Explore Your Data

Machine Learning Course Home Page

---

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.

Exercises

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

Step 1: Loading Data

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()

<IPython.core.display.Javascript object>

Correct

# Lines below will give you a hint or solution code
#step_1.hint()
#step_1.solution()

Step 2: Review The Data

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 = 12

# Checks your answers
step_2.check()

<IPython.core.display.Javascript object>

Correct

# step_2.hint()
#step_2.solution()

Think About Your Data

The newest house in your data isn’t that new. A few potential explanations for this:

1. They haven’t built new houses where this data was collected.
2. The data was collected a long time ago. Houses built after the data publication wouldn’t show up.

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.

Keep Going

You are ready for Your First Machine Learning Model.

---

Machine Learning Course Home Page