A project on Data Mining was developed using RapidMiner. In this project, a Portuguese Bank data set was used to implement CRISP-Dm. The insights derived, like how the client call duration affected the chances of the client turning into an investor, preparing questionnaires to answer common questions when pitching, the probability of blue collared employees being more susceptible to enroll for the fixed term, etc. could help increase business for the bank.

1. Data Mining Approach Using Crisp-Dm
for Bank Marketing Success Prediction
Presented by,
Harry Gavli
Saurabh Wadhwane
Sushanti Acharya

2. Agenda
Introduction
Methodology
Business Understanding
Data Understanding
Data Preparation
Modelling
Evaluation
Deployment

3. INTRODUCTION
DATASET
https://arc
hive.ics.uci.
edu/ml/dat
asets/Bank
+Marketing
Our target
variable is ‘y’,
which predicts
the customer
response.

4. METHODOLOGY
CRISP-DM

5. BUSINESS
UNDERSTANDING
Based on the dataset, we tried to
understand the business requirements
Whether the client will subscribe for the term?
Which age group is susceptible to more subscription?
Does marital status affect the chances of client subscription?
Which occupation does most subscribers fall in?
Which clients should be targeted based on their education level?
Are age, marital status, occupation, and education level
interdependent for a client's decision?
Do other/pervious loans affect the client’s decision of
subscription?

6. DATA
UNDERSTANDING
The data obtained for this project is
in csv format. It is a base dataset of a
Portuguese bank.
It contains 20 attributes which are
responsible for the client’s decision
of availing the subscription from the
bank.
The main motive of this project is
to predict the no of client’s that will
subscribe and what factors are
affecting this decision .

7. DATA PREPARATION
The data that was obtained did
not contain any missing values,
but certain outliers were present
in the dataset.
The outliers affected the
accuracy of the project. Thus
we cleaned the data by
removing the outliers.
After removing the outliers the
data was finally ready for
processing in RapidMiner.

8. The modelling process for this bank
dataset was carried out using
RapidMiner.
We performed the modelling process
using an auto model which helped in
the process of pre-processing data to
applying various algorithms to it.
MODELLING

9. Naïve Bayes

10. Generalized Linear Model

11. Logistic Regression

12. Deep Learning

13. Decision Tree

14. Random Forest

15. Gradient Boosted Tree

16. Support Vector Machine

17. EVALUATION

18. MODELACCURACYAND RUNTIME

19. ROC OF THE TESTED MODELS

20. DEPLOYMENT
Based on the mining process, some vital insights were generated.
Increasing the duration of engaging the client in the conversation may help bring in
more clients. Developing a questionnaire will boost the chances of keeping the
client engaged in a conversation, unlike without one.
Since the subscription is for term deposit (which involves investing an amount for
a specific time period without withdrawal) the focus should be on the age group of
to 50, as this age group showed maximum interest in investment.
Targeting the client’s that have previously subscribed in the last campaign period.
Emphasizing on clients belonging to the “blue collar” job category, since they were
the ones who agreed to subscribe.

21. Thank You