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KG COLLEGE OF ENGINEERING & TECHNOLOGY

(Approved by AICTE, New Delhi, Affiliated to JNTUH, Hyderabad) Chilkur (Village), Moinabad (Mandal), R. R Dist, TS-501504

DEPARTMENT OF COMPUTER SCIENCE ENGINEERING

MACHINE LEARNING

LAB MANUAL

Subject Code: CS601PC

Regulations: R 18 JNTUH

Class: III Year B. Tech. CSE I Semester/Semester II

COMPUTER SCIENCE AND ENGINEERING

KG REDDY COLLEGE OF ENGINEERING AND TECHNOLOGY

Affiliated to JNTUH, Chilkur,(V), Moinabad(M) R. R Dist, TS-501504

KG COLLEGE OF ENGINEERING & TECHNOLOGY

(Approved by AICTE, New Delhi, Affiliated to JNTUH, Hyderabad) Chilkur (Village), Moinabad (Mandal), R. R Dist, TS-501504

Page 1

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

VISION AND MISSION OF THE INSTITUTION

VISION

To become self-sustainable institution which is recognized for its new age engineering through

innovative teaching and learning culture, inculcating research and entrepreneurial ecosystem, and

sustainable social impact in the community.

MISSION

To offer undergraduate and post-graduate programs that is supported through industry relevant curriculum and innovative teaching and learning processes that would help students succeed in their professional careers. To provide necessary support structures for students, which will contribute to their personal and professional growth and enable them to become leaders in their respective fields. To provide faculty and students with an ecosystem that fosters research and development through strategic partnerships with government organisations and collaboration with industries. To contribute to the development of the region by using our technological expertise to work with nearby communities and support them in their social and economic growth.

VISION AND MISSION OF CSE DEPARTMENT

VISION

To be recognized as a department of excellence by stimulating a learning environment in which

students and faculty will thrive and grow to achieve their professional, institutional and societal goals.

MISSION

To provide high quality technical education to students that will enable life-long learning and build

expertise in advanced technologies in Computer Science and Engineering. To promote research and development by providing opportunities to solve complex engineering problems in collaboration with industry and government agencies. To encourage professional development of students that will inculcate ethical values and leadership skills while working with the community to address societal issues

ProgramEducationalObjectives(PEOs):

AgraduateoftheComputerScience andEngineeringProgramshould:

KG COLLEGE OF ENGINEERING & TECHNOLOGY

(Approved by AICTE, New Delhi, Affiliated to JNTUH, Hyderabad) Chilkur (Village), Moinabad (Mandal), R. R Dist, TS-501504

Page 2

ProgramEducationalObjective1:(PEO1)

The Graduates will provide solutions to difficult and challenging issues in their profession by applying

computer science and engineering theory and principles.

ProgramEducationalObjective2:(PEO2)

The Graduates have successful careers in computer science and engineering fields or will be able to successfully pursue advanced degrees.

ProgramEducationalObjective3:(PEO3)

The Graduates will communicate effectively, work collaboratively and exhibit high levels of Professionalism, moral and ethical responsibility.

ProgramEducationalObjective4:(PEO4)

The Graduates will develop the ability to understand and analyse Engineering issues in a broader perspective with ethical responsibility towards sustainable development.

ProgramOutcomes(POs):

PO1 Engineeringknowledge:Applytheknowledgeofmathematics,science,engineering PO2 Problemanalysis:Identify,formulate,reviewresearchliterature,andanalyzecomplexengineering problems reaching substantiated conclusions using first principles ofmathematics,natural sciences, andengineeringsciences. PO3 Design/developmentofsolutions:Designsolutionsforcomplexengineeringproblemsanddesign system components or processes that meet the specified needs with appropriateconsideration for the public health and safety, and the cultural, societal, and environmentalconsiderations. PO4 Conduct investigations of complex problems: Use research-based knowledge and researchmethods including design of experiments, analysis and interpretation of data, and synthesis oftheinformation to providevalid conclusions. PO5 Modern tool usage: Create, select, and apply appropriate techniques, resources, and modernengineering and IT tools including prediction and modeling to complex engineering activitieswithan understandingofthelimitations. PO6 The engineer and society: Apply reasoning informed by the contextual knowledge to assesssocietal, health, safety, legal and cultural issues and the consequent responsibilities relevant totheprofessional engineeringpractice.

