Data science pipeline: pandas, seaborn, scikit-learn¶

From the video series: Introduction to machine learning with scikit-learn

Agenda¶

How do I use the pandas library to read data into Python?
How do I use the seaborn library to visualize data?
What is linear regression, and how does it work?
How do I train and interpret a linear regression model in scikit-learn?
What are some evaluation metrics for regression problems?
How do I choose which features to include in my model?

Types of supervised learning¶

Classification: Predict a categorical response
Regression: Predict a continuous response

Reading data using pandas¶

Pandas: popular Python library for data exploration, manipulation, and analysis

Anaconda users: pandas is already installed
Other users: installation instructions

In [2]:

# conventional way to import pandas
import pandas as pd

In [3]:

# read CSV file directly from a URL and save the results
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)

# display the first 5 rows
data.head()

Out[3]:

	TV	Radio	Newspaper	Sales
1	230.1	37.8	69.2	22.1
2	44.5	39.3	45.1	10.4
3	17.2	45.9	69.3	9.3
4	151.5	41.3	58.5	18.5
5	180.8	10.8	58.4	12.9

Primary object types:

DataFrame: rows and columns (like a spreadsheet)
Series: a single column

In [4]:

# display the last 5 rows
data.tail()

Out[4]:

	TV	Radio	Newspaper	Sales
196	38.2	3.7	13.8	7.6
197	94.2	4.9	8.1	9.7
198	177.0	9.3	6.4	12.8
199	283.6	42.0	66.2	25.5
200	232.1	8.6	8.7	13.4

In [5]:

# check the shape of the DataFrame (rows, columns)
data.shape

Out[5]:

(200, 4)

What are the features?

TV: advertising dollars spent on TV for a single product in a given market (in thousands of dollars)
Radio: advertising dollars spent on Radio
Newspaper: advertising dollars spent on Newspaper

What is the response?

Sales: sales of a single product in a given market (in thousands of items)

What else do we know?

Because the response variable is continuous, this is a regression problem.
There are 200 observations (represented by the rows), and each observation is a single market.

Visualizing data using seaborn¶

Seaborn: Python library for statistical data visualization built on top of Matplotlib

Anaconda users: run conda install seaborn from the command line
Other users: installation instructions

In [6]:

# conventional way to import seaborn
import seaborn as sns

# allow plots to appear within the notebook
%matplotlib inline

In [7]:

# visualize the relationship between the features and the response using scatterplots
sns.pairplot(data, x_vars=['TV','Radio','Newspaper'], y_vars='Sales', size=7, aspect=0.7, kind='reg')

Out[7]:

<seaborn.axisgrid.PairGrid at 0xaaf8fd0>

Linear regression¶

Pros: fast, no tuning required, highly interpretable, well-understood

Cons: unlikely to produce the best predictive accuracy (presumes a linear relationship between the features and response)

Form of linear regression¶

$y = \beta_0 + \beta_1x_1 + \beta_2x_2 + ... + \beta_nx_n$

$y$ is the response
$\beta_0$ is the intercept
$\beta_1$ is the coefficient for $x_1$ (the first feature)
$\beta_n$ is the coefficient for $x_n$ (the nth feature)

In this case:

$y = \beta_0 + \beta_1 \times TV + \beta_2 \times Radio + \beta_3 \times Newspaper$

The $\beta$ values are called the model coefficients. These values are "learned" during the model fitting step using the "least squares" criterion. Then, the fitted model can be used to make predictions!

Preparing X and y using pandas¶

scikit-learn expects X (feature matrix) and y (response vector) to be NumPy arrays.
However, pandas is built on top of NumPy.
Thus, X can be a pandas DataFrame and y can be a pandas Series!

In [8]:

# create a Python list of feature names
feature_cols = ['TV', 'Radio', 'Newspaper']

# use the list to select a subset of the original DataFrame
X = data[feature_cols]

# equivalent command to do this in one line
X = data[['TV', 'Radio', 'Newspaper']]

# print the first 5 rows
X.head()

Out[8]:

	TV	Radio	Newspaper
1	230.1	37.8	69.2
2	44.5	39.3	45.1
3	17.2	45.9	69.3
4	151.5	41.3	58.5
5	180.8	10.8	58.4

In [9]:

# check the type and shape of X
print(type(X))
print(X.shape)

<class 'pandas.core.frame.DataFrame'>
(200, 3)

In [10]:

# select a Series from the DataFrame
y = data['Sales']

# equivalent command that works if there are no spaces in the column name
y = data.Sales

# print the first 5 values
y.head()

Out[10]:

1    22.1
2    10.4
3     9.3
4    18.5
5    12.9
Name: Sales, dtype: float64

In [11]:

# check the type and shape of y
print(type(y))
print(y.shape)

<class 'pandas.core.series.Series'>
(200L,)

Splitting X and y into training and testing sets¶

In [12]:

from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)

In [13]:

# default split is 75% for training and 25% for testing
print(X_train.shape)
print(y_train.shape)
print(X_test.shape)
print(y_test.shape)

