Topic : Predict if Client will subscribe to direct marketing campaign for a banking institution

Summary : The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.

## Y(has the client subscribed a term deposit?) (binary: 'yes','no')¶

In [52]:
df_y = df['y'].value_counts()
print 'Percentage of Y=yes:',(df_y[1] / float(df_y[0] + df_y[1])) * 100

Percentage of Y=yes: 11.2654171118

In [53]:
sns.countplot(df['y'])
plt.show()


# Take Aways:¶

1. We can clearly see that the target variable(subscribed) is imbalanced. We must deal with this imbalance during model building

## Month¶

Last Contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')

In [4]:
#Distribution of variable month
plt.figure(figsize=(15,5))

plt.subplot(1,2,1)
sns.countplot(df['month'])
plt.title('Bar Graph showing distribution of Calls made across various months')

plt.subplot(1,2,2)
sns.countplot(x="month", hue="y", data=df);
plt.title('Graph showing distribution of Calls made along with Convertion results across various months')

plt.show()

In [5]:
df_months = pd.crosstab(index=df['month'],columns=df['y'])
df_months['percentage(yes)'] = (df_months['yes'] / (df_months['yes'] + df_months['no'])) * 100

Out[5]:
y no yes percentage(yes)
month
apr 2093 539 20.478723
aug 5523 655 10.602137
dec 93 89 48.901099
jul 6525 649 9.046557
jun 4759 559 10.511470

# Take Aways:¶

1. We can clearly see that the users are usually contacted more in May-June-July-Aug
2. Having said that the outcome(Person subscribing) really is not dependent on the month they were last called in

## Day of week¶

Last contact day of the week (categorical: 'mon','tue','wed','thu','fri')

In [6]:
plt.figure(figsize=(15,5))

plt.subplot(1,2,1)
sns.countplot(df['day_of_week'])
plt.title('Distribution of Calls made across various days of the week')

plt.subplot(1,2,2)
sns.countplot(x="day_of_week", hue="y", data=df);
plt.title('Distribution of Calls made along with Convertion results across various days of the week')

plt.show()

In [7]:
df_days = pd.crosstab(index=df['day_of_week'],columns=df['y'])
df_days['percentage(yes)'] = (df_days['yes'] / (df_days['yes'] + df_days['no'])) * 100

Out[7]:
y no yes percentage(yes)
day_of_week
fri 6981 846 10.808739
mon 7667 847 9.948320
thu 7578 1045 12.118752
tue 7137 953 11.779975
wed 7185 949 11.667076

# Take Aways:¶

1. There's no trend as such in the days of week as the number of calls made based on day is uniform
2. There's an equal chance/prob of a person subscribing accros every day of the week

## Duration:¶

last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

In [8]:
#Distribution of variable day of week
plt.figure(figsize=(15,10))

plt.subplot(1,2,1)

plt.hist(df['duration'],bins=[0,100,200,300,400,500,1000,1500,2000])
plt.title('Histogram of Call duration')
plt.xlim(0,2500)

plt.subplot(1,2,2)
sns.boxplot(x='y',y='duration',data=df)

plt.title('Distribution of Call duration(in secs) vs Subscribed')

Out[8]:
<matplotlib.text.Text at 0x7f6537149190>
In [9]:
plt.show()

In [10]:
df[df['duration'] == 0]

Out[10]:
age job marital education default housing loan contact month day_of_week ... campaign pdays previous poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed y
6251 39 admin. married high.school no yes no telephone may tue ... 4 999 0 nonexistent 1.1 93.994 -36.4 4.857 5191.0 no
23031 59 management married university.degree no yes no cellular aug tue ... 10 999 0 nonexistent 1.4 93.444 -36.1 4.965 5228.1 no
28063 53 blue-collar divorced high.school no yes no cellular apr fri ... 3 999 0 nonexistent -1.8 93.075 -47.1 1.479 5099.1 no
33015 31 blue-collar married basic.9y no no no cellular may mon ... 2 999 0 nonexistent -1.8 92.893 -46.2 1.299 5099.1 no

4 rows × 21 columns

NOTE: We only have 4 datapoints with duration = 0 which means these people were contacted first time These 4 data points should be removed before model training, as the duration is not known

In [11]:
new_data = df[df['duration'] != 0]
#Let's see if there is still any trend in coversion based on duration after removing duration = 0, data points
plt.figure(figsize=(10,12))
sns.boxplot(x='y',y='duration',data=new_data)
plt.show()