KG COLLEGE OF ENGINEERING & TECHNOLOGY

(Approved by AICTE, New Delhi, Affiliated to JNTUH, Hyderabad) Chilkur (Village), Moinabad (Mandal), R. R Dist, TS-501504

Page 3

PO7 Environmentand sustainability:Understandtheimpactoftheprofessionalengineering nabledevelopment. PO8 Ethics: Apply ethical principles and commit to professional ethics and responsibilities andnormsoftheengineering practice. PO9 Individualand teamwork:Functioneffectivelyasanindividual,andasa memberorleader PO10 Communication: Communicate effectively on complex engineering activities with theengineering community and with society at large, such as, being able to comprehend iveclearinstructions. PO11 Projectmanagementand finance:Demonstrateknowledgeandunderstandingofthe engineeringandmanagementprinciplesandapply andleaderin ateam, to manageprojects andin multidisciplinaryenvironments. PO12 Life-long learning: Recognize the need for, and have the preparation and ability to engage inindependentandlife-longlearningin thebroadest context oftechnological change.

ProgramSpecificOutcomes(PSOs):

PSO1 Problem Solving Skills Graduate will be able to apply computational techniques and software principles to solve complex engineering problems pertaining to software engineering. PSO2 Professional Skills Graduate will be able to think critically, communicate effectively, and collaborate in teams through participation in co and extra-curricular activities. PSO3 Successful Career Graduates will possess a solid foundation in computer science and engineering that will enable them to grow in their profession and pursue lifelong learning through post-graduation and professional development.

KG COLLEGE OF ENGINEERING & TECHNOLOGY

(Approved by AICTE, New Delhi, Affiliated to JNTUH, Hyderabad) Chilkur (Village), Moinabad (Mandal), R. R Dist, TS-501504

Page 4

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

MACHINE LEARNING LAB

LIST OF EXPERIMENTS

S. No. Name of the Experiment

1. The probability that it is Friday and that a student is absent is 3 %. Since there are 5 school days in a week, the probability that it is Friday is 20 %. What is theprobability that a student is absent given that today is Friday? Apply rule in python to get the result. (Ans: 15%)

2. Extract the data from database using python

3. Implement k-nearest neighbours classification using python

4. Given the following data, which specify classifications for nine ombinations of VAR1 and VAR2 predict a classification for a case where VAR1=0.906 and VAR2=0.606, using the result of k-means clustering with 3 means (i.e., 3 centroids)

VAR1 VAR2 CLASS

1.713 1.586 0

0.180 1.786 1

0.353 1.240 1

0.940 1.566 0

1.486 0.759 1

1.266 1.106 0

1.540 0.419 1

0.459 1.799 1

0.773 0.186 1

5. The following training examples map descriptions of individuals

onto high, medium and low

KG COLLEGE OF ENGINEERING & TECHNOLOGY

(Approved by AICTE, New Delhi, Affiliated to JNTUH, Hyderabad) Chilkur (Village), Moinabad (Mandal), R. R Dist, TS-501504

Page 5

credit-worthiness. medium skiing design single twenties no ->highRisk high golf trading married forties yes ->lowRisk ow speedway transport married thirties yes ->medRisk medium football banking single thirties yes ->lowRisk high flying media married fifties yes ->highRisk ow football security single twenties no ->medRisk medium golf media single thirties yes ->medRisk medium golf transport married forties yes ->lowRisk high skiing banking single thirties yes ->highRisk ow golf unemployed married forties yes ->highRisk Input attributes are (from left to right) income, recreation, job, status, age- group, home-owner. Find the unconditional probability of `golf' and the conditional probability of `single' given `medRisk' in the dataset?

6. Implement linear regression using python.

7. Implement Naïve Bayes theorem to classify the English text

8. Implement an algorithm to demonstrate the significance of genetic

algorithm

KG COLLEGE OF ENGINEERING & TECHNOLOGY

(Approved by AICTE, New Delhi, Affiliated to JNTUH, Hyderabad) Chilkur (Village), Moinabad (Mandal), R. R Dist, TS-501504

Page 6

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

MACHINE LEARNING LAB

INTRODUCTION TO LAB:

Machine Learning is used anywhere from automating mundane tasks to offering intelligent insights,

industries in every sector try to benefit from it. You may already be using a device that utilizes it. For

example, a wearable fitness tracker like Fitbit, or an intelligent home assistant like Google Home. But

there are much more examples of ML in use. Prediction:Machine learning can also be used in the prediction systems. Considering the loan

example, to compute the probability of a fault, the system will need to classify the available data in

groups. Image recognition:Machine learning can be used for face detection in an image as well. There is a separate category for each person in a database of several people. Speech Recognition:It is the translation of spoken words into the text. It is used in voice searches and more. Voice user interfaces include voice dialing, call routing, and appliance control. It can also be used a simple data entry and the preparation of structured documents. Medical diagnoses:ML is trained to recognize cancerous tissues. Financial industry:andtrading:companies use ML in fraud investigations and credit checks.

