nonveg_B_bread=nonveg_B_list[3]*nonveg_sum_B
nonveg_B_dough=nonveg_B_list[4]*nonveg_sum_B
nonveg_B_chicken=nonveg_B_list[5]*nonveg_sum_B
nonveg_B_cheese=nonveg_B_list[6]*nonveg_sum_B
nonveg_B_corn=nonveg_B_list[7]*nonveg_sum_B
nonveg_B_rava=nonveg_B_list[8]*nonveg_sum_B
nonveg_B_sabudana=nonveg_B_list[9]*nonveg_sum_B
nonveg_B_masala=nonveg_B_list[10]*nonveg_sum_B
nonveg_B_vegetables=nonveg_B_list[11]*nonveg_sum_B
nonveg_B_dal=nonveg_B_list[12]*nonveg_sum_B
nonveg_B_flour=nonveg_B_list[13]*nonveg_sum_B
nonveg_B_rice=nonveg_B_list[14]*nonveg_sum_B
nonveg_B_papad=nonveg_B_list[15]*nonveg_sum_B
nonveg_B_butter=nonveg_B_list[16]*nonveg_sum_B
#Veg analysis for restaurant B
group_data_veg_B=veg_thali_data.groupby("Restaurant")
veg_for_rest_B=group_data_veg_B.get_group('B')
weekday_input_veg_B=veg_for_rest_B[['Weekday']]
weekday_input_veg_B=weekday_input_veg_B.apply(labelencoder_X.fit_transform)
veg_out_B=veg_for_rest_B[['Veg Thali']]
X_train_veg_B,X_test_veg_B,Y_train_veg_B,Y_test_veg_B = train_test_split(weekday_input_veg_B,veg_out_B,test_size=0.2,random_state=0)
regressor.fit(X_train_veg_B, Y_train_veg_B)
veg_sum_B=0
for i in range(0,7):
veg_predict_sum=regressor.predict([[i]])
veg_sum_B=veg_sum_B+veg_predict_sum
veg_sum_B=int(veg_sum_B)-1
default_quantity_data_B_veg=default_quantity_data_B.iloc[3,].values
veg_B_list=default_quantity_data_B_veg.tolist()
veg_B_list=veg_B_list[1:]
veg_B_tomato=veg_B_list[0]*veg_sum_B
veg_B_onion=veg_B_list[1]*veg_sum_B
veg_B_capsicum=veg_B_list[2]*veg_sum_B
veg_B_bread=veg_B_list[3]*veg_sum_B
veg_B_dough=veg_B_list[4]*veg_sum_B
veg_B_chicken=veg_B_list[5]*veg_sum_B
veg_B_cheese=veg_B_list[6]*veg_sum_B
veg_B_corn=veg_B_list[7]*veg_sum_B
veg_B_rava=veg_B_list[8]*veg_sum_B
veg_B_sabudana=veg_B_list[9]*veg_sum_B
veg_B_masala=veg_B_list[10]*veg_sum_B
veg_B_vegetables=veg_B_list[11]*veg_sum_B
veg_B_dal=veg_B_list[12]*veg_sum_B
veg_B_flour=veg_B_list[13]*veg_sum_B
veg_B_rice=veg_B_list[14]*veg_sum_B
veg_B_papad=veg_B_list[15]*veg_sum_B
veg_B_butter=veg_B_list[16]*veg_sum_B
#DOSA analysis for restaurant B
group_data_dosa_B=dosa_data.groupby("Restaurant")
dosa_for_rest_B=group_data_dosa_B.get_group('B')
weekday_input_dosa_B=dosa_for_rest_B[['Weekday']]
weekday_input_dosa_B=weekday_input_dosa_B.apply(labelencoder_X.fit_transform)
dosa_out_B=dosa_for_rest_B[['Dosa']]
X_train_dosa_B,X_test_dosa_B,Y_train_dosa_B,Y_test_dosa_B = train_test_split(weekday_input_dosa_B,dosa_out_B,test_size=0.2,random_state=0)
regressor.fit(X_train_dosa_B, Y_train_dosa_B)
dosa_sum_B=0
for i in range(0,7):
dosa_predict_sum=regressor.predict([[i]])
dosa_sum_B=dosa_sum_B+dosa_predict_sum
dosa_sum_B=int(dosa_sum_B)
default_quantity_data_B_dosa=default_quantity_data_B.iloc[4,].values
dosa_B_list=default_quantity_data_B_dosa.tolist()
dosa_B_list=dosa_B_list[1:]
dosa_B_tomato=dosa_B_list[0]*dosa_sum_B
dosa_B_onion=dosa_B_list[1]*dosa_sum_B
dosa_B_capsicum=dosa_B_list[2]*dosa_sum_B
dosa_B_bread=dosa_B_list[3]*dosa_sum_B
dosa_B_dough=dosa_B_list[4]*dosa_sum_B
dosa_B_chicken=dosa_B_list[5]*dosa_sum_B
dosa_B_cheese=dosa_B_list[6]*dosa_sum_B