# Take Aways:¶

1. Most number of calls last between 0-200 secs
2. There's a clear trend showing the chances of person subscribing increases once the duration goes above 300 secs

## Campaign¶

Number of contacts performed during this campaign and for this client (numeric, includes last contact)

In [12]:
#Distribution of variable day of week
plt.figure(figsize=(15,10))

plt.subplot(1,2,1)
plt.hist(df['campaign'])
plt.title('Distribution of Calls made during a campaign')

camp_less_than_20 = df[df['campaign'] < 20]
plt.subplot(1,2,2)
sns.countplot(x="campaign", hue="y", data=camp_less_than_20);

plt.title('Distribution of Calls made during a campaign vs Subscribed')

Out[12]:
<matplotlib.text.Text at 0x7f6536ce1b90>
In [13]:
plt.show()

In [14]:
df_campaign = pd.crosstab(index=camp_less_than_20['campaign'],columns=camp_less_than_20['y'])
df_campaign['percentage(yes)'] = (df_campaign['yes'] / (df_campaign['yes'] + df_campaign['no'])) * 100

Out[14]:
y no yes percentage(yes)
campaign
1 15342 2300 13.037071
2 9359 1211 11.456954
3 4767 574 10.747051
4 2402 249 9.392682
5 1479 120 7.504690

# Take Aways:¶

1. Number of the calls performed to a single person during a campaign are between 0-10 on average
2. Even though most of the people are subscribing in the very first call during a campaign, their percentage conversion is quite low. 2k subscribing out of 17k. Conversion percentage is higher 2 to 5. If a person is contacted more than 10 times then the signal is quite clear that he is not going to subscribe

## Age¶

Age of person (numeric)

In [15]:
sns.distplot(df['age'])
plt.show()


# Take Aways:¶

1. The distribution of age looks slightly skewed towards the right side
2. This doesn't mean they are outlier since we saw that their occupation is retired

## Marital¶

Marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)

In [16]:
plt.figure(figsize=(15,6))
plt.subplot(1,2,1)
sns.countplot(df['marital'])
plt.title('Distribution of Marital status of the population')

plt.subplot(1,2,2)
sns.countplot(x="marital", hue="y", data=df)
plt.title('Distribution of Marital status of the population against the target variable')

plt.show()

In [17]:
df_marital = pd.crosstab(index=df['marital'],columns=df['y'])
df_marital['percentage(yes)'] = (df_marital['yes'] / (df_marital['yes'] + df_marital['no'])) * 100
df_marital

Out[17]:
y no yes percentage(yes)
marital
divorced 4136 476 10.320902
married 22396 2532 10.157253
single 9948 1620 14.004149
unknown 68 12 15.000000

# Take Aways:¶

1. Looking at the above plots we can see some folks have marital status as unknown. We should be handling this 'unknowns' if possible.

## poutcome:¶

Outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')

In [18]:
plt.figure(figsize=(15,6))
plt.subplot(1,2,1)
sns.countplot(df['poutcome'])
plt.title('Distribution of outcome of previous marketing campaign')

plt.subplot(1,2,2)
sns.countplot(x="poutcome", hue="y", data=df)
plt.title('Distribution of outcome of previous marketing campaign against the outcome of current campaign')

plt.show()

In [19]:
df_outcome = pd.crosstab(index=df['poutcome'],columns=df['y'])
df_outcome['percentage(yes)'] = (df_outcome['yes'] / (df_outcome['yes'] + df_outcome['no'])) * 100
df_outcome

Out[19]:
y no yes percentage(yes)
poutcome
failure 3647 605 14.228598
nonexistent 32422 3141 8.832213
success 479 894 65.112891

# Take Aways:¶

1. The non-existent ones in the first plot means they were not contacted in the previous campaign
2. In the second plot, we have managed to convert some nonexistent ones(green). These are our new customers for this campaign)
3. Also, we can say that there are some folks who have said no in the previous campaign but we have still managed to convince 14% of them and vice versa.

## Contact:¶

Contact communication type (categorical: 'cellular','telephone')

In [20]:
df_calls = pd.crosstab(index=df['contact'],columns=df['y'])
df_calls['percentage(yes)'] = (df_calls['yes'] / (df_calls['yes'] + df_calls['no'])) * 100
df_calls

Out[20]:
y no yes percentage(yes)
contact
cellular 22291 3853 14.737607
telephone 14257 787 5.231321
In [21]:
plt.figure(figsize=(15,6))
plt.subplot(1,2,1)
sns.countplot(df['contact'])

plt.title('Distribution of communication type')

plt.subplot(1,2,2)
sns.countplot(x="contact", hue="y", data=df)
plt.title('Distribution of outcome of previous marketing campaign against the outcome of current campaign')
plt.show()


# Take Aways:¶

1. More people are called on cell the average age of the poulation is 40 as seen by the the age distribution above
2. It makes more sense in the next campaign to call people on cellphones(if they have both) more since the conversion rate in much higher in that case.
In [ ]:


In [22]:
print df['age'].mean()
print df['age'].median()

40.0240604059
38.0


## Data Cleaning¶

In [23]:
df.loc[(df['marital']  == 'unknown') & (df['age']  >= 40), 'marital'] = 'married'
df.loc[(df['marital']  == 'unknown') & (df['age']  < 40), 'marital'] = 'single'

In [24]:
df_1 = pd.crosstab(index=df['job'],columns=df['education'])
df_1

Out[24]:
education basic.4y basic.6y basic.9y high.school illiterate professional.course university.degree unknown
job
admin. 77 151 499 3329 1 363 5753 249
blue-collar 2318 1426 3623 878 8 453 94 454
entrepreneur 137 71 210 234 2 135 610 57
housemaid 474 77 94 174 1 59 139 42
management 100 85 166 298 0 89 2063 123
retired 597 75 145 276 3 241 285 98
self-employed 93 25 220 118 3 168 765 29
services 132 226 388 2682 0 218 173 150
student 26 13 99 357 0 43 170 167
technician 58 87 384 873 0 3320 1809 212
unemployed 112 34 186 259 0 142 262 19
unknown 52 22 31 37 0 12 45 131
In [25]:
df_unskilled = df[(df.job == 'blue-collar') | (df.job == 'housemaid')]
df_service = df[(df.job == 'admin.') | (df.job == 'services') | (df.job == 'technician')]
df_professional = df[(df.job == 'entrepreneur') | (df.job == 'self-employed') | (df.job == 'management')]
df_student = df[(df.job == 'student')]
df_retired = df[(df.job == 'retired')]
df_unemployed = df[(df.job == 'unemployed')]

In [26]:
df.loc[df_unskilled.index, 'job'] = 'Unskilled'
df.loc[df_service.index, 'job'] = 'Service'
df.loc[df_professional.index, 'job'] = 'Professional'
df.loc[df_student.index, 'job'] = 'Student'
df.loc[df_retired.index, 'job'] = 'Retired'
df.loc[df_unemployed.index, 'job'] = 'Unemployed'

In [27]:
df['job'].unique()

Out[27]:
array(['Unskilled', 'Service', 'Retired', 'Professional', 'Unemployed',
'unknown', 'Student'], dtype=object)
In [28]:
df_job2 = pd.crosstab(index=df_service.job,columns=df_service.education)
df_job2.loc['total'] = df_job2.iloc[:,:].sum()
df_job2

Out[28]:
education basic.4y basic.6y basic.9y high.school illiterate professional.course university.degree unknown
job
admin. 77 151 499 3329 1 363 5753 249
services 132 226 388 2682 0 218 173 150
technician 58 87 384 873 0 3320 1809 212
total 267 464 1271 6884 1 3901 7735 611
In [29]:
df_job1 = pd.crosstab(index=df_unskilled.job,columns=df_unskilled.education)
df_job1.loc['total'] = df_job1.iloc[:,:].sum()
df_job1

Out[29]:
education basic.4y basic.6y basic.9y high.school illiterate professional.course university.degree unknown
job
blue-collar 2318 1426 3623 878 8 453 94 454
housemaid 474 77 94 174 1 59 139 42
total 2792 1503 3717 1052 9 512 233 496
In [30]:
df_edu = pd.crosstab(index=df.job,columns=df.education)
df_edu.loc['total'] = df_edu.iloc[:,:].sum()
df_edu

Out[30]:
education basic.4y basic.6y basic.9y high.school illiterate professional.course university.degree unknown
job
Professional 330 181 596 650 5 392 3438 209
Retired 597 75 145 276 3 241 285 98
Service 267 464 1271 6884 1 3901 7735 611
Student 26 13 99 357 0 43 170 167
Unemployed 112 34 186 259 0 142 262 19
Unskilled 2792 1503 3717 1052 9 512 233 496
unknown 52 22 31 37 0 12 45 131
total 4176 2292 6045 9515 18 5243 12168 1731
In [31]:
df_basic_ed = df[(df.education == 'basic.4y') | (df.education == 'basic.6y') | (df.education == 'illiterate')]
df_mid_ed = df[(df.education == 'basic.9y') | (df.education == 'high.school')]
df_degree_ed = df[(df.education == 'professional.course') | (df.education == 'university.degree')]

