This exercise will test your ability to read a data file and understand statistics about the data.
In later exercises, you will apply techniques to filter the data, build a machine learning model, and iteratively improve your model.
The course examples use data from Melbourne. To ensure you can apply these techniques on your own, you will have to apply them to a new dataset (with house prices from Iowa).
The exercises use a "notebook" coding environment. In case you are unfamiliar with notebooks, we have a 90-second intro video.
Run the following cell to set up code-checking, which will verify your work as you go.
# Set up code checking
from learntools.core import binder
binder.bind(globals())
from learntools.machine_learning.ex2 import *
print("Setup Complete")
Setup Complete
Read the Iowa data file into a Pandas DataFrame called home_data.
import pandas as pd
# Path of the file to read
iowa_file_path = '../input/home-data-for-ml-course/train.csv'
# Fill in the line below to read the file into a variable home_data
home_data = pd.read_csv(iowa_file_path)
# Call line below with no argument to check that you've loaded the data correctly
step_1.check()
Correct
# Lines below will give you a hint or solution code
#step_1.hint()
#step_1.solution()
Use the command you learned to view summary statistics of the data. Then fill in variables to answer the following questions
# Print summary statistics in next line
home_data.describe()
| Id | MSSubClass | LotFrontage | LotArea | OverallQual | OverallCond | YearBuilt | YearRemodAdd | MasVnrArea | BsmtFinSF1 | ... | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | MiscVal | MoSold | YrSold | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1460.000000 | 1460.000000 | 1201.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1452.000000 | 1460.000000 | ... | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 |
| mean | 730.500000 | 56.897260 | 70.049958 | 10516.828082 | 6.099315 | 5.575342 | 1971.267808 | 1984.865753 | 103.685262 | 443.639726 | ... | 94.244521 | 46.660274 | 21.954110 | 3.409589 | 15.060959 | 2.758904 | 43.489041 | 6.321918 | 2007.815753 | 180921.195890 |
| std | 421.610009 | 42.300571 | 24.284752 | 9981.264932 | 1.382997 | 1.112799 | 30.202904 | 20.645407 | 181.066207 | 456.098091 | ... | 125.338794 | 66.256028 | 61.119149 | 29.317331 | 55.757415 | 40.177307 | 496.123024 | 2.703626 | 1.328095 | 79442.502883 |
| min | 1.000000 | 20.000000 | 21.000000 | 1300.000000 | 1.000000 | 1.000000 | 1872.000000 | 1950.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 2006.000000 | 34900.000000 |
| 25% | 365.750000 | 20.000000 | 59.000000 | 7553.500000 | 5.000000 | 5.000000 | 1954.000000 | 1967.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 5.000000 | 2007.000000 | 129975.000000 |
| 50% | 730.500000 | 50.000000 | 69.000000 | 9478.500000 | 6.000000 | 5.000000 | 1973.000000 | 1994.000000 | 0.000000 | 383.500000 | ... | 0.000000 | 25.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 6.000000 | 2008.000000 | 163000.000000 |
| 75% | 1095.250000 | 70.000000 | 80.000000 | 11601.500000 | 7.000000 | 6.000000 | 2000.000000 | 2004.000000 | 166.000000 | 712.250000 | ... | 168.000000 | 68.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 8.000000 | 2009.000000 | 214000.000000 |
| max | 1460.000000 | 190.000000 | 313.000000 | 215245.000000 | 10.000000 | 9.000000 | 2010.000000 | 2010.000000 | 1600.000000 | 5644.000000 | ... | 857.000000 | 547.000000 | 552.000000 | 508.000000 | 480.000000 | 738.000000 | 15500.000000 | 12.000000 | 2010.000000 | 755000.000000 |
8 rows × 38 columns
# What is the average lot size (rounded to nearest integer)?
avg_lot_size = 10517
# As of today, how old is the newest home (current year - the date in which it was built)
newest_home_age = 2021 - 2010
# Checks your answers
step_2.check()
Correct
step_2.hint()
step_2.solution()
Hint: Run the describe command. Lot size is in the column called LotArea. Also look at YearBuilt. Remember to round lot size
Solution:
# using data read from home_data.describe()
avg_lot_size = 10517
newest_home_age = 11
The newest house in your data isn't that new. A few potential explanations for this:
If the reason is explanation #1 above, does that affect your trust in the model you build with this data? What about if it is reason #2?
How could you dig into the data to see which explanation is more plausible?
Check out this discussion thread to see what others think or to add your ideas.
You are ready for Your First Machine Learning Model.