This presentation will provide you with a comprehensive overview of the predictive modeling development life cycle. You will learn about the different stages involved in the process, as well as the importance of each stage. You will also learn tips on how to improve the success of your predictive modeling projects.

1. PREDICTIVE MODELING
DEVELOPMENT LIFE
CYCLE
BA303: PREDICTIVE MODELLING
Submitted by:
RAVI VERHWANI
(2K20/BAE/111)

2. PREDICTIVE MODELLING
Predictive analytics is leveraged to create
predictions about unknown future actions. It
uses numerous techniques, such as statistical
algorithms, data mining, statistics, modeling,
machine learning, and AI, to evaluate current
data and make forecasts about the future. It
aims to identify the possibility of future results
based on the available historical data.
Meaning

3. Phone Number
6 STEPS TO PREDICTIVE ANALYSIS

4. IDENTIFYING A
PROBLEM AND
DEFINING A
PROJECT
Understanding the industrial problem more deeply is the
first stage in building a model. In business, a problem
doesn't arise until a client runs into a problem while using
the services.
We must properly establish the project objectives in order
to determine the problem's intent and the prediction aim. In
order to move forward with an analytical approach, we
must first identify the challenges. Keep in mind that
superior outcomes always require a deeper comprehension
of the issue.
For instance, Citi Bank wishes to establish a consistent
system for approving or rejecting credit card applications
from its numerous daily applicants.

5. COLLECTION
OF DATA
After determining the issue facing the
company, the next step entails gathering
pertinent data from customers, either
through secondary sources like data
already held by the client or through
primary sources.
Depending on the nature of the objective
issue facing the company and its cost-
effectiveness.

6. DATA
PREPARATION AND
ANALYSIS
Preparing the data for predictive modelling
after data collection is crucial since it may
contain null values, missing data, and errors.
Secondly, divergent datasets with different
formats may exist and need to be converted
to the same format before joining the data.
Finally, we must identify independent and
dependent variables. Exploratory data
analysis techniques such as graphs and
statistical tools can be used to find trends
and confirm data assumptions.

7. FEATURE
SELECTION
The variables are reduced
based on the correlation
between two input variables.
With the help of statistical
tools like correlation, the
variables are reduced.
In order to improve the
model's effectiveness and
cost-effectiveness, feature
selection can be described
as the reduction of input
random variables.

8. The following crucial step entails
developing a model using different
methods, such as linear regression or
lasso regression, validating the
presumptions made, training the
model using training data, and then
testing the model once more using
test data to determine whether it is
functioning properly.
By dividing the population's data into
training and testing sets, the data for
training and testing are obtained.
ROC can be used to test the accuracy.
MODEL
BUILDING

9. MODEL DEPLOYMENT
AND ENHANCEMENT
ONCE THE MODEL HAS UNDERGONE
TRAINING AND TESTING, IT IS PUT TO USE IN
A REAL-WORLD SETTING TO ASSIST
BUSINESSES IN MAKING DECISIONS AND, IF
NECESSARY, TO FURTHER REFINE THE MODEL
BASED ON THE DEMANDS AND PURPOSES
FOR WHICH IT IS BEING DEVELOPED.