In [32]:
df.loc[df_basic_ed.index, 'education'] = 'Basic'
df.loc[df_mid_ed.index, 'education'] = 'Mid'
df.loc[df_degree_ed.index, 'education'] = 'Degree'

In [33]:
df['education'].value_counts()

Out[33]:
Degree     17411
Mid        15560
Basic       6486
unknown     1731
Name: education, dtype: int64
In [34]:
pd.crosstab(index=df.job,columns=df.education)

Out[34]:
education Basic Degree Mid unknown
job
Professional 516 3830 1246 209
Retired 675 526 421 98
Service 732 11636 8155 611
Student 39 213 456 167
Unemployed 146 404 445 19
Unskilled 4304 745 4769 496
unknown 74 57 68 131
In [35]:
drop_index = df[(df.job == 'unknown') & (df.education == 'unknown')]['y'].index
print len(drop_index)

131

In [36]:
df_new = df.drop(drop_index,axis=0)

In [37]:
pd.crosstab(index=df_new.job,columns=df_new.education)

Out[37]:
education Basic Degree Mid unknown
job
Professional 516 3830 1246 209
Retired 675 526 421 98
Service 732 11636 8155 611
Student 39 213 456 167
Unemployed 146 404 445 19
Unskilled 4304 745 4769 496
unknown 74 57 68 0
In [38]:
df1 = df_new[(df_new.job == 'unknown') & (df_new.education == 'Basic')].index.values
df2 = df_new[(df_new.job == 'unknown') & (df_new.education == 'Degree')].index.values
df3 = df_new[(df_new.job == 'unknown') & (df_new.education == 'Mid')].index.values

df_new.loc[df1, 'job'] = 'Unskilled'
df_new.loc[df2, 'job'] = 'Service'
df_new.loc[df3, 'job'] = 'Service'

In [39]:
df_new[(df_new.job == 'unknown') & (df_new.education == 'Basic')]['job'].index.values

Out[39]:
array([], dtype=int64)
In [40]:
pd.crosstab(index=df_new.job,columns=df_new.education)

Out[40]:
education Basic Degree Mid unknown
job
Professional 516 3830 1246 209
Retired 675 526 421 98
Service 732 11693 8223 611
Student 39 213 456 167
Unemployed 146 404 445 19
Unskilled 4378 745 4769 496
In [41]:
df4 = df_new[(df_new.education == 'unknown') & (df_new.job == 'Professional')].index.values
df5 = df_new[(df_new.education == 'unknown') & (df_new.job == 'Retired')].index.values
df6 = df_new[(df_new.education == 'unknown') & (df_new.job == 'Service')].index.values
df7 = df_new[(df_new.education == 'unknown') & (df_new.job == 'Student')].index.values
df8 = df_new[(df_new.education == 'unknown') & (df_new.job == 'Unemployed')].index.values
df9 = df_new[(df_new.education == 'unknown') & (df_new.job == 'Unskilled')].index.values

df_new.loc[df4, 'education'] = 'Degree'
df_new.loc[df5, 'education'] = 'Basic'
df_new.loc[df6, 'education'] = 'Degree'
df_new.loc[df7, 'education'] = 'Mid'
df_new.loc[df8, 'education'] = 'Mid'
df_new.loc[df9, 'education'] = 'Mid'

In [42]:
pd.crosstab(index=df_new.job,columns=df_new.education)

Out[42]:
education Basic Degree Mid
job
Professional 516 4039 1246
Retired 773 526 421
Service 732 12304 8223
Student 39 213 623
Unemployed 146 404 464
Unskilled 4378 745 5265
In [43]:
pd.crosstab(index=df_new.loan,columns=df_new.education)

Out[43]:
education Basic Degree Mid
loan
no 5427 14979 13433
unknown 181 420 387
yes 976 2832 2422
In [44]:
pd.crosstab(index=df_new.loan,columns=df_new.job)

Out[44]:
job Professional Retired Service Student Unemployed Unskilled
loan
no 4814 1436 17455 710 838 8586
unknown 147 44 478 23 27 269
yes 840 240 3326 142 149 1533
In [45]:
df_new.to_csv('modified.csv')

In [ ]: