Machine Data

Venkatasai nagabhushanarao

Kadiri, Andhra Pradesh

Contact No: 891-***-****

Email-id: *******.*******@**********.**.**

CAREER OBJECTIVE:

Intend to build a career with leading corporate of stimulating environment which will help myself to explore fully and realize my potential

EDUCATIONAL QUALIFICATIONS:

Course

Institute

Board/

University

Year

Percentage

MTech (computer

science with specialization in Big data Analytics)

VIT, Vellore

VIT UNIVERSITY

2019

8.24

B. Tech (C.S.E)

Annamacharya Institute of Technology and Sciences(Autonomous), Rajampet.

J.N.T.U.A.

2016

82.68%

Intermediate

Narayana Junior College, Kurnool.

B.I.E.A.P.

2012

96.30%

S.S.C

Valmeeki high school, kadiri

S.S.C

2010

88.00%

TECHNICAL SKILLS:

Programming Languages : C, C++, Core Java, python.

Area of Expertise : Hadoop, Machine learning,Deep learning, NLP (text analytics),Hive, Apache pig, Spark

Machine learning libraries : scikit learn,tensor flow

Software and Design Tools: Microsoft Office

Operating systems : windows XP

Database : SQL server

Web Technologies : Html,CSS

ACADEMIC PROJECTS

Title : Diabetes disease prediction using Machine learning algorithms.

Description : Diabetes is considered as one of the deadliest and chronic diseases which causes an increase in blood sugar. The tedious identifying process results in visiting of a patient to a diagnostic center and consulting doctor. But the rise in machine learning approaches solves this critical problem. The aim of this study is to design a model which can predict the likelihood of diabetes in patients. Therefore three machine learning algorithms namely SVM,RandomForest,KNN are implemented on the collected data to find out the probability of occurring diabetes. The performances of all the three algorithms are evaluated on various measures like Precision, Accuracy, F-Measure, and Recall.

Title : Prediction Of Genetic Variants For Personalized Medicine .

Description : Personalized medical treatment in cancer requires huge manual effort even with advanced genetic analysis technology. Once sequenced, a cancer tumor can have thousands of genetic mutations. But the challenge is distinguishing the mutations that contribute to tumor growth. Currently this interpretation of genetic mutations is being done manually. This tedious task can be automated with the help of machine learning algorithms. Although previously Some techniques are applied on the same data but not highly accurate. In this report a system is proposed with various algorithms like Random Forest, Naive Bayes, Xgboost and their performance is evaluated.

ACHIEVEMENTS

Completed a certificate on” Machine Learning “from COURSERA.

STRENGTHS:

Can adapt to various situations prioritizing multiple work assignments simultaneously.

Quick learner with good grasping ability.

Easily communicate with others.

EXTRA CURRICULAR ACTIVITIES:

Volunteer in Swatch Bharath Programme in Our town.

Represented as a team member for cricket.

PERSONAL DETAILS:

Name : K. Venkatasai nagabhushanarao

Father’s Name : k. Ramakrishnaiah

Date of Birth : 2nd august 1995

Hobbies : Playing Cricket, Watching movies, Internet Surfing

Languages Known : English, Telugu

Declaration:

I hereby declare that the above information provided is true to the best of my knowledge and belief

Date :

Place:

(Venkatasai nagabhushanarao)

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