The document discusses the steps in an AI project cycle which includes problem scoping, data acquisition, data exploration, modelling, and evaluation. It provides examples of each step, such as identifying a problem in problem scoping, collecting reliable data from various sources in data acquisition, arranging data in tables and charts for better understanding in data exploration, creating models from visualized data in modelling, and testing model performance in evaluation.

1. A.I Project Cycle
CLASS IX

2. What is A.I project
cycle?
• It is a step-by-step process that a
person should follow to develop an
AI Project to solve a problem.
• Let us take some daily examples as project,
requiring steps to solve the problem.
• Making Tea
1.Boil Water .
2.Put Tea Powder
3.Put Milk

3. • It mainly has 5
ordered stages
which distribute
the entire
development in
specific and clear
steps:

4. • PROBLEM SCOPING:
• Identifying a problem and having a vision to solve it, is called Problem Scoping. Scoping a problem is not that easy as
we need to have a deeper understanding so that the picture becomes clearer while we are working to solve it.
• Problem scoping is the process by which student designers “figure out” the problem that they need to solve. Students identify
the key elements or factors to which they need to attend, and also consider the context of the problem.
• DATA ACQUISITION:
• Data Acquisition is the process of collecting accurate and reliable data to work with. Data acquisition is the second step in the
project cycle, we should ensure the data collected is collected from authentic and reliable sources for effective Decision Making.
• It is the process of digitizing data from the world around us so it can be displayed, analyzed, and stored in a computer.
• DATA EXPLORATION:
• Data exploration is the first step of data analysis used to explore and visualize data to uncover insights from the start or
identify areas or patterns to dig into more. Data exploration is a critical step in Artificial Intelligence and Machine
Learning. With data exploration, analysts attempt to find patterns and details in large pools of data.
• MODELLING:
• Modelling is the fourth stage of the AI Project Cycle, which deals with creating models from the data. Modelling is the
process in which different models based on the visualized data can be created and even checked for the advantages
and disadvantages of the model.
• EVALUATION:
• Evaluation is the last stage of the AI project cycle123. It is the process of understanding the reliability and performance of
an AI model45. After a model has been created and trained, it must be thoroughly tested to determine its efficiency and
performance2. Evaluation is done by checking the performance of the AI model against testing data with the correct
outcome5

8. Ques: Now, it’s your turn to describe
what you have learnt. Explain the
concept of AI project cycle with the
help of a suitable example.

9. i. Problem
Scoping:
• Problem Scoping is the first stage of the AI project cycle. In this
stage of AI development, problems will be identified.
• In AI project cycle everything will be failed if problem scoping is
failed or without appropriate problem scoping. Incorrect
problem scoping also leads to failure of the project as well.
The 4Ws of Problem Scoping:
The 4Ws are very helpful in problem scoping. They are:
1.Who? – Refers that who is facing a problem and who are the
stakeholders of the problem
2.What? – Refers to what is the problem and how you know
about the problem
3.Where? – It is related to the context or situation or location of
the problem
4.Why? – Refers to why we need to solve the problem and what
are the benefits to the stakeholders after solving the problem
• The outcome of problem scoping in ai is the problem
statement template.

10. The
problem
statement
template
• When the above 4Ws are completely filled you need to
prepare a summary of these 4Ws. This summary is known
as the problem statement template. This template
explains all the key points in a single template. So, if the
same problem arises in the future this statement helps to
resolve it easily.
• Who?
• The “Who” block helps you in analyzing the people getting
affected directly or indirectly due to it. Under this, you find out
who the ‘Stakeholders’ to this problem are and what you know
about them. Stakeholders are the people who face this
problem and would be benefitted with the solution. Let us fill
the “Who” canvas:
• Who are the stakeholders?
• What do you know about them?

11. • What?
• Under the “What” block, you need to look into what you have on hand. At this stage, you need to determine the nature of the problem.
What is the problem and how do you know that it is a problem? Under this block, you also gather evidence to prove that the problem
you have selected actually exists. Newspaper articles, Media, announcements, etc. are some examples.
• What is the problem?
• How do you know that it is a problem?
• Where?
• Now that you know who is associated with the problem and what the problem actually is; you need to focus on the
context/situation/location of the problem. This block will help you look into the situation in which the problem arises, the context of it,
and the locations where it is prominent.
• Let us fill the “Where” canvas!
• What is the context/ situation the stakeholders experience the problem?
• Where is the problem located?
• Why?
• You have finally listed down all the major elements that affect the problem directly. Now it is convenient to understand who the people
that would be benefitted by the solution are; what is to be solved; and where will the solution be deployed. These three canvases now
become the base of why you want to solve this problem. Thus, in the “Why” canvas, think about the benefits which the stakeholders
would get from the solution and how would it benefit them as well as the society.
• Let us fill the “Why” canvas!
• Why will this solution be of value to the stakeholders?
• How will the solution improve their situation?