(150, 3)
(150L,)
(50, 3)
(50L,)

Linear regression in scikit-learn¶

In [14]:

# import model
from sklearn.linear_model import LinearRegression

# instantiate
linreg = LinearRegression()

# fit the model to the training data (learn the coefficients)
linreg.fit(X_train, y_train)

Out[14]:

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

Interpreting model coefficients¶

In [15]:

# print the intercept and coefficients
print(linreg.intercept_)
print(linreg.coef_)

2.87696662232
[ 0.04656457  0.17915812  0.00345046]

In [16]:

# pair the feature names with the coefficients
list(zip(feature_cols, linreg.coef_))

Out[16]:

[('TV', 0.046564567874150288),
 ('Radio', 0.17915812245088836),
 ('Newspaper', 0.0034504647111804065)]

$$y = 2.88 + 0.0466 \times TV + 0.179 \times Radio + 0.00345 \times Newspaper$$

How do we interpret the TV coefficient (0.0466)?

For a given amount of Radio and Newspaper ad spending, a "unit" increase in TV ad spending is associated with a 0.0466 "unit" increase in Sales.
Or more clearly: For a given amount of Radio and Newspaper ad spending, an additional $1,000 spent on TV ads is associated with an increase in sales of 46.6 items.

Important notes:

This is a statement of association, not causation.
If an increase in TV ad spending was associated with a decrease in sales, $\beta_1$ would be negative.

Making predictions¶

In [17]:

# make predictions on the testing set
y_pred = linreg.predict(X_test)

We need an evaluation metric in order to compare our predictions with the actual values!

Model evaluation metrics for regression¶

Evaluation metrics for classification problems, such as accuracy, are not useful for regression problems. Instead, we need evaluation metrics designed for comparing continuous values.

Let's create some example numeric predictions, and calculate three common evaluation metrics for regression problems:

In [18]:

# define true and predicted response values
true = [100, 50, 30, 20]
pred = [90, 50, 50, 30]

Mean Absolute Error (MAE) is the mean of the absolute value of the errors:

$$\frac 1n\sum_{i=1}^n|y_i-\hat{y}_i|$$

In [19]:

# calculate MAE by hand
print((10 + 0 + 20 + 10)/4.)

# calculate MAE using scikit-learn
from sklearn import metrics
print(metrics.mean_absolute_error(true, pred))

10.0
10.0

Mean Squared Error (MSE) is the mean of the squared errors:

$$\frac 1n\sum_{i=1}^n(y_i-\hat{y}_i)^2$$

In [20]:

# calculate MSE by hand
print((10**2 + 0**2 + 20**2 + 10**2)/4.)

# calculate MSE using scikit-learn
print(metrics.mean_squared_error(true, pred))

150.0
150.0

Root Mean Squared Error (RMSE) is the square root of the mean of the squared errors:

$$\sqrt{\frac 1n\sum_{i=1}^n(y_i-\hat{y}_i)^2}$$

In [21]:

# calculate RMSE by hand
import numpy as np
print(np.sqrt((10**2 + 0**2 + 20**2 + 10**2)/4.))

# calculate RMSE using scikit-learn
print(np.sqrt(metrics.mean_squared_error(true, pred)))

12.2474487139
12.2474487139

Comparing these metrics:

MAE is the easiest to understand, because it's the average error.
MSE is more popular than MAE, because MSE "punishes" larger errors.
RMSE is even more popular than MSE, because RMSE is interpretable in the "y" units.

Computing the RMSE for our Sales predictions¶

In [22]:

print(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))

1.40465142303

Feature selection¶

Does Newspaper "belong" in our model? In other words, does it improve the quality of our predictions?

Let's remove it from the model and check the RMSE!

In [23]:

# create a Python list of feature names
feature_cols = ['TV', 'Radio']

# use the list to select a subset of the original DataFrame
X = data[feature_cols]

# select a Series from the DataFrame
y = data.Sales

# split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)

# fit the model to the training data (learn the coefficients)
linreg.fit(X_train, y_train)

# make predictions on the testing set
y_pred = linreg.predict(X_test)

# compute the RMSE of our predictions
print(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))

1.38790346994

The RMSE decreased when we removed Newspaper from the model. (Error is something we want to minimize, so a lower number for RMSE is better.) Thus, it is unlikely that this feature is useful for predicting Sales, and should be removed from the model.

Resources¶

Linear regression:

Longer notebook on linear regression by me
Chapter 3 of An Introduction to Statistical Learning and related videos by Hastie and Tibshirani (Stanford)
Quick reference guide to applying and interpreting linear regression by me
Introduction to linear regression by Robert Nau (Duke)

Pandas:

Three-part pandas tutorial by Greg Reda
read_csv and read_table documentation

Seaborn:

Comments or Questions?¶

In [1]:

from IPython.core.display import HTML
def css_styling():
    styles = open("styles/custom.css", "r").read()
    return HTML(styles)
css_styling()

Out[1]: