In [1]:

    

# Import necessary packages
from sklearn.linear_model import LinearRegression
import pandas as pd
import matplotlib.pyplot as plt


    

In [2]:

    

# Assign the data frame 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 [3]:

    

bmi_life_data.head()


    

    
Out[3]:






  

    

      

      
Country
      
Life expectancy
      
BMI
    
  
  

    

      
0
      
Afghanistan
      
52.8
      
20.62058
    
    

      
1
      
Albania
      
76.8
      
26.44657
    
    

      
2
      
Algeria
      
75.5
      
24.59620
    
    

      
3
      
Andorra
      
84.6
      
27.63048
    
    

      
4
      
Angola
      
56.7
      
22.25083
    
  

In [4]:

    

# Plot data
plt.scatter(x_values, y_values)
plt.show()


    

In [5]:

    

# Make and fit the linear regression model
bmi_life_model = LinearRegression()
bmi_life_model.fit(x_values, y_values)


    

    
Out[5]:





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

In [6]:

    

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


    

    




[[ 60.31564716]]

In [7]:

    

# plot data and prediction
plt.scatter(x_values, y_values, c='r')
plt.plot(x_values, bmi_life_model.predict(x_values))
plt.xlabel('BMI')
plt.ylabel('Life Expectancy')
plt.show()