In [40]:

    

%matplotlib inline

import pandas as pd
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt


    

In [41]:

    

# Assign the dataframe to this variable.
bmi_life_data = pd.read_csv("bmi_and_life_expectancy.csv")
x_values = bmi_life_data[['BMI']]
y_values = bmi_life_data[['Life expectancy']]


    

In [42]:

    

#print(bmi_life_data)


    

In [43]:

    

# Make and fit the linear regression model
#TODO: Fit the model and Assign it to bmi_life_model
bmi_life_model = LinearRegression()
bmi_life_model.fit(x_values,y_values)


    

    
Out[43]:





LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

In [44]:

    

# Mak a prediction using the model
# TODO: Predict life expectancy for a BMI value of 21.07931
laos_life_exp = bmi_life_model.predict(21.07931)


    

In [45]:

    

print(laos_life_exp)


    

    




[[ 60.31564716]]

In [47]:

    

#visualize results

plt.scatter(x_values, y_values)
plt.plot(x_values, bmi_life_model.predict(x_values))
plt.show()