代写代考 AMD64)]

To download the data, we will use !wget (on CognitiveClass Virtual Lab)

if sys.platform.startswith(‘linux’):
!wget -O /resources/customer_dbase_sel.csv http://analytics.romanko.ca/data/customer_dbase_sel.csv

Download data on any platform

opener = urllib.request.build_opener()
opener.addheaders = [(‘User-agent’, ‘Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:66.0) Gecko/20100101 Firefox/66.0’)]
urllib.request.install_opener(opener)
urllib.request.urlretrieve(“http://analytics.romanko.ca/data/customer_dbase_sel.csv”, “customer_dbase_sel.csv”)

(‘customer_dbase_sel.csv’, )

Understanding the Data¶
customer_dbase_sel.csv:¶
We have downloaded an extract from IBM SPSS sample dataset with customer data, customer_dbase_sel.csv, which contains customer-specific data such as age, income, credit card spendings, commute type and time, etc. Dataset source

custid e.g. 0648-AIPJSP-UVM (customer id)
gender e.g. Female or Male
age e.g. 26
debtinc e.g. 11.1 (debt to income ratio in %)
card e.g. Visa, Mastercard (type of primary credit card)
carditems e.g. 1, 2, 3 … (# of primary credit card purchases in the last month)
cardspent e.g 228.27 (amount in \$ spent on the primary credit card last month)
commute e.g. Walk, Car, Bus (commute type)
commutetime e.g. 22 (time in minutes to commute to work)
income e.g. 16.00 (income in thousands \$ per year)
edcat e.g. College degree, Post-undergraduate degree (education level)

Reading the data in¶

url = “customer_dbase_sel.csv”
df = pd.read_csv(url)

## On CognitiveClass Virtual Lab you can read from /resources directory
#df = pd.read_csv(“/resources/customer_dbase_sel.csv”)

# display first 5 rows of the dataset

custid gender age age_cat debtinc card carditems cardspent cardtype creddebt … carown region ed_cat ed_years job_cat employ_years emp_cat retire annual_income inc_cat
0 3964-QJWTRG-NPN Female 20 18-24 11.1 Mastercard 5 81.66 None 1.20 … Own Zone 1 Some college 15 Managerial and Professional 0 Less than 2 No 31000.0 $25 – $49
1 0648-AIPJSP-UVM Male 22 18-24 18.6 Visa 5 42.60 Other 1.22 … Own Zone 5 College degree 17 Sales and Office 0 Less than 2 No 15000.0 Under $25
2 5195-TLUDJE-HVO Female 67 >65 9.9 Visa 9 184.22 None 0.93 … Own Zone 3 High school degree 14 Sales and Office 16 More than 15 No 35000.0 $25 – $49
3 4459-VLPQUH-3OL Male 23 18-24 5.7 Visa 17 340.99 None 0.02 … Own Zone 4 Some college 16 Sales and Office 0 Less than 2 No 20000.0 Under $25
4 8158-SMTQFB-CNO Male 26 25-34 1.7 Discover 8 255.10 Gold 0.21 … Lease Zone 2 Some college 16 Sales and Office 1 Less than 2 No 23000.0 Under $25

