Week 9 Practice Notebooks - Duplicate

# Import NumPy and matplotlib.pyplot import numpy as np import matplotlib.pyplot as plt

# Read in the data for age, bmi and insurance charges in separate arrays. # Print out to check # filename='insurance.csv' age = np.loadtxt ('insurance.csv', skiprows=1, usecols=0, delimiter=',') print ("Age Data") print (age) bmi = np.loadtxt ('insurance.csv', skiprows=1, usecols=2, delimiter=',') print ("BMI Data") print (bmi) charge = np.loadtxt ('insurance.csv', skiprows=1, usecols=6, delimiter=',') print ("Insurance Charge") print (charge)

# print out the number of data instances print(f"We have {len(age)} data entries")

# plot charges vs age and add labels to axis and title plt.scatter (age, charge) plt.xlabel ("AGE") plt.ylabel ("Insurance Charge") plt.title ("Age vs Insurance")

# plot charges vs BMI; add axis labels and plot title plt.scatter (bmi, charge) plt.xlabel ("BMI") plt.ylabel ("Insurance Charge") plt.title ("BMI vs Insurance")

# Import NumPy and matplotlib.pyplot import numpy as np import matplotlib.pyplot as plt

# Read in the data for bmi and insurance charges in separate arrays. # Print out to check # filename='insurance.csv' bmi = np.loadtxt ('insurance.csv', skiprows=1, usecols=2, delimiter=',') print ("BMI Data") print (bmi) charge = np.loadtxt ('insurance.csv', skiprows=1, usecols=6, delimiter=',') print ("Insurance Charge") print (charge)

# Fit a line using linear regression line_coeff = np.polyfit(bmi,charge,1) print (f"The equation of the line is {line_coeff[0]}x + {line_coeff[1]}" )

# Plot data and line on same graph f = np.poly1d(line_coeff) plt.plot(bmi,charge, 'bo') plt.xlabel("BMI") plt.ylabel("Insurance Charge") bmi_min = min(bmi) bmi_max = max(bmi) x=np.linspace(bmi_min, bmi_max,100) plt.plot(x, f(x),'r') plt.title("Linear regression fit to data") plt.show()

# Fit a parabola to data coeffs_quad = np.polyfit (bmi,charge,2) print (f"Quadratic polynomial is {coeffs_quad[2]}*x^2 +{ coeffs_quad[1]}*x +\ { coeffs_quad[0]}")

# Plot data and parabola on same graph g = np.poly1d(coeffs_quad) plt.plot(bmi,charge, 'bo') plt.xlabel("BMI") plt.ylabel("Insurance Charge") plt.title ("Quadratic Polynomial fit to data") x_eval = np.linspace(0,100); y_eval=g(x_eval) plt.plot(x_eval,y_eval) plt.show