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', usecols=0, skiprows=1, delimiter=',') print("Age:") print(f"{age}") bmi = np.loadtxt('insurance.csv', usecols=2, skiprows=1, delimiter=',') print("BMI:") print(f"{bmi}") charges = np.loadtxt('insurance.csv', usecols=6, skiprows=1, delimiter=',') print("Insurance Charges:") print(f"{charges}")

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

# plot charges vs age and add labels to axis and title plt.plot(age, charges, 'bo') plt.ylabel('Insurance Charges') plt.xlabel('Age') plt.title("Age vs Insurance Charges") plt.show

# plot charges vs BMI; add axis labels and plot title plt.plot(bmi, charges, 'bo') plt.xlabel("BMI") plt.ylabel("Insurance Charges") plt.title("BMI vs Insurance Charges") plt.show

# 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', usecols=2, skiprows=1, delimiter=',') print("BMI:") print(f"{bmi}") charges = np.loadtxt('insurance.csv', usecols=6, skiprows=1, delimiter=',') print("Insurance Charges:") print(f"{charges}")

# Fit a line using linear regression line_coeff = np.polyfit(bmi, charges, 1) print (f"equation of the line is {line_coeff[0]}x + {line_coeff[1]}") f = np.poly1d(line_coeff) plt.plot(bmi, charges, 'bo') plt.xlabel("BMI") plt.ylabel("Insurance Charges") plt.title("BMI vs Insurance Charges") bmi_min = min(bmi) bmi_max = max(bmi) x = np.linspace(bmi_min, bmi_max, 100) plt.plot(x, f(x), 'r') plt.text(15,55000,"line: y = 393.87x + 1192.94") plt.show

# Plot data and line on same graph line_coeff = np.polyfit(bmi, charges, 1) print (f"equation of the line is {line_coeff[0]}x + {line_coeff[1]}") f = np.poly1d(line_coeff) plt.plot(bmi, charges, 'bo') plt.xlabel("BMI") plt.ylabel("Insurance Charges") plt.title("BMI vs Insurance Charges") bmi_min = min(bmi) bmi_max = max(bmi) x = np.linspace(bmi_min, bmi_max, 100) plt.plot(x, f(x), 'r') plt.text(15,55000,"line: y = 393.87x + 1192.94") plt.show

# Fit a parabola to data coeffs_quad = np.polyfit(bmi, charges, 2) print (f"Quadratic polynomial is {coeffs_quad[2]}*x^2 +{ coeffs_quad[1]}*x +\ { coeffs_quad[0]}") g = np.poly1d( coeffs_quad) x_eval = np.linspace(0.5,65); y_eval=g(x_eval) plt.plot(bmi, charges, 'ro') plt.plot(x_eval,y_eval) plt.xlabel("BMI") plt.ylabel("Insurance Charges") plt.title("BMI vs Insurance Charges") plt.text(0,55000,"parabola: y = -5177.03x^2 + 813.97x - 6.66")

# Plot data and parabola on same graph coeffs_quad = np.polyfit(bmi, charges, 2) print (f"Quadratic polynomial is {coeffs_quad[2]}*x^2 +{ coeffs_quad[1]}*x +\ { coeffs_quad[0]}") g = np.poly1d( coeffs_quad) x_eval = np.linspace(0.5,65); y_eval=g(x_eval) plt.plot(bmi, charges, 'ro') plt.plot(x_eval,y_eval) plt.xlabel("BMI") plt.ylabel("Insurance Charges") plt.title("BMI vs Insurance Charges") plt.text(0,55000,"parabola: y = -5177.03x^2 + 813.97x - 6.66")