首页 » Python辅导 » Python辅导 | ANLY 530 Machine Learning Laboratory 2

Python辅导 | ANLY 530 Machine Learning Laboratory 2

这个作业是使用机器学习对银行的客户数据进行分析
ANLY 530 Machine Learning I, Laboratory #2
Introduction
As you learned in ANLY 500 analytics can be categorized as descriptive, predictive or
prescriptive. Machine learning is engaged in all categories of analytics. We’ve obviously done
descriptive and predictive analytics. Prescriptive analytics is what happens when you take
predictions made and use them to make strategic changes, e.g. to a business model in order to
refocus or enhance the model. Part of this process is also handling risk. Analytics is frequently
used to consider a variety of types of risk; credit, sales, fraud related, etc. One of the challenges
to prescriptive analytics is the complexity in integrating multiple streams of data, e.g. business,
risk, environmental, contextual, stakeholder, and so much more.
This laboratory will get you started thinking about pieces of what is required for prescriptive
analytics and in particular how machine learning can be used to support that function. This
laboratory will specifically look at credit risk. We’ll define credit risk as the risk of default on a
debt due to a borrower failing to make the required payments in a timely manner. In this
definition, the lender assumes the risk of losing both the principal and the anticipated interest on
that principal. For the problems in this laboratory we will use a bank as the financial institution.
The bank will analyze customer data to predict which customers might be credit risks. These
predictions will then feed into risk management.
This particular laboratory focuses on the use of Bayesian methods, specifically Naïve Bayesian
Classifiers. You won’t necessarily have to read the uploaded papers or the websites listed below
to complete this laboratory. They are available only if you want more information. These two
papers have been uploaded to Moodle and are intended to provide you with additional
background and information about the different challenges and algorithms used for credit
scoring; “Guide to Credit Scoring in R’ by Sharma, and “Classification methods applied to credit
scoring: A systematic review and overall comparison” by Louzada. You may use following
links to learn more about Bayesian network learning and its usage in scikit-learn package:
 https://scikit-learn.org/stable/modules/naive_bayes.html
 https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/
A data set has been provided for you, creditData.csv. We’ve used many data sets already.
You’ll need to start by loading the data into Python and looking at the data set. Since we’ve
done this many times I won’t include a separate step for reading in the data and examining it in
this laboratory. That is, the equivalent of “Step 1” in Laboratory #1 and is not explicitly
included in this laboratory. A description of the data set is included at the end of this laboratory.
2
One of the differences between data mining and machine learning is that machine learning
focuses more on improving performance, whether that is improving the performance of an
algorithm or increasing learning, than it (machine learning) does on the specific results obtained
from running an algorithm (as does data mining). In this laboratory we’ll begin to consider that
and take a “brute force” approach to improving performance and/or increasing learning. We’ll
begin by using Python functions for a Naïve Bayes Classifier as black-box tools and then we’ll
apply common techniques for improvement. I’ll call the original, unimproved application of the
algorithms Part 1. The brute force part is that we’ll apply techniques to improve performance
individually and manually to the original algorithms. I’ll call this Part 2.
Laboratory 2: Naïve Bayes Classifiers, Part 1
Step 1: Exploring and Preparing the Data For many functions to work correctly there cannot
be any missing values in the data set. So far we haven’t used any data sets that require a lot of
processing or looking to determine if there are missing values. For this data set it is more
difficult to just visually ensure that there are no missing values. So, you could use the following
command built on the sum() and isnull() functions (in newer versions of python, you can use
isna() command). It will return a value that is the sum (total number of) all missing values:
credit.isnull().sum()
