My Projects
Some of the things that I have built in the past!
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Developed by AI4Bharat Lab at IIT Madras. Shoonya is an open source platform to annotate and label data at scale, built with a vision to enhance digital presence of under-represented languages in India. The aim is to improve the efficiency of language work in Indian languages with AI tools and custom-built UI interfaces and features.
Developed by AI4Bharat Lab at IIT Madras. Chitralekha is an open source platform tool for video subtitling across various Indic languages, using ML model support (ASR for Transcription and NMT for Translation).
This application allows a user to enter an article title on Wikipedia. The application collects article data from Wikipedia and lets the users translate the article content into other languages. The application is built using Django and HTML Templates. The application is hosted on Python Anywhere.
Unemployment Rate forecasting tool built for BMWi during the Data Science for Social Good Fellowship. The model predicts the unemployment rate in Germany at the county level.
This is a portfolio website which can help you showcase your projects, your education and work experience. The new features let you add your resume, recommendations, bio and social media links among so many other features!
As the name CiviWiki implies, our core content will be contributed by volunteers on our Wiki. Our topic format is modular. The structure allows both a community of volunteers to collaborate on a single political issue and reserves space for dissenting opinions.
Data Storyteller is an AI based tool that can take a data set, identify patterns in the data, can interpret the result, and can then produce an output story that is understandable to a business user based on the context.
Platform that supports the users in their trips to the supermarket by optimising their visit. Application to display all nearby stores, product catalog and stock-on-hand to prevent hoarding and breach of social distancing protocol for users and streamline inventory management for local shopkeepers.
This is an application that automates the process of text analysis with a user friendly GUI. 📱 It has been implemented using Python and the features have been deployed using the Streamlit package.
Socket programming is a way of connecting two nodes on a network to communicate with each other. One socket(node) listens on a particular port at an IP, while other socket reaches out to the other to form a connection. Server forms the listener socket while client reaches out to the server.
Solved two real-world problems using Data Collection and Econometrics on Stata. The first problem involved identifying whether the an aptitude test which is conducted before a course is good enough to predict the course outcome for each student in the class. The second problem was solved using the Educational Attainment and Wage Equations dataset to see if there were any differences in the educational attainment of males and females and how closely was it related to their race. I received an A grade in the course.
This software will be used to create and manage a peer-to-peer delivery system without involving any courier service in the middle. A person who is already travelling from one place to another can act as a delivery medium for someone else who is looking for a cheaper and faster mode of delivering items.
We have built a UNIX Shell in C which can perform all the basic built-in functions of a unix shell. The execvp() function has been used for the same. To implement cd command chdir() in-built C function has been used. Additionally, there is a history feature and an execute most recent commands feature.
Projects that I completed for the Udacity AI for Trading Nanodegree. Learnt the basics of quantitative analysis, including data processing, trading signal generation, and portfolio management. Used Python to work with historical stock data, develop trading strategies, and construct a multi-factor model with optimization. Learnt how to analyze alternative data and use machine learning to generate trading signals.
An end-to-end machine learning project where I performed data collection, data analysis, built and deployed a Multinomial Naive Bayes Model. Analysed Reddit data to predict which flair(category) a post would belong to by submitting the URL of the post. Created a front-end using HTML, CSS and JS and the model was deployed using Flask and Docker.
Used Recurrent Neural Networks and LSTM implementation with 60-day time steps to predict the trends of Alphabet Inc. closing stock prices with a mean absolute error of 9.641.
