# Machine Learning and Data Science: Linear Regression Part 2

## Linear Regression Part2: Getting and Evaluating Data¶

This is part 2 of the posts on Linear Regression. Part 1 gave an introduction, motivation and defined many of the terms that will be used. You might want to give that a read before looking at this.

You are free to use this post as you see fit. It was converted to html from a Jupyter notebook. The notebook is available at https://github.com/dbkinghorn/blog-jupyter-notebooks

The focus in this post is on getting our data and taking a first look at it. We will use some of the of the standard (nice!) tools available as Python modules to do this.

Since this is supposed to be an introduction I will be fairly verbose in descriptions of what is being done. Most of the dialog will be in “markdown” cells like what you are reading now. I will use Python comments in code cells for short descriptions.

## Get the data¶

### King County WA Housing sales data for 2014-15¶

I got the data I’m using from Kaggle. Kaggle is an interesting data analysis community site and worth a visit if you haven’t seen it. The data set is on the page “House Sales in King County, USA”. The data is in a .zip file and will expand to a .csv file (comma separated values). Grab that, unzip it and put it in your working directory.

## Evaluate the data for usability¶

Now the real work starts. We have some data so the first thing we need to do see if it is usable. Our goal is simple: Find some “interesting” data to use for an example of linear regression. We are guessing that housing sale prices data will correlate well with the size of a house. However,

… the first rule of data analysis is don’t assume anything! Look at the data and make sure it usable. Remember, garbage in garbage out!

Lets take a look at this data set and see if we can use it.

The fist thing we need to do is load some Python tools. The following module loads will become a pretty standard start to this kind of work. We are importing modules and using the common abrivations for their namespaces. [pandas, numpy, matplotlib, and seaborn are common tools for this work. I hope to write separate usage posts on these at some point ]

• pandas — is a VERY useful data handling package for Python (it is one of the reasons that Python has become so popular for data analysis!
• numpy — is a standard Python module for numerical calculations with nice array (vector and matrix) handling.
• matplotlib — is a popular plotting library often used as a backend for other packages.
• seaborn — is very nice plotting and data handling package that uses matplotlib (easier to use)

• %matplotlib inline — is what is know as a Jupyter notebook magic it is a just telling the notebook to put the plots in-line in the notebook.

• pd.set_option('...') — lines are just something I added to make the output formatting look better in some of the output cells.

In [1]:
import pandas as pd  # data handeling
import numpy as np   # numeriacal computing
import matplotlib.pyplot as plt  # plotting core
import seaborn as sns  # higher level plotting tools

%matplotlib inline

pd.set_option('display.float_format', lambda x: '%.2f' % x)
pd.set_option('display.max_columns', 21)
pd.set_option('display.max_rows', 70)


After you have “un-zipped” the data file you have a file named kc_house_data.csv” We will load that into a pandas data frame and take a look at the first few lines of data. [ We will call the data-frame df for now, which is common practice.]

In [2]:
df = pd.read_csv("kc_house_data.csv")  # create a dataframe with pandas "pd"

In [3]:
df.head()  # display the first few lines of the dataframe "df"

Out[3]:
id date price bedrooms bathrooms sqft_living sqft_lot floors waterfront view condition grade sqft_above sqft_basement yr_built yr_renovated zipcode lat long sqft_living15 sqft_lot15
0 7129300520 20141013T000000 221900.00 3 1.00 1180 5650 1.00 0 0 3 7 1180 0 1955 0 98178 47.51 -122.26 1340 5650
1 6414100192 20141209T000000 538000.00 3 2.25 2570 7242 2.00 0 0 3 7 2170 400 1951 1991 98125 47.72 -122.32 1690 7639
2 5631500400 20150225T000000 180000.00 2 1.00 770 10000 1.00 0 0 3 6 770 0 1933 0 98028 47.74 -122.23 2720 8062
3 2487200875 20141209T000000 604000.00 4 3.00 1960 5000 1.00 0 0 5 7 1050 910 1965 0 98136 47.52 -122.39 1360 5000
4 1954400510 20150218T000000 510000.00 3 2.00 1680 8080 1.00 0 0 3 8 1680 0 1987 0 98074 47.62 -122.05 1800 7503

The data looks very clean. We wont have to do any type or format conversions. Lets make a quick check for missing values. [ looks OK ]

In [4]:
df.isnull().values.any()  # check for missing values

Out[4]:
False

Here is some basic stats for some of the data.

