This document provides an overview of predictive analytics, including: - Predictive analytics uses historical data and machine learning techniques to predict future outcomes. It focuses on forecasting rather than just describing past events. - Common predictive analytics applications include customer churn prediction, demand forecasting, risk assessment, and equipment maintenance scheduling. - There are two main types of predictive models: logic-driven models based on known relationships between variables, and data-driven models using statistics and machine learning. - The predictive analytics process involves collecting and cleaning data, selecting a modeling technique, building and validating the model, and deploying it to make predictions.

1. UNIT IV PREDICTIVE ANALYTICS 9
 Artificial Intelligence
 Introduction to Predictive analytics
 Logic and Data Driven Models
 Predictive Analysis Modeling and procedure
 Data Mining for Predictive analytics.
 Analysis of Predictive analytics

2. What is artificial intelligence (AI)?
Artificial intelligence is the simulation of human intelligence processes by
machines, especially computer systems. Specific applications of AI
include expert systems, natural language processing, speech recognition
and machine vision.
how does it works:
AI systems work by ingesting large amounts of labeled training data, analyzing
the data for correlations and patterns, and using these patterns to make
predictions about future states. In this way, a chatbot that is fed examples of
text can learn to generate lifelike exchanges with people, or an image
recognition tool can learn to identify and describe objects in images by
reviewing millions of examples. New, rapidly improving generative
AI techniques can create realistic text, images, music and other media.

3. AI programming focuses on cognitive skills that include the following:
Learning. This aspect of AI programming focuses on acquiring data and creating
rules for how to turn it into actionable information. The rules, which are
called algorithms, provide computing devices with step-by-step instructions for
how to complete a specific task.
Reasoning. This aspect of AI programming focuses on choosing the right
algorithm to reach a desired outcome.
Self-correction. This aspect of AI programming is designed to continually fine-
tune algorithms and ensure they provide the most accurate results possible.
Creativity. This aspect of AI uses neural networks, rules-based systems,
statistical methods and other AI techniques to generate new images, new text,
new music and new ideas.

4. Artificial intelligence applications
There are numerous, real-world applications of AI systems today. Below are some of the
most common use cases:
Speech recognition: It is also known as automatic speech recognition (ASR), computer
speech recognition, or speech-to-text, and it is a capability which uses natural language
processing (NLP) to process human speech into a written format. Many mobile devices
incorporate speech recognition into their systems to conduct voice search—e.g. Siri—or
provide more accessibility around texting.
Customer service: Online virtual agents are replacing human agents along the customer
journey. They answer frequently asked questions (FAQs) around topics, like shipping, or
provide personalized advice, cross-selling products or suggesting sizes for users,
changing the way we think about customer engagement across websites and social
media platforms. Examples include messaging bots on e-commerce sites with virtual
agents, messaging apps, such as Slack and Facebook Messenger, and tasks usually done
by virtual assistants and voice assistants.

5. Computer vision: This AI technology enables computers and systems to derive
meaningful information from digital images, videos and other visual inputs, and
based on those inputs, it can take action. This ability to provide
recommendations distinguishes it from image recognition tasks. Powered by
convolutional neural networks, computer vision has applications within photo
tagging in social media, radiology imaging in healthcare, and self-driving cars
within the automotive industry.
Recommendation engines: Using past consumption behavior data, AI
algorithms can help to discover data trends that can be used to develop more
effective cross-selling strategies. This is used to make relevant add-on
recommendations to customers during the checkout process for online
retailers.
Automated stock trading: Designed to optimize stock portfolios, AI-driven high-
frequency trading platforms make thousands or even millions of trades per day
without human intervention.

9. What is Predictive Analytics?
Predictive analytics is a significant analytical approach used by many firms to assess risk,
forecast future business trends, and predict when maintenance is required. Data
scientists use historical data as their source and utilize various regression
models and machine learning techniques to detect patterns and trends in the data.
The basic goal of predictive analytics is to forecast what will happen in the future with a
high degree of certainty. This distinguishes predictive analytics from descriptive analytics,
which assists analysts in analyzing what has previously occurred, and prescriptive analytics,
which uses optimization techniques to detect optimal solutions to address the trends
revealed by predictive analytics.

10. Examples of Predictive Analytics
Customer Service
Businesses may better estimate demand by utilizing advanced and effective analytics and
business intelligence. Consider a hotel company that wants to estimate how many people
will stay in a certain area this weekend so that they can guarantee they have adequate
employees and resources to meet demand.
Higher Education
Predictive analytics applications in higher education include enrollment management,
fundraising, recruiting, and retention. Predictive analytics offers a significant advantage in
each of these areas by offering intelligent insights that would otherwise be neglected.
A prediction algorithm can rate each student and tell administrators ways to serve students
during the duration of their enrollment using data from a student's high school years.
Models can give crucial information to fundraisers regarding the optimal times and
strategies for reaching out to prospective and current donors.