dosa_B_corn=dosa_B_list[7]*dosa_sum_B
dosa_B_rava=dosa_B_list[8]*dosa_sum_B
dosa_B_sabudana=dosa_B_list[9]*dosa_sum_B
dosa_B_masala=dosa_B_list[10]*dosa_sum_B
dosa_B_vegetables=dosa_B_list[11]*dosa_sum_B
dosa_B_dal=dosa_B_list[12]*dosa_sum_B
dosa_B_flour=dosa_B_list[13]*dosa_sum_B
dosa_B_rice=dosa_B_list[14]*dosa_sum_B
dosa_B_papad=dosa_B_list[15]*dosa_sum_B
dosa_B_butter=dosa_B_list[16]*dosa_sum_B
#Sandwich ananlysis for restaurant B
group_data_sandwich_B=sandwich_data.groupby("Restaurant")
sandwich_for_rest_B=group_data_sandwich_B.get_group('B')
weekday_input_sandwich_B=sandwich_for_rest_B[['Weekday']]
weekday_input_sandwich_B=weekday_input_sandwich_B.apply(labelencoder_X.fit_transform)
sandwich_out_B=sandwich_for_rest_B[['Sandwich']]
X_train_sandwich_B,X_test_sandwich_B,Y_train_sandwich_B,Y_test_sandwich_B = train_test_split(weekday_input_sandwich_B,sandwich_out_B,test_size=0.2,random_state=0)
regressor.fit(X_train_sandwich_B, Y_train_sandwich_B)
sandwich_sum_B=0
for i in range(0,7):
sandwich_predict_sum=regressor.predict([[i]])
sandwich_sum_B=sandwich_sum_B+sandwich_predict_sum
sandwich_sum_B=int(sandwich_sum_B)
default_quantity_data_B_sandwich=default_quantity_data_B.iloc[5,].values
sandwich_B_list=default_quantity_data_B_sandwich.tolist()
sandwich_B_list=sandwich_B_list[1:]
sandwich_B_tomato=sandwich_B_list[0]*sandwich_sum_B
sandwich_B_onion=sandwich_B_list[1]*sandwich_sum_B
sandwich_B_capsicum=sandwich_B_list[2]*sandwich_sum_B
sandwich_B_bread=sandwich_B_list[3]*sandwich_sum_B
sandwich_B_dough=sandwich_B_list[4]*sandwich_sum_B
sandwich_B_chicken=sandwich_B_list[5]*sandwich_sum_B
sandwich_B_cheese=sandwich_B_list[6]*sandwich_sum_B
sandwich_B_corn=sandwich_B_list[7]*sandwich_sum_B
sandwich_B_rava=sandwich_B_list[8]*sandwich_sum_B
sandwich_B_sabudana=sandwich_B_list[9]*sandwich_sum_B
sandwich_B_masala=sandwich_B_list[10]*sandwich_sum_B
sandwich_B_vegetables=sandwich_B_list[11]*sandwich_sum_B
sandwich_B_dal=sandwich_B_list[12]*sandwich_sum_B
sandwich_B_flour=sandwich_B_list[13]*sandwich_sum_B
sandwich_B_rice=sandwich_B_list[14]*sandwich_sum_B
sandwich_B_papad=sandwich_B_list[15]*sandwich_sum_B
sandwich_B_butter=sandwich_B_list[16]*sandwich_sum_B
#PAVBHAJI analysis for restauarnt B
group_data_pavbhaji_B=pav_bhaji_data.groupby("Restaurant")
pavbhaji_for_rest_B=group_data_pavbhaji_B.get_group('B')
weekday_input_pavbhaji_B=pavbhaji_for_rest_B[['Weekday']]
weekday_input_pavbhaji_B=weekday_input_pavbhaji_B.apply(labelencoder_X.fit_transform)
pavbhaji_out_B=pavbhaji_for_rest_B[['Pav Bhaji']]
X_train_pavbhaji_B,X_test_pavbhaji_B,Y_train_pavbhaji_B,Y_test_pavbhaji_B = train_test_split(weekday_input_pavbhaji_B,pavbhaji_out_B,test_size=0.2,random_state=0)
regressor.fit(X_train_pavbhaji_B, Y_train_pavbhaji_B)
pavbhaji_sum_B=0
for i in range(0,7):
pavbhaji_predict_sum=regressor.predict([[i]])
pavbhaji_sum_B=pavbhaji_sum_B+pavbhaji_predict_sum
pavbhaji_sum_B=int(pavbhaji_sum_B)
default_quantity_data_B_pavbhaji=default_quantity_data_B.iloc[6,].values
pavbhaji_B_list=default_quantity_data_B_pavbhaji.tolist()