Q1- What is your suggestion if you see any NA values?
Next, use a 75%/25% split for training and test data, i.e. use 75% of the records for the training
set and 25% of the records for the test set. Report the number of missing values you find in the
data in your results report. Use the randomization seed of 123.
Q2- Compute the percentage of both classes similar to what you did in lab 1 and see if the
distribution of both classes preserved for both training and testing data.
Step 2: Training a Model on the Data Use the GaussianNB()function on your training data to
build the Naïve Bayes Classifier.
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
gnb = gnb.fit(X_train, y_train)
Step 3: Evaluating Model Performance Now, use the processes we have completed before to
evaluate your Naïve Bayes Classifier. (Hint: If you didn’t convert your class variable before you
will not be able to complete this step!) In your results report include your confusion table
3
showing your results. Be sure to include 2-3 sentences explaining what those results actually
mean.
Laboratory 2: Naïve Bayes Classifiers, Part 2
In this part of the laboratory we’ll manually work to improve the performance of the Naïve
Bayes classifier. There are several ways of improving the performance of an algorithm, e.g. preprocessing the data can make a very big difference in the performance of any algorithm. One
category of methods to improve performance of algorithms is referred to as feature selection.
We will follow the approach using correlations to determine if there are variables in the data set
that if they are eliminated would improve the performance of the Naïve Bayes Classifier. You
can choose whatever method you want to use to select the features (variables) to keep. Be sure
to include a paragraph or so in your results report describing what/how you tried to increase the
Naïve Bayes algorithm’s performance; and, include any gains in performance in that report too.
Step 1: Exploring and Preparing the Data The first approach I’ll introduce is using the
correlation matrix to perform feature, i.e. variable selection. As always, there are several
packages with functions for computing a correlation matrix. But, before doing anything you
need to determine if any pre-processing of the data is required, e.g. scaling. Be sure to report any
pre-processing of the data in your results report
Next, you’ll need to pull out those features, or variables, with correlations higher than a specified
threshold. What is the best threshold to use? It depends on whether you’re most interested in
achieving the highest level of classification with the fewest errors (increasing performance) or in
balancing this with the variables that reduce overall risk the most. That is, if you eliminate too
many features (variables) you may end up with a classifier that misses some critical piece of
evidence that actually increases risk even though the classifier performs more perfectly.
Let’s start with using following code to see how correlated the variables are:
import matplotlib.pyplot as plt
corr = X_train.corr()
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(corr,cmap=’coolwarm’, vmin=-1, vmax=1)
fig.colorbar(cax)
ticks = np.arange(0,len(X_train.columns),1)
ax.set_xticks(ticks)
plt.xticks(rotation=90)
ax.set_yticks(ticks)
ax.set_xticklabels(X_train.columns)
ax.set_yticklabels(X_train.columns)
plt.show()
4
By running this code, you will see this graph.
You can print the variable corr to see actual value of correlations.
Now you can use following code to remove the features that have correlation over 0.6.
# Create correlation matrix
corr_matrix = X.corr().abs()
# Select upper triangle of correlation matrix
upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape),
k=1).astype(np.bool))
# Find index of feature columns with correlation greater than 0.60
to_drop = [column for column in upper.columns if any(upper[column] > 0.6)]
Xnew = X.drop(to_drop, axis=1)
Split your data in the similar way that you did in previous section.
Step 2: Training a Model on the Data At this point we follow the same process that we have
before. To train the model
Step 3: Evaluating Model Performance
5
Evaluate the Naïve Bayes Classifier as usual
Q3- What is the accuracy this time? Be sure to include in your results report whether or not, after
all this work, the performance of your Naïve Bayes Classifier was improved.