Types of Machine Learning?

Machine learning can be classified into 3 types of algorithms

1. Supervised Learning

2. Unsupervised Learning

3. Reinforcement Learning

Overview of Supervised Learning Algorithm

In Supervised learning, an AI system is presented with data which is labeled, which means that each data

tagged with the correct label.

KG COLLEGE OF ENGINEERING & TECHNOLOGY

(Approved by AICTE, New Delhi, Affiliated to JNTUH, Hyderabad) Chilkur (Village), Moinabad (Mandal), R. R Dist, TS-501504

Page 7

The goal is to approximate the mapping function so well that when you have new input data (x) that you

can predict the output variables (Y) for that data.

KG COLLEGE OF ENGINEERING & TECHNOLOGY

(Approved by AICTE, New Delhi, Affiliated to JNTUH, Hyderabad) Chilkur (Village), Moinabad (Mandal), R. R Dist, TS-501504

Page 8

This labeled data is used by the training supervised model, this data is used to train the model.

Once it is trained we can test our model by testing it with some test new mails and checking of the model

is able to predict the right output.

Types of Supervised learning

Classification: A classification problem is when the output variable is a category, such as and Regression: A regression problem is when the output variable is a real value, such as or

Overview of Unsupervised Learning Algorithm

In unsupervised learning, an AI system is presented

algorithms act on the data without prior training. The output is dependent upon the coded algorithms.

Subjecting a system to unsupervised learning is one way of testing AI.

Types of Unsupervised learning:

Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.

KG COLLEGE OF ENGINEERING & TECHNOLOGY

(Approved by AICTE, New Delhi, Affiliated to JNTUH, Hyderabad) Chilkur (Village), Moinabad (Mandal), R. R Dist, TS-501504

Page 9

Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.

Overview of Reinforcement Learning

A reinforcement learning algorithm, or agent, learns by interacting with its environment. The agent

receives rewards by performing correctly and penalties for performing incorrectly. The agent learns

without intervention from a human by maximizing its reward and minimizing its penalty. It is a type of

dynamic programming that trains algorithms using a system of reward and punishment.

KG COLLEGE OF ENGINEERING & TECHNOLOGY

(Approved by AICTE, New Delhi, Affiliated to JNTUH, Hyderabad) Chilkur (Village), Moinabad (Mandal), R. R Dist, TS-501504

Page 10

in the above example, we can see that the agent is given 2 options i.e. a path with water or a path with fire.

A reinforcement algorithm works on reward a system i.e. if the agent uses the fire path then the rewards

are subtracted and agent tries to learn that it should avoid the fire path. If it had chosen the water path or

the safe path then some points would have been added to the reward points, the agent then would try to

learn what path is safe and what path

It is basically leveraging the rewards obtained; the agent improves its environment knowledge to select the

next action.

KG COLLEGE OF ENGINEERING & TECHNOLOGY

(Approved by AICTE, New Delhi, Affiliated to JNTUH, Hyderabad) Chilkur (Village), Moinabad (Mandal), R. R Dist, TS-501504

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PROGRAM 1

The probability thatitisFridayandthatastudentisabsentis3%.Sincethereare5school daysinaweek,theprobabilitythatitisFridayis20%.Whatisthe probabilitythatastudent is 15%) AIM: To find the probability that a student is absent given that today is Friday.

DESCRIPTION:

Machine learning is a method of data analysis that automates analytical model building of data set. Using the implemented algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. Naive bayes algorithm is one of the most popular machines learning technique. In this article we will look how to implement

Naive bayes algorithm using python.

ed concepts first, namely, the idea of Conditional Probability, and Rule.

Conditional Probability is just what is the probability that something will happen, given that something

else has already happened.

