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11

data science intern

  • Developing a tool for Brand Logo Detection  to find influencers and content creators that are in sync with a brand’s audience for sponsored content    
  •  Building  and Deploying State of the art scalable Deep learning models to  flag Unsafe Youtube Channels to enable ease of deployment of ad campaigns  for  brands
  • Contributing to extraction  and  visualization of Channel and Video level data using Youtube Data API 
  • Sentimental analysis of youtube
  • Keyword extraction by using web scraping and natural language processing
12

data science intern

  • Developed a graph based customer recommendation model for 30+ customers which resulted in creating aggregated business value of $20mn+ for our customers.
  • Primary responsibilities include initial research, designing graph data schema, gathering, cleaning, ingesting data and building a graph based recommendation engine.
  • Technologies used : Neo4j, Python, Keras
  • Performed multiple Exploratory Data Analysis on a given dataset.
13

data science intern

  • Developed and deployed a polynomial regression model to predict a date for when a given number of projects uploaded on www.pypi.org could be reached. 
  • Performed an exploratory data analysis on 30,000 properties in Berlin to determine price trends. 
  • Technologies/languages used: Python (BeautifulSoup, Requests, Pandas, Seaborn, Sklearn, Flask).
  • Developed code for implementing algorithms to detect outlier based anomalies in sensor readings using statistical methods and machine learning algorithms using Python
14

data science intern

  • Working on projects that span from Supervised & Unsupervised learning to NLP & Deep Learning. 
  • Implement ML Algorithms on Spark via PySpark. 
  • Use Elastic Stack for Log Analysis and Nagios for Infrastructure Monitoring. 
  • Developed REST APIs. 
  • Gained Exposure to Databases such as MongoDB and Postgres
15

data science intern

  • Scraped tweets off twitter through twitter API
  • Applied text pre-processing techniques like tokenisation, stemming and lemmatisation
  • Applied feature engineering techniques like Bag of words models, TF-IDF vectorisation to get required features
  • Used cosine similarity to cluster statements which have similar sentiments
  • Applied Naive-bayes model and linear models
  • Got an accuracy of 87% on test dataset for the model