# 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 # usecols = index of what you want to grab, delimiter = a string used to separate data types, skiprows = number of lines at first of data file to skip filename = 'insurance.csv' age = np.loadtxt(filename, usecols = 0, delimiter = ",", skiprows = 1) print (f"Ages are {age}") bmi = np.loadtxt(filename, usecols = 2, delimiter = ",", skiprows = 1) print (f"BMIs are {bmi}") charges = np.loadtxt(filename, usecols = 6, delimiter = ",", skiprows = 1) print (f"Charges are {charges}")

# print out the number of data instances print (f"The number of data instances in Age are {len(age)}") print (f"The number of data instances in BMI are {len(bmi)}") print (f"The number of data instances in Charges are {len(charges)}")

# Scatter plot charges (y-axis) vs age (x-axis) and add labels to axis and title plt.scatter(age, charges) plt.xlabel("Age (in years)") plt.ylabel("Charges (in dollars)") plt.title("Age vs Insurance Charges")

# Scatter plot charges (y-axis) vs BMI (x-axis); add axis labels and plot title plt.scatter(bmi, charges) plt.xlabel("BMI") plt.ylabel("Charges (in dollars)") plt.title("BMI vs Insurance Charges")

# 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(filename, usecols = 2, delimiter = ",", skiprows = 1) print (f"The BMIs are {bmi}") charges = np.loadtxt(filename, usecols = 6, delimiter = ",", skiprows = 1) print (f"Insurance charges are {charges}")

# Fit a line using linear regression coeff_line = np.polyfit(bmi, charges, 1) f = np.poly1d(coeff_line) print (f"The line fit for linear regression is {f}")

# Plot data and line on same graph plt.scatter(bmi,charges) bmi_min = np.min(bmi) bmi_max = np.max(bmi) x_line = np.linspace(bmi_min, bmi_max, 2) y_line = f(x_line) plt.plot(x_line, y_line, "r") plt.xlabel("BMI") plt.ylabel("Charges (in dollars)") plt.title("BMI vs. Insurance Charges")

# Fit a parabola to data coeff_quad = np.polyfit(bmi, charges, 2) p = np.poly1d(coeff_quad) print(f"The parabola fit to data is:") print(p)

# Plot data and parabola on same graph plt.scatter(bmi, charges) bmi_min = np.min(bmi) bmi_max = np.max(bmi) x_par = np.linspace(bmi_min, bmi_max, 3) y_par = p(x_par) plt.plot(x_par, y_par, "r") plt.xlabel("BMI") plt.ylabel("Charges (in dollars") plt.title("BMI vs Insurance Charges")