Software Engineer in Machine Learning
As the part of the R&D team I develop and implement solutions in the domains of Machine Learning. I have worked on projects for clients that include leading consulting companies, and multinational banks of the world. Some notable projects:
- Risk Analysis
- Banks receive millions of documents for violating terms. Weather or not the document is pertinent requires one to read it in completion, this platform aims to ameliorate this problem by evaluating the document for possible risks and violations and predicting the extent of possible loss for the bank.
- Bidirectional Long Short Term Memory Network, Word Embedding and Convolutional Neural Network were used to classify the text for risk with a precision of 92% and accuracy of 88%.
- On classification of risk, an Extremely Randomized Trees classifier was used to predict the extent of risk (Very risky to less risky) hence enabling the bank to address the most relevant issues first.
- Brand Sentiment Analysis
- The BSA platform helps companies to determine the success of their products in market based on sentiment analysis on empirical and live stream data from sources like Twitter, Facebook, News portals, and other sources giving key insights into how the end customer feels about the product.
- Bagged model with Naive Bayes, Max Entropy, and Multilayered Perceptron was trained with an accuracy of 83%
- Invoice Filter
- Extracting the required fields from scanned invoices is a challenging and tedious task, Invoice Filter aims to solve it.
- After processing like Binarisation, Deskewing, Rescaling with OpenCV, and ImageMagick. Tesseract model was used for this OCR task.
- Technology stack: Python, Flask, NLTK, OpenCV, Tesseract, PDFMiner.