An IT professional with 2+ years of experience, recently completed a master's in Data Science to fulfil the knack of learning new technology and playing with data. Being an avid learner I would love to pursue the same learning opportunities in the future. Experienced in analysing large datasets and solving key business problems with data driven solutions. Ability to build and train machine and deep learning models from scratch and optimise for best performance.
Looking for a vibrant organization where I can utilize my skills and convert them into fruitful results for the organization's growth and development along with my own growth.
MSc Data Science
Skills Achieved: Python, Machine Learning, Deep Learning, Visualisation
B.Tech Computer Science Engineering
Skills Achieved: C++, Java, Data Structures, OOPS
• Completed summer training workshop for Data Structures and Algorithms at IIT Kanpur.
• Trained on concepts of Data Mining, Big Data Hadoop at IIT Kanpur.
• Infosys Certified Java Programmer Certification
• Infosys Certified Agile Global Certification
• Infosys Certified Selenium Basic tester Developer
Links to several of my recent quick projects. Topics and methods covered include time series analysis (vector error correction models in finance), topic modelling, Bayesian structural time series, clustering algorithms, principal component analysis (PCA), deep learning algorithms (image classifiers, LSTM, transfer learning), natural language processing applications (language models, sentiment analysis), time series analysis (retail location prediction, churn analysis, analysis of clicks on digital ads, and others), principal component analysis (PCA), and principal component analysis (PCA) in finance. Hover your mouse over the images and click on them to view the description and source code for each project.
The objective of the project was to design, create, and assess a network architecture which identifies and segments the Functional Tissue Units (FTUs) across five different organs of the Human body namely the lung, kidney, spleen, large intestine, and prostate.
It predicts the subreddits to which each Reddit post belongs using NLP. The performance of at least 5 classifiers was compared before the best one was selected and tuned to produce the best results.
It aims to develop a feature engineering technique for CNP (Central neuropathic pain) in SCI (Spinal Cord Injury) patients using feature elimination methods in KNN and Logistics Regression classifiers in order to optimise tuning parameters to evaluate cross-validation accuracy, sensitivity and specificity.
Developed a batch-based search and filtering pipeline in Apache Spark. It ranks the documents according to the user queries based on DPH ranking model.
Trained a deep neural network which takes a 32x32 image with a cell nuclei and classifies it into the type of nuclei i.e., Normal, Cancer, Immune, Connective.
This project aims to serve the deaf community by helping them communicate through American Sign Language trained through CNN using Deep Learning concepts. Would take an image as input and outputs the corresponding alphabet.
It was implemented using CNNs. A task that this project was successful in achieving was the recognition of images as per the input provided by the user.
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