pavbhaji_B_list=pavbhaji_B_list[1:]
pavbhaji_B_tomato=pavbhaji_B_list[0]*pavbhaji_sum_B
pavbhaji_B_onion=pavbhaji_B_list[1]*pavbhaji_sum_B
pavbhaji_B_capsicum=pavbhaji_B_list[2]*pavbhaji_sum_B
pavbhaji_B_bread=pavbhaji_B_list[3]*pavbhaji_sum_B
pavbhaji_B_dough=pavbhaji_B_list[4]*pavbhaji_sum_B
pavbhaji_B_chicken=pavbhaji_B_list[5]*pavbhaji_sum_B
pavbhaji_B_cheese=pavbhaji_B_list[6]*pavbhaji_sum_B
pavbhaji_B_corn=pavbhaji_B_list[7]*pavbhaji_sum_B
pavbhaji_B_rava=pavbhaji_B_list[8]*pavbhaji_sum_B
pavbhaji_B_sabudana=pavbhaji_B_list[9]*pavbhaji_sum_B
pavbhaji_B_masala=pavbhaji_B_list[10]*pavbhaji_sum_B
pavbhaji_B_vegetables=pavbhaji_B_list[11]*pavbhaji_sum_B
pavbhaji_B_dal=pavbhaji_B_list[12]*pavbhaji_sum_B
pavbhaji_B_flour=pavbhaji_B_list[13]*pavbhaji_sum_B
pavbhaji_B_rice=pavbhaji_B_list[14]*pavbhaji_sum_B
pavbhaji_B_papad=pavbhaji_B_list[15]*pavbhaji_sum_B
pavbhaji_B_butter=pavbhaji_B_list[16]*pavbhaji_sum_B
#Misal analysis for restaurant B
group_data_misal_B=misal_data.groupby("Restaurant")
misal_for_rest_B=group_data_misal_B.get_group('B')
weekday_input_misal_B=misal_for_rest_B[['Weekday']]
weekday_input_misal_B=weekday_input_misal_B.apply(labelencoder_X.fit_transform)
misal_out_B=misal_for_rest_B[['Misal']]
X_train_misal_B,X_test_misal_B,Y_train_misal_B,Y_test_misal_B = train_test_split(weekday_input_misal_B,misal_out_B,test_size=0.2,random_state=0)
regressor.fit(X_train_misal_B, Y_train_misal_B)
misal_sum_B=0
for i in range(0,7):
misal_predict_sum=regressor.predict([[i]])
misal_sum_B=misal_sum_B+misal_predict_sum
misal_sum_B=int(misal_sum_B)+1
default_quantity_data_B_misal=default_quantity_data_B.iloc[7,].values
misal_B_list=default_quantity_data_B_misal.tolist()
misal_B_list=misal_B_list[1:]
misal_B_tomato=misal_B_list[0]*misal_sum_B
misal_B_onion=misal_B_list[1]*misal_sum_B
misal_B_capsicum=misal_B_list[2]*misal_sum_B
misal_B_bread=misal_B_list[3]*misal_sum_B
misal_B_dough=misal_B_list[4]*misal_sum_B
misal_B_chicken=misal_B_list[5]*misal_sum_B
misal_B_cheese=misal_B_list[6]*misal_sum_B
misal_B_corn=misal_B_list[7]*misal_sum_B
misal_B_rava=misal_B_list[8]*misal_sum_B
misal_B_sabudana=misal_B_list[9]*misal_sum_B
misal_B_masala=misal_B_list[10]*misal_sum_B
misal_B_vegetables=misal_B_list[11]*misal_sum_B
misal_B_dal=misal_B_list[12]*misal_sum_B
misal_B_flour=misal_B_list[13]*misal_sum_B
misal_B_rice=misal_B_list[14]*misal_sum_B
misal_B_papad=misal_B_list[15]*misal_sum_B
misal_B_butter=misal_B_list[16]*misal_sum_B
#idli analysis for restauarant B
group_data_idli_B=idli_data.groupby("Restaurant")
idli_for_rest_B=group_data_idli_B.get_group('B')
weekday_input_idli_B=idli_for_rest_B[['Weekday']]
weekday_input_idli_B=weekday_input_idli_B.apply(labelencoder_X.fit_transform)
idli_out_B=idli_for_rest_B[['idli']]
X_train_idli_B,X_test_idli_B,Y_train_idli_B,Y_test_idli_B = train_test_split(weekday_input_idli_B,idli_out_B,test_size=0.2,random_state=0)
regressor.fit(X_train_idli_B, Y_train_idli_B)
idli_sum_B=0
for i in range(0,7):
idli_predict_sum=regressor.predict([[i]])
idli_sum_B=idli_sum_B+idli_predict_sum