5 rows × 30 columns

Data Exploration¶

# Summarize the data
df.describe()

age debtinc carditems cardspent creddebt commutetime card2items card2spent cars ed_years employ_years annual_income
count 5000.000000 5000.000000 5000.00000 5000.000000 5000.000000 4998.000000 5000.000000 5000.000000 5000.000000 5000.000000 5000.000000 5.000000e+03
mean 46.939800 9.957800 10.19920 339.635878 1.874982 25.346739 4.666000 161.331270 2.134200 14.537600 9.740200 5.504060e+04
std 17.703312 6.423173 3.39279 248.382982 3.441425 5.890674 2.482434 146.798035 1.306037 3.294717 9.691062 5.554475e+04
min 18.000000 0.000000 0.00000 0.000000 0.000000 7.000000 0.000000 0.000000 0.000000 6.000000 0.000000 9.000000e+03
25% 32.000000 5.175000 8.00000 184.860000 0.390000 21.000000 3.000000 67.682500 1.000000 12.000000 2.000000 2.400000e+04
50% 46.000000 8.800000 10.00000 278.655000 0.930000 25.000000 5.000000 125.455000 2.000000 14.000000 7.000000 3.800000e+04
75% 62.000000 13.500000 12.00000 422.402500 2.080000 29.000000 6.000000 208.612500 3.000000 17.000000 15.000000 6.700000e+04
max 79.000000 43.100000 23.00000 3926.410000 109.070000 48.000000 15.000000 2069.250000 8.000000 23.000000 52.000000 1.073000e+06

# Number of rows and columns in the data

(5000, 30)

# Display column names
df.columns

Index([‘custid’, ‘gender’, ‘age’, ‘age_cat’, ‘debtinc’, ‘card’, ‘carditems’,
‘cardspent’, ‘cardtype’, ‘creddebt’, ‘commute’, ‘commutetime’, ‘card2’,
‘card2items’, ‘card2spent’, ‘card2type’, ‘marital’, ‘homeown’,
‘hometype’, ‘cars’, ‘carown’, ‘region’, ‘ed_cat’, ‘ed_years’, ‘job_cat’,
’employ_years’, ’emp_cat’, ‘retire’, ‘annual_income’, ‘inc_cat’],
dtype=’object’)

Labeling Data¶
income > 30000 –> High-income –> 1

income < 30000 --> Low-income –> 0

# To label data into high-income and low-income
df[‘income_category’] = df[‘annual_income’].map(lambda x: 1 if x>30000 else 0)
df[[‘annual_income’,’income_category’]].head()

annual_income income_category
0 31000.0 1
1 15000.0 0
2 35000.0 1
3 20000.0 0
4 23000.0 0

Data Exploration¶
Select 4 data columns for visualizing:¶

viz = df[[‘cardspent’,’debtinc’,’carditems’,’commutetime’]]
viz.head()

cardspent debtinc carditems commutetime
0 81.66 11.1 5 22.0
1 42.60 18.6 5 29.0
2 184.22 9.9 9 24.0
3 340.99 5.7 17 38.0
4 255.10 1.7 8 32.0

Compute descriptive statistics for the data:¶

viz.describe()

cardspent debtinc carditems commutetime
count 5000.000000 5000.000000 5000.00000 4998.000000
mean 339.635878 9.957800 10.19920 25.346739
std 248.382982 6.423173 3.39279 5.890674
min 0.000000 0.000000 0.00000 7.000000
25% 184.860000 5.175000 8.00000 21.000000
50% 278.655000 8.800000 10.00000 25.000000
75% 422.402500 13.500000 12.00000 29.000000
max 3926.410000 43.100000 23.00000 48.000000

Drop NaN (Not-a-Number) observations:

df[[‘commutetime’]].dropna().count()

commutetime 4998
dtype: int64

Print observations with NaN commutetime:

print( df[np.isnan(df[“commutetime”])] )

custid gender age age_cat debtinc card carditems \
965 3622-JHDLVP-V1E Female 48 35-49 6.5 Discover 12
2734 0860-BRGALK-LLR Female 68 >65 17.3 Other 8

cardspent cardtype creddebt … region ed_cat ed_years \
965 261.91 Platinum 2.25 … Zone 1 College degree 19
2734 178.75 Platinum 1.08 … Zone 5 Some college 15

job_cat employ_years emp_cat \
965 Service 12 11 to 15
2734 Operation, Fabrication, General Labor 20 More than 15

retire annual_income inc_cat income_category
965 No 121000.0 $75 – $124 1
2734 Yes 23000.0 Under $25 0

[2 rows x 31 columns]

Visualize data:¶

viz.hist()
plt.tight_layout()
plt.show()

df[[‘cardspent’]].hist()
plt.show()

df[[‘commutetime’]].hist()
plt.show()

Confidence Intervals¶
For computing confidence intervals and performing simple statistical tests, we will use the stats sub-module of scipy:

from scipy import stats

Confidence intervals tell us how close we think the mean is to the true value, with a certain level of confidence.

