{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

Chap3_Exploration

Chap3_Exploration - Chapter 3 Data Exploration and...

This preview shows pages 1–14. Sign up to view the full content.

Chapter 3 – Data Exploration and Dimension Reduction © Galit Shmueli and Peter Bruce 2008 Data Mining for Business Intelligence Shmueli, Patel & Bruce

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Exploring the data Statistical summary of data: common metrics Average Median Minimum Maximum Standard deviation Counts & percentages
Summary Statistics – Boston Housing

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Correlations Between Pairs of Variables: Correlation Matrix from Excel PTRATIO B LSTAT MEDV PTRATIO 1 B -0.17738 1 LSTAT 0.374044 -0.36609 1 MEDV -0.50779 0.333461 -0.73766 1
Summarize Using Pivot Tables Count of MEDV CHAS Total 0 471 1 35 Grand Total 506 Counts & percentages are useful for summarizing categorical data Boston Housing example: 471 neighborhoods border the Charles River (1) 35 neighborhoods do not (0)

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Pivot Tables - cont. In Boston Housing example: Compare average home values in neighborhoods that border Charles River (1) and those that do not (0) Average of MEDV CHAS Total 0 22.09 1 28.44 Grand Total 22.53 Averages are useful for summarizing grouped numerical data
Pivot Tables, cont. Group by multiple criteria: By # rooms and location E.g., neighborhoods on the Charles with 6-7 rooms have average house value of 25.92 (\$000) Average of MEDV CHAS RM 0 1 Grand Total 3-4 25.30 25.30 4-5 16.02 16.02 5-6 17.13 22.22 17.49 6-7 21.77 25.92 22.02 7-8 35.96 44.07 36.92 8-9 45.70 35.95 44.20 Grand Total 22.09 28.44 22.53

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Graphs
Histograms Histogram shows the distribution of the outcome variable (median house value) 0 20 40 60 80 100 120 140 160 180 5 10 15 20 25 30 35 40 45 50 Frequency MEDV Histogram Boston Housing example:

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Boxplots Boston Housing Example: Display distribution of outcome variable (MEDV) for neighborhoods on Charles (1) and not on Charles (0) 0 1 0 10 20 30 40 50 60 Y Values CHAS Box Plot MEDV Side-by-side boxplots are useful for comparing subgroups
Box Plot Top outliers defined as those above Q3+1.5(Q3-Q1). “max” is the maximum of non-outliers Analogous definitions for bottom outliers and for “min” Details may differ across software Media n Quartile 1 “max “min” outliers mea n outlier Quartile 3

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Correlation Analysis Below: Correlation matrix for portion of Boston Housing data Shows correlation between variable pairs CRIM ZN INDUS CHAS NOX RM CRIM 1 ZN -0.20047 1 INDUS 0.406583 -0.53383 1 CHAS -0.05589 -0.0427 0.062938 1 NOX 0.420972 -0.5166 0.763651 0.091203 1 RM -0.21925 0.311991 -0.39168 0.091251 -0.30219 1
Matrix Plot Shows scatterplots for variable pairs Example: scatterplots for 3 Boston Housing variables

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

Page1 / 29

Chap3_Exploration - Chapter 3 Data Exploration and...

This preview shows document pages 1 - 14. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online