A company that manufactures riding mowers wants to identify the best sales prospects for an intensive sales campaign. In particular, the manufacturer is interested in classifying households as prospective owners or nonowners on the basis of Income (in $ 1000s) and Lot Size (in 1000 ft2). The marketing expert looked at a random sample of 24 households, given in the file RidingMowers.csv.

Income	Lot_Size	Ownership
60	18.4	Owner
85.5	16.8	Owner
64.8	21.6	Owner
61.5	20.8	Owner
87	23.6	Owner
110.1	19.2	Owner
108	17.6	Owner
82.8	22.4	Owner
69	20	Owner
93	20.8	Owner
51	22	Owner
81	20	Owner
75	19.6	Nonowner
52.8	20.8	Nonowner
64.8	17.2	Nonowner
43.2	20.4	Nonowner
84	17.6	Nonowner
49.2	17.6	Nonowner
59.4	16	Nonowner
66	18.4	Nonowner
47.4	16.4	Nonowner
33	18.8	Nonowner
51	14	Nonowner
63	14.8	Nonowner

Using Python, create a scatter plot of Lot Size vs. Income, color-coded by the outcome variable owneronowner. Make sure to obtain a well-formatted plot (create legible labels and a legend, etc.).

# 1. load the data using Pandas read_csv (1 line of code) # MISSING: one line of code needed.

# 2. Print the number of rows and columns (1 line of code) # MISSING: one line of code needed.

output:

(24, 3)

Using pandas, write the code in Python to get the following output

# scatter plot of Lot Size vs. Income # Hint: Color the points by Ownership for this assignment.

Out[12]: : Income Lot_Size Ownership 0 60.0 18.4 Owner 1 85.5 16.8 Owner 2 64.8 21.6 Owner Owner 61.5 20.8 4 87.0 23.6 Owner Out[13]: 24 22 20 Lot_Size 18 16 14 30 40 - 50 60 80 90 100 110 70 Income