In [5]:
df[["price","bedrooms","bathrooms","sqft_living","sqft_lot","sqft_above","yr_built","sqft_living15","sqft_lot15"]].describe()

Out[5]:
price bedrooms bathrooms sqft_living sqft_lot sqft_above yr_built sqft_living15 sqft_lot15
count 21613.00 21613.00 21613.00 21613.00 21613.00 21613.00 21613.00 21613.00 21613.00
mean 540088.14 3.37 2.11 2079.90 15106.97 1788.39 1971.01 1986.55 12768.46
std 367127.20 0.93 0.77 918.44 41420.51 828.09 29.37 685.39 27304.18
min 75000.00 0.00 0.00 290.00 520.00 290.00 1900.00 399.00 651.00
25% 321950.00 3.00 1.75 1427.00 5040.00 1190.00 1951.00 1490.00 5100.00
50% 450000.00 3.00 2.25 1910.00 7618.00 1560.00 1975.00 1840.00 7620.00
75% 645000.00 4.00 2.50 2550.00 10688.00 2210.00 1997.00 2360.00 10083.00
max 7700000.00 33.00 8.00 13540.00 1651359.00 9410.00 2015.00 6210.00 871200.00

You can see the home prices vary from \$75K to \$7.7million with living space from 290sqft to 13540sqft. Lots of variety!

The data set contains 21613 observations (home sales in 2014-15) with 19 features plus house price. Descriptions and names of the columns (features) are given below.

• id
• date: Date house was sold
• price: Price of the sold house
• bedrooms: Number of Bedrooms
• bathrooms: Number of bathrooms
• sqft_living: Square footage of the living space
• sqrt_lot: Square footage of the lot
• floors: Total floors in the house
• waterfront: Whether the house is on a waterfront(1: yes, 0: no)
• view: special view?
• condition: Condition of the house
• sqft_above: Square footage of house apart from basement
• sqft_basement: Square footage of the basement
• yr_built: Built year
• yr_renovated: Year when the house was renovated
• zipcode: zipcode of the house
• lat: Latitude coordinate
• long Longitude coordinate
• sqft_living15: Living room area in 2015(implies some renovations)
• sqrt_lot15: Lot area in 2015(implies some renovations)

Since we are doing linear regression we’ll want to look at “continuous” features. Intuitively that will be sqft_living but could possibly be ‘sqft_living’,’sqft_lot’,’sqft_above’,’sqft_living15′,’sqft_lot15′. Lets take a look at these with some plots using seaborn.

In [6]:
sns.pairplot(data=df, x_vars=['sqft_living','sqft_lot','sqft_above','sqft_living15','sqft_lot15'], y_vars=["price"])

Out[6]:

You can see that “lot” size is not well correlated to price but the data for living space is reasonable. Visually the best feature to use looks like sqft_living as we expected.

Lets pull that data out of the data-frame into a new frame.

In [7]:
df2 = df[["price", "sqft_living"]]

Out[7]:
price sqft_living
0 221900.00 1180
1 538000.00 2570
2 180000.00 770
3 604000.00 1960
4 510000.00 1680

Now take a a closer look at the data with a joint distribution plot.

In [8]:
sns.jointplot('sqft_living','price', data=df2, size=10, alpha=.5, marker='+')

Out[8]:

The increase of price with sqft_living space is pretty clear and the “Pearson r value” is .7 indicating a reasonable correlation. However, the data distributions show a big concentration of values in the lower left of the plot. That makes sense, most houses are between 1200 and 3000 sqft and a few hundred thousand dollars. We can eliminate the very expensive and very large houses and take another look at the data.

