The main target of this project is that it enables the telemarketing team to prioritize targeting for term loan marketing program by adopting a data-driven approach in Machine learning.

1. Telemarketing
Prediction:
Project Session
Phase 1 : Problem Solving Approach
July
2020
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2. Problem
Statement
Enable the telemarketing team to
prioritize targeting for term loan
marketing program – adopt a
data driven approach
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3. Data Source and Objective
▪ This dataset is based on "Bank Marketing" UCI
dataset
▪ The full description along with dataset is available
here :
http://archive.ics.uci.edu/ml/datasets/Bank+Marketi
ng
▪ This dataset is enriched with a few social and
economic attributes
▪ Due to confidentiality clauses all attributes are
not mentioned
▪ The binary classification goal is to predict if the
client will subscribe a bank term deposit
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4. Data Description
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Variable Description
Age Age of Customer
Job
Type of Job (Categorical : “admin”, ”blue-collar”, “entrepreneur”, “housemaid”, ”management”, "retired","self-
employed","services","student","technician","unemployed","unknown")
Marital marital status(categorical:"divorced","married","single","unknown")
education (categorical: “basic.4y”, “basic.6y”, “basic.9y”, “high.school”, “illiterate”, “professional.course”, “university.degree”, “unknown”)
default default: has credit in default? (categorical: “no”, “yes”, “unknown”)
housing housing: has housing loan? (categorical: “no”, “yes”, “unknown”)
loan loan: has personal loan? (categorical: “no”, “yes”, “unknown”)
contact contact: contact communication type (categorical: “cellular”, “telephone”)
month month: last contact month of year (categorical: “jan”, “feb”, “mar”, …, “nov”, “dec”)
day_of_week day_of_week: last contact day of the week (categorical: “mon”, “tue”, “wed”, “thu”, “fri”)
duration duration: last contact duration, in seconds (numeric).
campaign campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)
pdays
pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not
previously contacted)
previous previous: number of contacts performed before this campaign and for this client (numeric)
poutcome poutcome: outcome of the previous marketing campaign (categorical: “failure”, “nonexistent”,“success”)
emp.var.rate emp.var.rate: employment variation rate — (numeric)
cons.price.idx cons.price.idx: consumer price index — (numeric)
cons.conf.idx cons.conf.idx: consumer confidence index — (numeric)
euribor3m euribor3m: euribor 3 month rate — (numeric)
nr.employed nr.employed: number of employees — (numeric)
y target variable - has the client subscribed to term deposit (1/0) Visit: Learnbay.co

5. Exploratory Data
Analysis
Data Understanding – Univariate
Analysis
▪ How well populated is the data?
▪ How much variation is there in the variables given to
you?
▪ What are the unique levels for the categorical variables
▪ What is the proportion of missing data for the given raw
variables? Discard variables that are more than 25%
missing in values
▪ Missing Value Imputation Methods : Mean for Numeric
and Mode for Categorical 5
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6. Bi-Variate Plots
Visualizations to reveal Bi-Variate
data patterns and relationships
▪ Can you spot a concentration of higher than average
proportion of term deposit clients with a feature?
▪ What can be an appropriate grouping logic of
different features, based on linear trend of term
deposit rates?
▪ Are there extreme values in the predictor variables?
How do we decide the capping and flooring points
for features
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7. Insights from Bi-Variate Plots
Based on the nature of the Bi-Variate Plots , we determine optimal predictors and
identify variable interactions.
▪ Cross Tabulate categorical variables with respect to
term deposit rates
▪ Within the cross tabulation are there segments
where the term deposit rates are higher than
average?
▪ Can they be combined to identify the interactions
that can be strong discriminators?
Optimal Features
▪ Based on discrimination of Term Deposit rates , we
can which features are good predictors
▪ Capturing an example below
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Interaction Variables
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8. Classification Methodologies to Consider
Bi-directional approach : Strong classifier algorithms (Statistical and ML Based)
can be tested and results compared for final deployment
▪ Leverage Linearized Features to satisfy
assumptions of Logistic Regression
▪ LR will estimate the likelihood of the event and
utilize the link function (Logit) for computation
▪ Flexibility to create custom target segment based
on predicted probability for each client
Tree Based ML Algorithm
▪ Develops a tree like structure across parent and child
nodes
▪ Generates a set of rules that can be visually
interpreted and readily deployed for decision making
▪ Based on classification capabilities, strategies can be
designed on the ML Tree Rules to derive optimal
business benefit
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Binomial LR Algorithm
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9. A bit about both Methods
▪ Dependent Variable – Dichotomous from Binomial
Distribution
▪ Relates Log of Odds to a Linear Combination of
Predictors
▪ Final model have statistically significant predictors
Tree Based ML Algorithm
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Binomial LR Algorithm
Predicted Probabilities
Describe a Sigmoidal
Curve
▪ Dependent Variable – Dichotomous from Binomial
Distribution
▪ The goal is to form child nodes so that the node
impurity is reduced
▪ The higher the difference in impurity between
parent and child nodes, the better the split.
▪ Example : Class with p(event)=0.5 is most impure
while Class with p(event)=100 is the most pure
Impurity Measures
for C Classes in
Target Variable
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10. How to
Measure
Classification
Performance
Confusion Matrix
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• Accuracy
• Precision
• Recall
F Score
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11. How to enable strategic prioritization
Achieve Campaign Optimization through Profiling and Priority Ordering
▪ Develop Target profiles
▪ Utilize features that helps build up custom
audiences
Prioritization
▪ Develop target segments within every group or
profiles
▪ Attach a priority order for the Telemarketing team to
leverage
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Characterization
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12. THANK
YOU!
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