Let say we have a collection of people. Some of them are singers. They are either male or female. If we

select a random sample, what is the probability that this person is a male? what is the probability that

this person is a male and singer? Conditional Probability is the best option here. We can calculate

probability like,

P(Singer & Male) = P(Male) x P(Singer / Male)

What is Bayes rule ?

together form the sample space S. Let B be any event from the same sample space, such that P(B) > 0. Then, P( Ak | B ) = P( Ak ŀ / P( A1 ŀ + P( A2 ŀ B ) + . . . + P( An ŀ B )

KG COLLEGE OF ENGINEERING & TECHNOLOGY

(Approved by AICTE, New Delhi, Affiliated to JNTUH, Hyderabad) Chilkur (Village), Moinabad (Mandal), R. R Dist, TS-501504

Page 12

What is Bayes classifier?

Naive Bayes classifiers are a family of simple probabilistic classifiers based on applying theorem with strong (naive) independence assumptions between the features in machine learning. Basically we can use above theories and equations for classification problem.

SOURCE CODE:

probAbsentFriday=0.0

3 probFriday=0.2

# bayes Formula # Therefore the result is: print(bayesResult * 100)

Output: 15

1.5.VIVA QUESTIONS & ANSWERS

1. What are Bayesian Networks (BN) ?

Bayesian Network is used to represent the graphical model for probability relationship among a set of

conditional probability. Conditional probability is the

probability of an event happening, given that it has some relationship to one or more other events. For

example, your probability of getting a parking space is connected to the time of day you park, where nutshell, it gives you the actual probability of an event given information about tests.

KG COLLEGE OF ENGINEERING & TECHNOLOGY

(Approved by AICTE, New Delhi, Affiliated to JNTUH, Hyderabad) Chilkur (Village), Moinabad (Mandal), R. R Dist, TS-501504

Page 13

test separate from the event of actually having liver disease. Tests are flawed: just because you have a positive test does not mean you actually have the disease. Many tests have a high false positive rate. Rare events tend to have higher false positive rates than mor real probability that the test has identified the event.

2. Can you give any real time example using disease).

alcoholic. is the test (kind of like a litmus test) for liver disease. A could mean the event has liver Past data tells you that 10% of patients entering your clinic have liver disease. P(A) = 0.10. B could mean the litmus test that is an Five percent of the are alcoholics. P(B) = 0.05. You might also know that among those patients diagnosed with liver disease, 7% are alcoholics.

This is your B|A: the probability that a patient is alcoholic, given that they have liver disease, is 7%.

theorem tells you:

P(A|B)=(0.07*0.1)/0.05=0.14

In other words, if the patient is an alcoholic, their chances of having liver disease is 0.14 (14%). patient has liver disease.

3. Examples 2: what is the probability that they will be prescribed pain pills?

no one right way to do this: use the terminology that makes most sense to you.

In a particular pain clinic, 10% of patients are prescribed narcotic pain killers. Overall, five percent of

the narcotics (including pain killers and illegal substances). Out of all

the people prescribed pain pills, 8% are addicts. If a patient is an addict, what is the probability that

they will be prescribed pain pills?

KG COLLEGE OF ENGINEERING & TECHNOLOGY

(Approved by AICTE, New Delhi, Affiliated to JNTUH, Hyderabad) Chilkur (Village), Moinabad (Mandal), R. R Dist, TS-501504

Page 14

That information is in the italicized part

10%.

That information is also in the italicized

part of this particular question. Event B is being an addict. given as 5%. Step 3:Figure out what the probability of event B (Step 2) given event A (Step 1). In other words, they are an That is given in the question as 8%, or .8.

Step 4:Insert your answers from Steps 1, 2 and 3 into the formula and solve.

P(A|B) = P(B|A) * P(A) / P(B) = (0.08 * 0.1)/0.05 = 0.16 The probability of an addict being prescribed pain pills is 0.16 (16%).

4. Examples 3: the Medical Test if a person gets a positive test result.

what are the odds they actually have the genetic defect? A slightly more complicated example involves a medical test (in this case, a genetic test): There are out there, and they are all equivalent (they are just written in slightly different ways). In this next equation, some changes in the denominator. The proof of why we can rearrange the equation like this is beyond

the scope of this article (otherwise it would be 5,000 words instead of 2,000!). However, if you come

answer:

1% of people have a certain genetic defect.

quotesdbs_dbs19.pdfusesText_25

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