4.3.4

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression

data = pd.read_csv("data.csv")
x = data["Age"].values
y = data["Blood Pressure"].values

#Use reshape to turn the x values into 2D arrays:
x = x.reshape(-1,1)

#Create the model
model = LinearRegression().fit(x, y)

#Find the coefficient, bias, and r squared values.
#Each should be a float and rounded to two decimal places.
coef = round(float(model.coef_), 2)
intercept = round(float(model.intercept_), 2)
r_squared = model.score(x, y)


#Print out the linear equation and r squared value
print("Model's Linear Equation: y=",coef, "x+", intercept)
print("R Squared value:", r_squared)

#Predict the the blood pressure of someone who is 43 years old.
#Print out the prediction
x_predict = 43
prediction = model.predict([[x_predict]])
print("Prediction of someone 48 years old: ", prediction)