Laboratory 2: Support Vector Machine, Part 3
Step 1: Collecting the Data
We’ll look at support vector machines from the perspective of optical character recognition
(OCR).We’ll use the letterdata.csv file also from the UCI Data Repository.We can simply read it
in and look at the structure as follows:
input_file = “address/of/the/file/letterdata.csv”
letters = pd.read_csv(input_file)
letters.head(5)
Step 2: Preparing the Data
The only thing we need to do to prepare this data set is to subset it into our training and test data
sets. There are 20,000 observations so we have considerable flexibility in deciding how many to
put in our training set and how many to keep for testing. Let’s use 90% of this data for training
and the rest for testing. Use any of the methods that you have learnt by now.
Step 3: Training a Model on the Data
Use the same methodology that you saw in class and design a linear SVM to make classification.
from sklearn import svm
# Design the model
clf = svm.SVC(kernel=’linear’)
#clf = svm.LinearSVC(C=1)
clf.fit(X_train, y_train)
y_predict = clf.predict(X_test)
Step 4: Evaluating Model Performance
6
Again, we’ll develop a confusion table to determine how well our model performed overall.
Since we are looking at letters of the alphabet we’ll have a considerable number of columns and
rows to our table. You might want to adjust the size of the console frame accordingly. The
commands to run the evaluation and output the table is:
confusion_matrix(y_test, y_predict)
array([[71,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0, 0,1,0,0,0,0,0,0,2,0],
[1,67,0,1,0,0,2,1,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0],
[0,0,67,0,2,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],
[0,5,0,67,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0],
[0,1,1,0,58,1,3,0,1,0,1,4,0,0,0,0,2,0,1,3,0,0,0,0,0,2],
[0,1,1,0,0,74,0,0,0,1,0,0,0,2,0,1,0,0,3,2,0,0,0,0,1,0],
[0,0,4,3,1,0,61,0,0,0,1,2,0,0,0,1,2,0,4,0,0,0,1,0,0,0],
[0,1,0,0,0,2,0,52,0,1,1,1,0,0,7,0,0,3,0,0,2,0,0,1,0,0],
[0,1,0,1,0,2,0,0,67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1],
[2,0,0,0,0,1,0,0,5,67,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1],
[0,1,2,1,0,0,3,2,0,0,68,0,0,0,0,0,0,3,0,1,0,0,0,2,0,0],
[0,0,0,0,1,0,1,1,0,0,1,63,0,0,0,0,2,0,1,0,0,0,0,0,0,0],
[0,1,0,0,0,0,0,3,0,0,0,0,83,0,1,0,0,2,0,0,1,0,0,0,0,0],
[0,0,0,0,0,0,0,1,0,0,1,0,0,65,1,0,0,1,0,0,0,1,0,0,0,0],
[1,0,1,1,0,0,0,7,0,0,0,0,1,0,66,0,1,1,0,0,1,0,1,0,0,0],
[0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,69,0,0,0,0,0,0,1,0,2,0],
[3,0,0,1,0,0,1,2,0,0,1,0,0,0,3,1,49,0,4,0,0,1,0,0,0,0],
[1,3,1,1,0,0,1,1,0,0,1,0,0,0,1,0,0,62,0,0,0,0,0,0,0,0],
[0,3,0,0,2,1,2,0,1,0,0,1,0,0,0,0,4,0,51,0,0,0,0,1,0,6],
[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,76,0,0,0,0,3,0],
[0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,70,0,0,0,0,0],
[1,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,69,0,0,4,0],
[0,0,0,0,0,0,1,0,0,0,0,0,4,1,1,0,0,0,0,0,3,1,68,0,0,0],
[0,0,0,0,2,0,0,0,1,1,2,2,0,0,0,0,0,0,0,0,0,0,0,73,1,0],
[1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,3,76,0],
[0,0,0,0,2,0,0,0,0,3,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,62]], dtype=
int64)
Now compute the accuracy.
Q4- We may be able to do better than this by changing the Kernels. Try Polynomial and RBF
kernels to improve the result.
Laboratory 2: News popularity, Part 4
Now apply the Naïve Bayes classifier and SVM that you saw in Parts 1 through 3 on News
popularity data set from lab 1.
7
Running SVM on original dataset will take a long time. To resolve this issue, try to remove some
of the features or consider less number of instances for training.
Q5- Do you see any improvement compared to last three techniques? Please completely explain
your results and analysis.
8
Summary of covariates for the dataset
“Determining the solidness of borrowers via creditscoring”
Notice:
The given score for the categorical covariates is based on the assessment of experienced bank
specialists dealing with credits and is contained in the downloadable file. The description of the
variables is consistent with “Fahrmeir / Hamerle / Tutz (1996, 2nd ed.): Multivariate statistische
Verfahren. de Gruyter, Berlin. p. 390 ff.”
Variable Description Categories Score
rel. frequency
in % for
good
credits
bad
credits
Creditability
Creditability:
1 : credit-worthy
0 : not credit-worthy
Account Balance Balance of current
account
no balance or debit 2 35.00 23.43