11. Forecasting is an important concern in manufacturing because it guarantees that
resources in a supply chain are used optimally. Inventory management and the shop floor,
for example, are critical spokes of the supply chain wheel that require accurate forecasts
to function.
Supply Chain
Predictive modeling is frequently used to clean and improve the data utilized for such
estimates. Modeling guarantees that additional data, including data from customer-facing
activities, may be consumed by the system, resulting in a more accurate prediction.
Insurance
Insurance firms evaluate policy applicants to assess the chance of having to pay out for a
future claim based on the existing risk pool of comparable policyholders, as well as
previous occurrences that resulted in payments. Actuaries frequently utilize models that
compare attributes to data about previous policyholders and claims.

12. Software Testing
Predictive analytics can help you enhance your operations throughout the full software
testing life cycle.
Predictive analytics can assess your clients' moods by researching social media and
spotting trends, allowing you to anticipate any reaction before it occurs.

13. Predictive Analytics process

15. Logic-Driven Model
It leverages statistics to predict outcomes. Most often the event one wants to predict is in
the future, but predictive modeling can be applied to any type of unknown event,
regardless of when it occurred. For example, predictive models are often used to detect
crimes and identify suspects, after the crime has taken place.
In many cases the model is chosen on the basis of detection theory to try to guess the
probability of an outcome given a set amount of input data, for example given an email
determining how likely that it is spam.
A logic-driven is based on experience, knowledge and logical relationships of variable and
constants connected to the desired performance outcome. To help conceptualize the
relationships inherent in a system, diagramming methods are useful.
Cause and effect diagram enables a user to hypothesize relationships between potential
causes and of an outcome.
Influence diagram are another tool to conceptualize relationships with business
performance relationships.

16. Assuming the average lifetime of a customer (time for which a consumer
remains a customer) W 1/.3 = 3.33 years. So, the average gross profit for a
typical customer turns out to be 12000 × 3.33 = ₹39,960.
Armed with all the above details, we can logically arrive at a conclusion
and can derive the following model for the above problem statement:
Economic Value of each Customer (V) = (R × F × M)/D
Where,
R = Revenue generated per customer
F = Frequency of visits per year
M = Profit margin
D = Defection rate (Non-returning customers each year)
Example –

17. Predictive Analysis Modeling and procedure
Consider these common steps required for predictive modeling:
 Collect data relevant to your target of analysis.
 Organize data into a single dataset.
 Clean your data to avoid a misleading model.
 Create new, useful variables to understand your records.
 Choose a methodology/algorithm.
 Build the model.
predictive modeling is a statistical technique using machine learning and data mining to
predict and forecast likely future outcomes with the aid of historical and existing data.
It works by analyzing current and historical data and projecting what it learns on a
model generated to forecast likely outcomes.

18. The top five predictive analytics models are:
Classification model: Considered the simplest model, it categorizes data for simple and
direct query response. An example use case would be to answer the question “Is this a
fraudulent transaction?”
Clustering model: This model nests data together by common attributes. It works by
grouping things or people with shared characteristics or behaviors and plans strategies
for each group at a larger scale.
Forecast model: This is a very popular model, and it works on anything with a numerical
value based on learning from historical data. For example, in answering how much
lettuce a restaurant should order next week or how many calls a customer support
agent should be able to handle per day or week, the system looks back to historical
data.
Outliers model: This model works by analyzing abnormal or outlying data points. For
example, a bank might use an outlier model to identify fraud by asking whether a
transaction is outside of the customer’s normal buying habits or whether an expense in

22. Data Mining for Predictive analytics

23. Predictive analysis is divided into two main categories:
Descriptive analysis: Descriptive analysis is the process of summarizing and describing
data, including identifying patterns and relationships.
Prescriptive analysis: Prescriptive analysis, on the other hand, is the process of using
data and algorithms to make predictions and recommendations about future
outcomes.

24. How do you Analyse predictive analysis?
Follow these four general steps for implementing a predictive analytics practice in your
organization:
 Identify the business objective. ...
 Determine the datasets. ...
 Create processes for sharing and using insights. ...
 Choose the right software solutions.
What are the stages of predictive analysis?
Five key phases in the predictive analytics process cycle require various types of
expertise: Define the requirements, explore the data, develop the model, deploy the
model and validate the results

26. Thank you