Nitesh

Srivatsav

Graduated Avid coder

Machine Learning enthusiast

Personal Info

Address

**** ********* ****,

Dallas,TX-75252

Phone

214-***-****

E-mail

ac5z7h@r.postjobfree.com

Github

https://github.com/TheCode2017

LinkedIn

https://www.linkedin.com/in/nitesh-srivatsav/

Tools Skills

Java,Python,C++,

Advanced

HTML5,Javascript,CSS3,D3,Plot.ly.

Proficient

SQL,MySQL,NoSQL

Advanced

Google Cloud Platform,Amazon AWS EC2

Proficient

Spark(Mllib,Pyspark,Scala),Kafka,Hadoop

Proficient

TensorFlow,NLP

Proficient

Elasticsearch-Kibana,Matplotlib,seaborn

Advanced

Linux,Mac,Windows

Advanced

Hard Skills

Leadership

Teamwork

Relationship-building

Teaching

Certificates

2017-06

Machine Learning:Hands-on in Python and R In

Data Science-Udemy

2017-07

Getting and cleaning data-Coursera

2017-07

Exploratory data analysis-Coursera

Experience

2017-11 -

present

Freelance data scientist

• Worked on several data visualization and analysis jobs on Upwork.

• Built various ML models(random forest,SVM) for different jobs. Gained immense experience on cleaning dirty datasets,performed feature engineering,building optimized ML models and finding the best classification/regression models to fit on the data.

[Python]

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2015-05 -

2015-07

Paragon Digital Services

Swiftly promoted from an intern to the lead intern manager based upon leadership and teaching skills.

•

Assisted in projects to develop contextual and targeted keywords to generate efficient Google adwords(which is responsible for 80% of Google’s revenue), the biggest company that deals with adwords.

•

Scraped advertisement data from the web(from search results) using BeautifulSoup and analyzed the different ad-words used for various products.

•

2014-05 -

2014-07

HCL technologies

Completed a sentiment analysis mini-project that consisted of collecting and analyzing reviews and produced an overall review of the product.(Using JAVA and MySQL).

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Collected numerous reviews of a restaurant from the web and stored them in the MySQL database.Then using asp.net, built a tool that removed the stop words and using MeaningCloud determined the sentiment of each review and displayed the total number of positive and negative reviews for the restaurant.

•

Projects

2017-09 -

2017-05

Big Data Project “Twitter sentiment analysis"

Leveraged my expertise on the subject to help my teammates understand the subject and led the team to successfully complete this challenging project.

•

Then using Apache Kafka to stream the performed sentiment analysis on the fly (live stream) from the producer to the consumer using StanfordNLP library to classify the tweets as

'positive','negative' or 'neutral' .

•

Classified 50,000 tweets and identified the number of tweets from each category originating from different states of USA using Elasticsearch's Kibana. [Python,Scala]

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2017-09 -

2017-05

Big Data Project “Crime rate Forecasting System"

Used the Portland crime rate dataset which consisted of 829,384 rows and 19 columns which I clustered into three clusters using the Spark MLLIB(K-means clustering).

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•Developed a time forecasting system on the three clusters using the ARIMA(Autoregressive Integrated Moving Average) model that forecasted the crime rate for one month with an accuracy of 80%. [Pyspark,Scala]

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2018-05 E-commerce case study(Recommendation system) A case study to determine the reasonable complements given a product(adhesives&sealants) in a dataset of 2107537 rows(observations) 6 columns(variables).

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• Preprocessed the dataset to avoid the user cold start problem in Rec engines. Built an effective item-based Collaborative Filtering Recommendation System for an e- commerce company that recommends the top 'n' products for each product in the dataset.[Python]

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2018-04 -

2018-05

Predicting if a user clicks on an advertisement

Cleaned an advertisement dataset that consisted of 19 features and a target variable(to be predicted).

•

Using various ML models(Logistic Regression,Decision Trees,Random Forests) built a prediction model that predicts whether a user is likely to click an ad or not.

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Improved sales and website volume by identifying features that contributed positively towards a user clicking on an advertisement.

•

2017-02 -

2017-12

Object detection using TensorFlow

Led and helped the team understand the inner workings of TensorFlow with my excellent teaching skills.

•

• Used the TensorFlow API to successfully detect objects in an image/video. Trained new objects on the API(soccer ball) using Amazon EC2 for faster training(reduced training time by 20%).

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Achieved a remarkable 94% accuracy on the test images and finally used this trained model to detect the objects in a video game ( GTA V, Watchdogs). [Python,Tensorflow]

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2017-09 -

2017-11

Babysitter match system

Designed a software system for both parents and child-care takers where parents can finally find the best caretakers for their child using Spring MVC framework.

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Websites were designed using HTML,Javascript and Bootstrap and was run on the Tomcat Server which acted as the interface for the users of the system,the websites were connected to the controller(written in JAVA) which was in turn connected to the MYSQL database.

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• The project was designed using AGILE methodology. (SpringMVC,JAVA,Javascript,MYSQL). Education

2016-08 -

2018-05

The University of Texas at Dallas,Masters in Computer Science,Data Science

• Excelled in machine learning and data science coursework. Coursework: Website development,Machine learning,Big data management,Statistical methods for data science,Database Design,Algorithm Analysis and Design,Data Analysis.

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• GPA: 3.62/4

2012-08 -

2016-05

SRM University,Computer Science

GPA:3.6/4

HONORS AND AWARDS: President of the Machine Learning club,Dean's Excellence Scholarship,Top 5% of the class.

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