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Machine Project

Visakhapatnam, Andhra Pradesh, India
January 02, 2020

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Flat no. ***, Sai Balaji Enclave Appartment,Yendada, Vishakhapatnam, Andhra Pradesh - 530045

709-***-****, 996-***-****

Hardworking graduate with good communication skills along with learning attitude and also has the ability to work with a team and lead one as in when needed. I am seeking for an opportunity to work in a challenging environment where I can prove my technical skills or utilise my fast learning ability to develop new skills when needed for the organization.


X, Narayana Concept School

SSC, 2014


XII, Narayana Junior College

BSEAP, 2016


B.Tech/B.E., GITAM college of Engineering




English - Conversational, Hindi - Conversational, Telugu - Conversational HOBBIES

Learning, Fitness, Playing Games


Contest Rating 1507(Percentile 70%) in University of Codesprint,HackerRank,9 September 2018



Tools - MS Word, MS Excel, Eclipse


Data Science with Python

Score - 96/100, Certification Link - Programming Languages - C, C++, Java, Python, Javascript Technical skills - machine learning, computer vision, natural language processing, data Machine learning

Score - 94/100, Certification Link - Deep learning Specialization

Score - 98/100, Certification Link - PROJECTS

Computer performance prediction

The goal of this project is to predict the CPU performance based on the terms of its cycle time, memory size,etc.

Used various machine learning methods and classified the best method through scores . Hence used random forest regression for this project. Got 98% training accuracy and 94% test accuracy.


Digit recognition system is the working of a machine to train itself or recognizing the digits from different sources like emails, bank cheque, papers, images, etc. and in different real- world scenarios for online handwriting recognition on computer tablets or system, recognize number plates of vehicles, processing bank cheque amounts and so on. Dataset Used : MNIST

Used Jupyter Notebook Open Source Web Application

Accuracy : 99.1%

Mushroom Classification

The Purpose of this project is to find out whether the Mushroom is edible(e) or poisonous(p) based on the different features using the decision tree learning algorithm. Used various machine learning methods and classified the best method through scores . Hence used decision tree algorithm for this project. Got 100 % train and test accuracy .

Diabetes-Prediction-using-Machine-Learning-Algorithms Diabetes is considered as one of the deadliest and chronic diseases which causes an increase in blood sugar.Many complications occur if diabetes remains untreated and unidentified.The motive of this project is to design a model which can prognosticate the likelihood of diabetes in patients with maximum accuracy. Used Jupyter Notebook Open Source Web Application

PIMA Dataset

CLassifiers Used : Logistic Regression,K-neighbours Classifier,Random-Forest Classifier,SVM Classifier,GradientBoostingClassifier,AdaBoostCLassifier Facial recognition is a category of biometric software that maps an individual's facial features mathematically and stores the data as a faceprint. The software uses deep learning algorithms to compare a live capture or digital image to the stored faceprint in order to verify an individual's identity.

Web Scrapping using BeautifulSoup and Selenium

Arcface Loss function

Resnet 50 architecture

Programming Language used: Python

Working on Deployment using Flask

Face Recognition

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