***, **** ****** *** *, Brooklyn, LinkedIn: Abel NY Pratyaksh @11209,AbelPratyakshKumar (347)Kumar -335-8528, adcizh@r.postjobfree.com Master New Bachelor Dr. APJ York Abdul of of University, Science, Technology, Kalam Computer Technical Tandon Computer School Science University, Science of Engineering, KIET and group Engineering Brooklyn, of Institutions, Education NY India GPA: May May 3.33/2018 2020 4 Languages: Databases: Web Big Data/technologies: ML: MySQL, Python, MapReduce, HTML/MongoDB, Java, C/CSS, Apache C++JavaScript, DynamoDB, R Spark, Kafka, React, Hadoop, Angular, Pandas, D3.Skills js, Django, NumPy, AWS- TensorFlow S3, lambda, SNS, SQS, Elastic Search FacePay • Designed - A facial a web recognition application app with for Rekognition payments (AWS-used Academic Lambda, to learn Projects Rekognition, and detect faces Elasticsearch, using object DynamoDB) and scene detection Spring and 2020 updating a wide range of facial data in DynamoDB. The system detects an object on camera and recognizes the user.

• Designed and Developed the entire Application Login Interface by creating HTTP Request over a Web Service running on Amazon Web Server. Both Web Sockets and web services have been used throughout the application depending on the requirement. All the edge cases were taken into consideration and a system which would incorporate forgot password and Reset Password was also put into place. Data • Designed Science-Machine a pipeline Learning to solve Pipeline machine (Python, learning/TensorFlow, data science NumPy, problems Pandas) which range from classification to regression. Spring 2019 The pipeline used multiple algorithms like Logistic regression, SVM, Random forest, decision tree, ANN, and PCA.

• Includes pipelined Data preprocessing, feature extraction, Estimation and post-processing. Compatible to multiple ranges of datasets and problems. Performed with an average accuracy of 91% across 5 Kaggle competitions Real • Designed Time Financial a system Data which Analysis takes (Kafka, real time Apache market Spark, data Arima, for 500 Python) companies every 30 seconds and the machine Spring learning 2019 model fits every 30 seconds too. Used ARIMA to fit and predict.

• Used Kafka to stream and then process the real time data. The consumer side runs an API through a cluster via Apache Spark and provides data to the Kafka brokers. Consumer contains an ARIMA model which makes an estimation. The time series model achieved an RMSE of 0.9030 after testing. Sentiment • Developed Analysis a web based application Movie reviewing which smartly system scrapes (Python, data Django, from various Beautiful movie Soup, review TensorFlow) websites using Beautiful Fall soup 2017 and sends the data over to our sentiment analysis model. Text analysis is performed, and our model generates a sentiment between -1 to 1 which we later converted to percentage.

• The Project includes database management, front and backend programming and machine learning using Natural Language processing

NYU • Leads Tandon a team Online that – Senior oversees Enrollment enrollment Specialist operations Experience into the university. The job entails data analytics and visualization 2019-present which is forwarded to the marketing and the managerial teams. Stellar • Interned Inc – Web in the Development Web development Intern team and worked on managing and building web applications covering Summer-both front 2017 and back-end development.