12. Who Stakeholders
Farmers, Fertilizer Producers,
Labours, Tractor Companies
What The problem, Issue, Need
Determine what will a good
time for seeding or crop
harvesting?
When Context/Situation
Decide the mature age for the
crop and determine its time
Ideal Solution Benefits
Take the crop on time and
supply against market demand
on time
Suppose we have selected the theme
of Agriculture

13. ii. DATA ACQUISITION:
• Data: Data refers to the raw facts,
figures, information, or statistics.
• Acquisition: Acquisition refers to
acquiring data for the project.
• So, Data Acquisition means
Acquiring Data needed to solve the
problem.
• DATA MAY BE THE PROPERTY
OF SOMEONE ELSE, AND THE
USE OF THAT DATA WITHOUT
THEIR PERMISSION IS NOT
ACCEPTABLE.
• But there are some sources from
which we can collect data, no
hassle whatsoever. Let’s Take a
Look:

14. Types of data:
• Primary Data: Primary data is the
kind of data that is collected
directly from the data source. It is
real time data and is mostly
collected when needed and not
stored.
• Secondary Data: Secondary data
is the data that has been collected
in the past by someone else and
made available for others to use.
Secondary data is usually easily
accessible to researchers and
individuals because they are

15. • Data can be a piece of information or
facts and statistics collected together
for reference or analysis.
• Whenever we want an AI project to
be able to predict an output, we
need to train it first using data.
• For example, If you want to make an
Artificially Intelligent system which
can predict the salary of any
employee based on his previous
salaries, you would feed the data of
his previous salaries into the
machine. This is the data with which
the machine can be trained. Now,
once it is ready, it will predict his next
salary efficiently. The previous salary
data here is known as Training Data
while the next salary prediction data
set is known as the Testing Data.

16. • For better efficiency of an AI project,
the Training data needs to be relevant
and authentic.
• In the previous example, if the training
data was not of the previous salaries
but of his expenses, the machine would
not have predicted his next salary
correctly since the whole training went
wrong.
• Similarly, if the previous salary data was
not authentic, that is, it was not correct,
then too the prediction could have gone
wrong.
• Hence…. For any AI project to be
efficient, the training data should be
authentic and relevant to the problem
statement scoped.

17. • There could be various ways in which you collect data.
• Sometimes, you use the internet and try to acquire data for your project
from some random websites. Such data might not be authentic as its
accuracy cannot be proved. Due to this, it becomes necessary to find a
reliable source of data from where some authentic information can be
taken. At the same time, we should keep in mind that the data which we
collect is open-sourced and not someone’s property. Extracting private
data can be an offence. One of the most reliable and authentic sources of
information, are the open-sourced websites hosted by the government.
These government portals have general information collected in suitable
format which can be downloaded and used wisely. Some of the open-
sourced Govt. portals are: data.gov.in, india.gov.in

18. System Maps:
• Components of Water Cycle:

19. iii. DATA
EXPLORATION:
• Data Exploration is the process of arranging the gathered data uniformly for a
better understanding.
• Data exploration, also known as exploratory data analysis (EDA)
• Data can be arranged in the form of a table, plotting a chart, or making a
database.
• Data exploration tools make data analysis easier to present and understand
through interactive, visual elements, making it easier to share and communicate
key insights.
• There are two main types of data exploration tools and techniques: manual data
exploration and automated data exploration.

20. • Line Chart: Line charts are resoundingly popular for a range of business use cases
because they demonstrate an overall trend swiftly and concisely, in a way that’s
hard to misinterpret. In particular, they’re good for depicting trends for different
categories over the same period of time, to aid comparison.
• Bar & Column chart: Both the Bar and the Column charts display data using
rectangular bars where the length of the bar is proportional to the data value. Both
charts compare two or more values. However, the difference lies in their
orientation. A bar chart is oriented horizontally, whereas a column chart is oriented
vertically.

21. • Pie charts: A pie chart is a type of a chart that
visually displays data in a circular graph. Pie
charts can be helpful for showing the
relationship of parts to the whole when there
are a small number of levels.
• Tables: Tables are used to organize data that
is too detailed or complicated to be described
adequately in the text, allowing the reader to
quickly see the results.
• Infographics: An infographic example is a
visual representation of information.
Infographics examples include a variety of
elements, such as images, icons, text, charts,
and diagrams to convey messages at a
glance.

22. MODELLING:
• An AI model is a program that has been trained to recognize
patterns using a set of data.
• AI modeling is the process of creating algorithms, also known
as models, that may be educated to produce intelligent
results.
• This is the process of programming code to create a machine
artificially.
• RULE BASED MODEL: Rule Based Approach Refers to the AI
modelling where the relationship or patterns in data are defined
by the developer. The machine follows the rules or instructions
mentioned by the developer, and performs its task accordingly. For
example, suppose you have a dataset comprising of 100 images of
apples and 100 images of bananas. To train your machine, you
feed this data into the machine and label each image as either
apple or banana. Now if you test the machine with the image of an
apple, it will compare the image with the trained data and
according to the labels of trained images, it will identify the test
image as an apple. This is known as Rule based approach. The
rules given to the machine in this example are the labels given to
the machine for each image in the training dataset.