We compute mean mu, standard deviation sigma and the number of observations N in our sample of the debt-to-income ratio:

mu, sigma = np.mean(df[[‘debtinc’]]), np.std(df[[‘debtinc’]])
print (“mean = %G, st. dev = %g” % (mu, sigma))

mean = 9.9578, st. dev = 6.42253

N = len(df[[‘debtinc’]])

The 95% confidence interval for the mean of N draws from a Normal distribution with mean mu and standard deviation sigma is

conf_int = stats.norm.interval( 0.95, loc = mu, scale = sigma/np.sqrt(N) )

(array([9.7797798]), array([10.1358202]))

print (“95%% confidence interval for the mean of debt to income ratio = [%g %g]” % (conf_int[0], conf_int[1]))

95% confidence interval for the mean of debt to income ratio = [9.77978 10.1358]

type(conf_int)

Statistical Tests¶

Select columns by name:

adf=df[[‘gender’,’cardspent’,’debtinc’]]
print(adf[‘gender’])

0 Female
1 Male
2 Female
3 Male
4 Male
4995 Male
4996 Male
4997 Female
4998 Female
4999 Female
Name: gender, Length: 5000, dtype: object

Compute means for cardspent and debtinc for the male and female populations:

gender_data = adf.groupby(‘gender’)
print (gender_data.mean())

cardspent debtinc
Female 323.343489 9.985221
Male 356.606840 9.929236

Compute mean for cardspent for female population only:

adf[adf[‘gender’] == ‘Female’][‘cardspent’].mean()

323.34348882791136

We have seen above that the mean cardspent and debtinc in the male and female populations were different. To test if this is significant, we do a 2-sample t-test with scipy.stats.ttest_ind():

female_card = adf[adf[‘gender’] == ‘Female’][‘cardspent’]
male_card = adf[adf[‘gender’] == ‘Male’][‘cardspent’]
tc, pc = stats.ttest_ind(female_card, male_card)
print (“t-test: t = %g p = %g” % (tc, pc))

t-test: t = -4.74396 p = 2.15418e-06

In the case of amount spent on primary credit card, we conclude that men tend to charge more on their primary card (p-value = 2e-6 < 0.05, statistically significant). female_debt = adf[adf['gender'] == 'Female']['debtinc'] male_debt = adf[adf['gender'] == 'Male']['debtinc'] td, pd = stats.ttest_ind(female_debt, male_debt) print ("t-test: t = %g p = %g" % (td, pd)) t-test: t = 0.308069 p = 0.758043 In the case of debt-to-income ratio, we conclude that there is no significant difference between men and women (p-value = 0.758 > 0.05, not statistically significant).

Plot Data¶
Plot statistical measures for amounts spent on primary credit card¶
Use boxplot to compare medians, 25% and 75% percentiles, 12.5% and 87.5% percentiles:¶

adf.boxplot(column=’cardspent’, by=’gender’, grid=False, showfliers=False)
plt.tight_layout()
plt.show()

Plot observations with boxplot:¶

gend = list([‘Female’, ‘Male’])
for i in [1,2]:
y = adf.cardspent[adf.gender==gend[i-1]].dropna()
# Add some random “jitter” to the x-axis
x = np.random.normal(i, 0.04, size=len(y))
plt.plot(x, y, ‘r.’, alpha=0.2)
plt.boxplot([female_card,male_card],labels=gend)
plt.ylabel(“cardspent”)
plt.ylim((-50,850))
plt.show()

Plot age vs. income data to find some interesting relationships.¶

plt.scatter(df.age, df.annual_income)
plt.xlabel(“Age”)
plt.ylabel(“Income”)
plt.show()

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