Sign in

Python, Java, Algorithms, Data Mining, Machine Learning, Hadoop, Spark

Fairfax, Virginia, United States
April 04, 2018

Contact this candidate


Prakhar Dogra



Master of Science in Computer Science, May 2018

George Mason University, Fairfax, VA GPA: 3.8

(Relevant Coursework: Machine Learning, Computer Vision, Pattern Recognition, Mining Massive Datasets using MapReduce, Data Mining) Nanodegree: Deep Learning (Udacity) May 2018

Nanodegree: Machine Learning Engineer (Udacity) Feb 2018 Bachelor of Technology in Computer Engineering, July 2016 Delhi Technological University, Delhi, India GPA: 3.5 TECHNICAL SKILLS

Proficient: Python, Java, C/C++, Hadoop, SPARK, Tensorflow, OpenCV Familiar: HTML, CSS, JavaScript, R, SQL, Django, Amazon EC2, Google Cloud RELEVANT EXPERIENCE

System Design Intern, Cafex Communications, New York Jun - Aug 2017

• Developed an Intelligent document parsing system to extract relevant information from meeting minutes

• Deployed the system online using Django web framework Data Science Intern, Premier Logic, Noida, India Jan - July 2016

• Scraped news websites in order to find recent news of a particular domain.

• Developed an API for Virtuagym to find clients interested in fitness and gym using Twitter. PROJECTS

• Bank Marketing Classification.

• Implemented various models like Decision Tree, Random Forest, Adaboost, Naïve Bayes, Multi-Layer Perceptron and Support Vector Machine.

• Conducted comparative study on the above models when applied on different feature sets obtained via feature selection (Ch- Square Test), feature transformation (Principal Component Analysis) and feature elimination (Recursive Feature Elimination).

• Youtube Video Label Classification.

• Implemented a Long term Recurrent Convolution Network to classify YouTube videos into multiple classes

• Extracted frames from videos as input to a Convolution Neural Network for feature extraction.

• The features so extracted are passed to a LSTM for predicting labels.

• Finding similar images using Locality Sensitive Hashing.

• Performed shingling on images followed by Min Hashing and Random Hyperplanes

• Generated Signature Matrix, a compact representation of images using above techniques.

• Generate candidate pairs from signature matrix using locality sensitive hashing in Apache Spark

• Anomaly Detection on Credit Card Fraud.

• Implemented Random Forest, log likelihood and various other models for detecting frauds.

• The dataset used for this project was Credit Card Fraud Detection dataset from Kaggle

• Meeting Minutes Parser.

• Developed a document parsing system and deployed it online, using Django web framework, that can extract required information from meeting minutes with a feedback system.

• Performed text analysis on meeting minutes to extract meaningful actions discussed in meetings

• Deployed the results on a web portal built with Django framework

• Social Media Complaint Workflow Automation Tool Using Sentiment Intelligence.

• Developed a complaint classification and forwarding mechanism for bank posts obtained from Facebook web pages.

• Used Twitter and Facebook graph API for building a crawler

• Used bag of words and NLTK for extracting features from text obtained from websites through crawler

• Classified posts into complaints, inquiries and irrelevant posts, then an additional layer of classification was added to classify complaints into relevant departments.

• Twitter Analysis to Find New Gym Clients.

• Used Twitter API to fetch tweets

• Implementing classifiers such as SVM, Multinomial Naïve Bayes Classifier and its variations

• After classification, actual usernames, mentions on twitter for the classified users were extracted.

Contact this candidate