23. Learning based model:
• Refers to the AI modelling where the relationship or
patterns in data are not defined by the developer. In this
approach, random data is fed to the machine, and it is left
on the machine to figure out patterns and trends out of it.
• Generally, this approach is followed when the data is
unlabeled and too random for a human to make sense out
of it. Thus, the machine looks at the data, tries to extract
similar features out of it and clusters same datasets
together. In the end as output, the machine tells us about
the trends which it observed in the training data.
• For example, suppose you have a dataset of 1000 images
of random stray dogs of your area. Now you do not have
any clue as to what trend is being followed in this dataset
as you don’t know their breed, or colour or any other
feature. Thus, you would put this into a learning approach-
based AI machine and the machine would come up with
various patterns it has observed in the features of these
1000 images. It might cluster the data on the basis of
colour, size, fur style, etc. It might also come up with some
very unusual clustering algorithm which you might not
have even thought of

24. Rule based
v/s Learning
based
Approach:

25. Decision Trees:
• A decision tree is a tree-like structure that represents a series of decisions
and their possible consequences.
• .They are an example of a rule based approach. The basic structure of a
Decision Tree starts from the root which the point where the decision tree
starts.
• From there, the tree diverges into multiple directions with the help of arrows
called branches. These branches depict the condition because of which the
tree diverges. In the end, the final decision is where the tree ends. These
decisions are termed as the leaves of the tree. You would realize that this
looks like an upside-down tree.
• A decision tree simply asks a question, and based on the answer (Yes/No), it
further split the tree into subtrees.
• In a Decision tree, there are two nodes, which are the Decision
Node and Leaf Node. Decision nodes are used to make any decision and have
multiple branches, whereas Leaf nodes are the output of those decisions and
do not contain any further branches.
• Why use decision trees?
• Decision Trees usually mimic human thinking ability while making a decision,
so it is easy to understand.
• The logic behind the decision tree can be easily understood because it shows
a tree-like structure.

26. Decision Tree Terminologies
•Root Node: Root node is from where the
decision tree starts. It represents the entire
dataset, which further gets divided into two
or more homogeneous sets.
•Leaf Node: Leaf nodes are the final output
node, and the tree cannot be segregated
further after getting a leaf node.
•Splitting: Splitting is the process of dividing
the decision node/root node into sub-
nodes according to the given conditions.
•Branch/Sub Tree: A tree formed by splitting
the tree.
•Pruning: Pruning is the process of removing
the unwanted branches from the tree.
•Parent/Child node: The root node of the tree
is called the parent node, and other nodes are
called the child nodes.

27. Machine Learning :
• Machine learning is an application of AI.
• Machine learning aims to teach a machine how to perform a specific
task and provide accurate results by identifying
patterns.
• This enables a computer system to continue learning and improving on
its own
based on experience.
• Types of Machine Learning:
• Supervised learning
• It is a type of machine learning that uses labeled data to train machine
learning models. In labeled data, the output is already known. The
model just needs to map the inputs to the respective outputs.
• An example of supervised learning is to train a system that identifies
the image of an animal.
• Example, you can see that we have our trained model that identifies
the picture of a cat.
• Application of Supervised Learning: Weather forecasting, Sales
forecasting

28. • Unsupervised Learning: Unsupervised learning is a type of machine learning that uses unlabeled data to train
machines. Unlabeled data doesn’t have a fixed output variable. The model learns from the data, discovers the
patterns and features in the data, and returns the output.
• Depicted below is an example of an unsupervised learning technique that uses the images of vehicles to classify if
it’s a bus or a truck. The model learns by identifying the parts of a vehicle, such as a length and width of the
vehicle, the front, and rear end covers, roof hoods, the types of wheels used, etc. Based on these features, the
model classifies if the vehicle is a bus or a truck.
• Application: One of the applications of unsupervised learning is customer segmentation.
Based on customer behavior, likes, dislikes, and interests, you can segment and cluster
similar customers into a group.
• Reinforcement Learning: Reinforcement learning follows trial and error methods
to get the desired result. After accomplishing a task, the agent receives an award.
• An example could be to train a dog to catch the ball. If the dog learns to catch a ball,
you give it a reward, such as a biscuit.
• Reinforcement Learning methods do not need any external supervision to train models.
• Reinforcement learning problems are reward-based. For every task or for every step completed, there will be a reward received
by the agent. If the task is not achieved correctly, there will be some penalty added.
• Applications:
• Reinforcement learning algorithms are widely used in the gaming industries to build games. It is also used to train robots to do
human tasks.

29. Deep Learning:
• Deep learning is part of a broader family of machine learning
methods, which is based on artificial neural networks with
representation learning.
• The adjective "deep" in deep learning refers to the use of multiple
layers in the network.
• Methods used can be either supervised, semi-supervised or
unsupervised.
• Using deep learning computers process data in a way that is
inspired by the human brain.
• Deep learning models can recognize complex patterns in pictures,
text, sounds, and other data to produce accurate insights and
predictions.