Palak Langer Software developer at Cisco Systems India Pvt. Ltd. I am backened developer, enthusiastic to learn latest technologies. Love to play badminton, travel and explore new places.

Projects

• Analysis Of Young People Survey Data Aug 2017 – Dec 2017 Project descriptionAnalysis and Mining of the financial, psychological and educational details of the Youth of Slovakia. Performed descriptive analysis, classification, clustering and association rule mining on the data. Applied the different algorithms and techniques of data analysis. Languages and Tools : Coded in R and Python, Tableau for data visualisation Dataset-https://www.kaggle.com/miroslavsabo/young-people-survey
• Taxi Trip Time Prediction & DataAnalysis: A Study of NYC Taxi and Limousine Commission dataset Sep 2017 – Dec 2017 Project description● Performed Exploratory Data Analysis(EDA) on the NYC taxi trip data ● Augmented data with external data-sources(weather; OSRM-fastestRoute) ● Applied various machine learning techniques like clustering (for features), regression and ExtremeGradientBoosting(XGB) to predict the duration of a taxi trip in New York City.
• Event Recognition In Cricket Videos Using Temporal Sequencing Jan 2017 – Jun 2017 Project descriptionWrote a classifier that classifies ball by ball event into outcome like four, six, wicket and run. In the classifier, individual frames are categorised into categories like pitch, ground and sky using a k-nearest neighbour classifier on various extracted features like visual bag of words, Grass Pixel Ratio. The classifier then uses pattern mining algorithm to form feature vector and finally uses support vector machine for categorisation.
• XML Based Content Management System Jan 2017 – Jun 2017 Project descriptionDeveloped a content management system that is used to manage a large collection of college data. The system also allows dynamic addition of new pages. The system also converts XML into required HTML and PDF format using languages like XSLT and XSL-FO.
• Gender Classification Using Feature Extraction, Oct 2015 – Dec 2015 Project descriptionBuilt a model that obtained an accuracy of 70.02% to detect gender from facial images. Machine learning techniques like k-nearest neighbour and random forests were used with appearance based features like pyramid histogram of oriented gradients and local binary pattern for gender detection

Work Experience

Skills