If we set the size (xlim) from 500 to 3500sqft and the price (ylim) from 100,000 to \$1,000,000 the data still shows the trend but it looks very scattered. In [9]: sns.jointplot('sqft_living','price', data=df2, xlim=(500,3500), ylim=(100000,1000000), size=10, alpha=.5, marker='+')  Out[9]: Something worth considering is that different neighborhoods can vary greatly in average house price. Some nice neighborhoods are very expensive and some other (also nice!) neighborhoods can be quite affordable. It might be good to look at average house price by zipcode since we have that in our dataset. In [10]: df["zipcode"].nunique()  Out[10]: 70 It looks like there are 70 different sip codes in King county. Lets see how many house sales there were in each. In [11]: df['zipcode'].value_counts()  Out[11]: 98103 602 98038 590 98115 583 98052 574 98117 553 98042 548 98034 545 98118 508 98023 499 98006 498 98133 494 98059 468 98058 455 98155 446 98074 441 98033 432 98027 412 98125 410 98056 406 98053 405 98001 362 98075 359 98126 354 98092 351 98144 343 98106 335 98116 330 98029 321 98004 317 98199 317 98065 310 98122 290 98146 288 98028 283 98008 283 98040 282 98198 280 98003 280 98031 274 98072 273 98168 269 98112 269 98055 268 98107 266 98136 263 98178 262 98030 256 98177 255 98166 254 98022 234 98105 229 98045 221 98002 199 98077 198 98011 195 98019 190 98108 186 98119 184 98005 168 98007 141 98188 136 98032 125 98014 124 98070 118 98109 109 98102 105 98010 100 98024 81 98148 57 98039 50 Name: zipcode, dtype: int64 How about the average house sale price in each zipcode … In [12]: df.groupby('zipcode')['price'].mean() # group by zipcode and compute the mean of prices in a zipcode  Out[12]: zipcode 98001 280804.69 98002 234284.04 98003 294111.28 98004 1355927.08 98005 810164.88 98006 859684.78 98007 617105.09 98008 645507.38 98010 423665.99 98011 490351.47 98014 455617.11 98019 424788.75 98022 315709.30 98023 286732.79 98024 580526.79 98027 616990.59 98028 462480.04 98029 612653.61 98030 296187.98 98031 300539.89 98032 251296.24 98033 803719.52 98034 521652.86 98038 366867.60 98039 2160606.60 98040 1194230.02 98042 311632.11 98045 439471.08 98052 645231.46 98053 678163.06 98055 304262.11 98056 420890.55 98058 353608.64 98059 493552.53 98065 527961.20 98070 487479.63 98072 569958.47 98074 685605.78 98075 790576.65 98077 682774.88 98092 334921.06 98102 901258.27 98103 584919.21 98105 862825.23 98106 319581.39 98107 579053.42 98108 355678.52 98109 879623.62 98112 1095499.34 98115 619900.55 98116 618634.17 98117 576795.01 98118 417637.43 98119 849448.02 98122 634360.18 98125 469455.77 98126 424706.36 98133 386997.40 98136 551688.67 98144 594547.65 98146 359483.24 98148 284908.60 98155 423725.70 98166 464231.84 98168 240328.37 98177 676185.39 98178 310612.76 98188 289078.35 98198 302878.88 98199 791820.81 Name: price, dtype: float64 The two zipcodes that look the most interesting to me are 98103 and 98039. 98103 has the most house sale values, 602, with an average sale price of \$584,919. The most expensive zipcode 98039 has 50 sale values with an average sale price of \\$2,160,606.

We can create “selectors” by creating lists of true-false values for data entries that match these two zipcodes and then use those to filter our data-frame.

In [13]:
zip98103 = df['zipcode'] == 98103  # True if zip is 98103
zip98039 = df['zipcode'] == 98039


Using the “selectors” above we can look at plots of price vs sqft_living in those zipcodes.

In [14]:
sns.jointplot('sqft_living','price', data=df2[zip98103], size=10, alpha=.5, marker='+')

Out[14]:
In [15]:
sns.jointplot('sqft_living','price', data=df2[zip98039], size=10, alpha=.5, marker='+')

Out[15]: