#Predicting Health Costs. Linear Regression Model

#importing required modules

import pandas as pd

import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

from sklearn import preprocessing

from sklearn.metrics import explained_variance_score

from sklearn.metrics import max_error

from sklearn.metrics import mean_absolute_error

from sklearn.metrics import mean_squared_error

#set path to the csv file

path_dataset = "insurance.csv"

dataset = pd.read_csv(path_dataset)

#converting categorical data to numeric data

df = dataset

label_encoder = preprocessing.LabelEncoder()

df["sex"]= label_encoder.fit_transform(df["sex"])

df["smoker"]= label_encoder.fit_transform(df["smoker"])

label_encoder.fit(df['region'])

region_list = label_encoder.transform(df["region"])

region_mapping = dict(zip(label_encoder.classes_, region_list))

df["region"]= region_list

feature_cols = ["age","sex","bmi","children","smoker","region"]

target = "charges"

#separating feature attributes and target attribute

y = df[target].values

X = df[feature_cols].values

#80/20 hold out approach

#splitting data into training and testing

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

#training and fitting the model

reg = LinearRegression()

reg.fit(X_train, y_train)

X_input = [[12,1,28,0,1,2]]

y_pred = reg.predict(X_test)

#plotting actual vs predicted. First n instances

df1 = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})

n = 25

df1=df1.head(n)

df1.plot(kind='bar',figsize=(10,8))

plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')

plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')

plt.title('Actual vs Predicted Values')

plt.xlabel('Instance')

plt.ylabel('Charges')

plt.show()

#Printing stats

print("Train R^2 Score: ",reg.score(X_train, y_train))

print("Test R^2 Score: ",reg.score(X_test, y_test))

print("\nExplained Variance Score: ",explained_variance_score(y_test, y_pred))

print("Max Error: ",max_error(y_test, y_pred))

print("Mean Absolute Error: ",mean_absolute_error(y_test, y_pred))

print("Mean Squared Error: ",mean_squared_error(y_test, y_pred))

print("\nIntercept: ",reg.intercept_)

mylist = list(zip(feature_cols, reg.coef_))

print("\nCoefficients:")

for elem in mylist:

print(elem)

age = input("Enter age: ")

bmi = input("Enter bmi[i.e 28.7]: ")

gender = input("Enter gender[female=0, male=1]: ")

children = input("Enter children: ")

smoker = input("Enter smoker[yes=1, no=0]: ")

print(region_mapping)

region = input("Enter region number accordingly: ")

#type casting to integer and float

try:

age = int(age)

bmi = float(bmi)

children = int(children)

region = int(region)

sex = int(gender)

smoker = int(smoker)

except ValueError:

print("\nError: Enter digits only")

#error handling

if(children < 0 ) :

print("Error: Children must be 0 or more")

exit()

elif sex < 0 or sex > 1:

print("Error: Gender must be 0 or 1")

exit()

elif smoker < 0 or smoker > 1:

print("Error: smoker must be 0 or 1")

exit()

elif region < 1 or region > 3:

print("Error: region must be in range")

exit()

elif age < 1 :

print("Error: Age cannot be negative or zero")

exit()

X_input = [[age,sex,bmi,children,smoker,region]]

y_pred_in=reg.predict(X_input)

print("\nPredicted charges: "+str(y_pred_in[0]))