0 <= … < 200 DM 3 4.67 7.00
… >= 200 DM or
checking account for
at least 1 year
4 15.33 49.71
no running account 1 45.00 19.86
Duration of
Credit (month) Duration in months (metric)
Duration of
Credit (month)
Duration in months
(categorized)
<=6 10 3.00 10.43
6 < … <= 12 9 22.33 30.00
12 < … <= 18 8 18.67 18.71
18 < … <= 24 7 22.00 22.57
24 < … <= 30 6 6.33 5.43
30 < … <= 36 5 12.67 6.86
36 < … <= 42 4 1.67 1.71
42 < … <= 48 3 10.67 3.14
48 < … <= 54 2 0.33 0.14
> 54 1 2.33 1.00
Payment Status
of Previous
Payment of previous
credits
no previous credits /
paid back all previous 2 56.33 51.57
9
Credit credits
paid back previous
credits at this bank 4 16.67 34.71
no problems with
current credits at this
bank
3 9.33 8.57
hesitant payment of
previous credits 0 8.33 2.14
problematic running
account / there are
further credits running
but at other banks
1 9.33 3.00
Purpose Purpose of credit
new car 1 5.67 12.29
used car 2 19.33 17.57
items of furniture 3 20.67 31.14
radio / television 4 1.33 1.14
household appliances 5 2.67 2.00
repair 6 7.33 4.00
education 7 0.00 0.00
vacation 8 0.33 1.14
retraining 9 11.33 9.00
business 10 1.67 1.00
other 0 29.67 20.71
Credit Amount Amount of credit in “Deutsche Mark” (metric)
Credit Amount Amount of credit in
DM (categorized)
<=500 10 1.00 2.14
500 < … <= 1000 9 11.33 9.14
1000 < … <= 1500 8 17.00 19.86
1500 < … <= 2500 7 19.67 24.57
2500 < … <= 5000 6 25.00 28.57
5000 < … <= 7500 5 11.33 9.71
7500 < … <= 10000 4 6.67 3.71
10000 < … <= 15000 3 7.00 2.00
15000 < … <= 20000 2 1.00 0.29
> 20000 1 0.00 0.00
10
Value
Savings/Stocks
Value of savings or
stocks
< 100,- DM 2 11.33 9.86
100,- <= … < 500,-
DM 3 3.67 7.43
500,- <= … < 1000,-
DM 4 2.00 6.00
>= 1000,- DM 5 10.67 21.57
not available / no
savings 1 72.33 55.14
Length of
current
employment
Has been employed
by current employer
for
unemployed 1 7.67 5.57
<= 1 year 2 23.33 14.57
1 <= … < 4 years 3 34.67 33.57
4 <= … < 7 years 4 13.00 19.29
>= 7 years 5 21.33 27.00
Instalment per
cent
Installment in % of
available income
>= 35 1 11.33 14.57
25 <= … < 35 2 20.67 24.14
20 <= … < 25 3 15.00 16.00
< 20 4 53.00 45.29
Sex & Marital
Status Marital Status / Sex
male: divorced / living
apart 1 6.67 4.29
female: divorced /
living apart / married 2 11.33 10.29
male: single 2 25.00 18.43
male: married /
widowed 3 48.67 57.43
female: single 4 8.33 9.57
Guarantors Further debtors /
Guarantors
none 1 90.67 90.71
Co-Applicant 2 6.00 3.29
Guarantor 3 3.33 6.00
Duration in
Current address
Living in current
household for
< 1 year 1 12.00 13.43
1 <= … < 4 years 2 32.33 30.14
4 <= … < 7 years 3 14.33 15.14
>= 7 years 4 41.33 41.29
Most valuable
available asset
Most valuable
available assets
Ownership of house or
land 4 22.33 12.43
11
Savings contract with
a building society /
Life insurance
3 34.00 32.86
Car / Other 2 23.67 23.00
not available / no
assets 1 20.00 31.71
Age (years) Age in years (metric)
Age (years) Age in years
(categorized)
0 <= … <= 25 1 26.67 15.71
26 <= … <= 39 2 47.33 52.72
40 <= … <= 59 3 21.67 26.14
60 <= … <= 64 5 2.33 3.00
>= 65 4 2.00 2.43
Concurrent
Credits
Further running
credits
at other banks 1 19.00 11.71
at department store or
mail order house 2 6.33 4.00
no further running
credits 3 74.67 84.29
Type of
apartment Type of apartment
rented flat 2 62.00 75.43
owner-occupied flat 3 14.67 9.14
free apartment 1 23.33 15.57
No of Credits at
this Bank
Number of previous
credits at this bank
(including the
running one)
one 1 66.67 61.86
two or three 2 30.67 34.43
four or five 3 2.00 3.14
six or more 4 0.67 0.57
Occupation Occupation
unemployed /
unskilled with no
permanent residence
1 2.33 2.14
unskilled with
permanent residence 2 18.67 20.57
skilled worker / skilled
employee / minor civil
servant
3 62.00 63.43
executive / selfemployed / higher
civil servant
4 17.00 13.86
No of dependents Number of persons 0 to 2 2 84.67 84.43
12
entitled to
maintenance 3 and more 1 15.33 15.57
Telephone Telephone
no 1 62.33 58.43
yes 2 37.67 41.57
Foreign Worker Foreign worker
yes 1 1.33 4.71
no 2 98.67 95.29
The covariates M1, M2, M5, M7, M8 are consistent with the description in “Fahrmeir / Hamerle
/ Tutz (1985, 1st ed.): Multivariate statistische Verfahren. de Gruyter, Berlin. p. 285 ff.”


程序辅导定制C/C++/JAVA/安卓/PYTHON/留学生/PHP/APP开发/MATLAB


本网站支持 Alipay WeChatPay PayPal等支付方式

E-mail: vipdue@outlook.com  微信号:vipnxx


如果您使用手机请先保存二维码,微信识别。如果用电脑,直接掏出手机果断扫描。

blank

发表评论

您的电子邮箱地址不会被公开。