SAN FRANCISCO, CALIFORNIA

About

Hi! I'm Rahul Bhatia

Rahul Bhatia is a final year student pursuing his Bachelors in Computer Science and Engineering from The LNM Institute of Information Technology, Jaipur, India. He is a self taught Data Scientist and Full Stack Web Developer, always open to learn new stuff.

Contact me here!

Email: rbhatia46@gmail.com

Phone: +918989834570

What i do

Skills

Data Analysis

Data Visualization

Data Science

HTML/CSS

JavaScript

MERN Stack

Community Development

Leadership

Team Management

Portfolio

Checkout a few of my works

Finance, SVM , Algorithmic Trading

Using SVM to predict Buy/Sell for Reliance Stock

Comparing different SVM Kernels, specifically the linear kernel, polynomial kernel, and the Radial Basis Function Kernel, in their effectiveness when applied to predict buy/sell of Reliance Stock, on top of technical indicators(which are used by traders before making decision) as features for the machine learning model. Framed as a binary classification problem, Buy/Sell - Buy - if price is predicted to rise, Sell - if price is predicted to drop.

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Finance, Reinforcement Learning, Algorithmic Trading

Actor Critic Algorithmic Trading for Stock Market

Automating the trading and making intelligent decisions based on data has been quite popular recently. The objective of this project was to predict the stock market price using a special type of Reinforcement Learning algorithm, known as Actor-Critic, as opposed to Deep-Q Learning. The algorithm was backtested on predicting the stock price of a portfolio consisting of 2 probably correlated stocks, MSFT and AAPL Stocks.

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Time Series Forecasting

Taxi Demand Prediction in New York City

The objective of this project was to predict the taxi demand for yellow cabs in a particular region in next 10 minutes for New York city. Based on the data, machine learning model predicts the pickup demand of cabs in 10 minutes time frame. The data was provided by the Taxi & Limousine Commission for yellow cabs. Correct prediction of the same can fairly improve the time utilization of a taxi driver.

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Graph Theory, Social Network Analysis, Machine Learning

Social Network Graph Link Prediction : Follower Recommendation

In this project, given a directed social network graph, the objective was to predict missing links to recommend Friends/Connections/Followers, using just the information of existance of a link(a directed edge in the graph) between 2 users, and leveraging the same to predict missing links in the graph(new possible users to follow). This is an important functionality for any social network to improve user experience. The dataset used for this project was an open-sourced dataset from Facebook.

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Healthcare,NLP

Personalised Cancer Treatment

Currently the interpretation of genetic mutations is being done manually. This is a very time-consuming task where a clinical pathologist has to manually review and classify every single genetic mutation based on evidence from text-based clinical literature. This project aims to develop a Machine Learning algorithm that, using this knowledge base as a baseline, automatically classifies genetic variations.

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Finance, Exploratory Data Analysis

Lending Analysis in Financial Sector

Lending loans to ‘risky’ applicants is the largest source of financial loss(called credit loss) for any bank/lending company. If we are able to identify these risky loan applicants, then such loans can be reduced thereby cutting down the amount of credit loss. Identification of such applicants using Data Analysis is the aim of this case study. Lending Club (a peer-to-peer lending company) wants to understand the driving factors behind loan default. The company can utilise this knowledge for its portfolio and risk assessment.

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Healthcare, Computer Vision

Diabetic Retinopathy Detection

Diabetic Retinopathy can be a tricky disease to diagnose for an Ophthalmologist and is a time-taking and error-prone process. This project aims to solve the issue by taking in a high-dimensional retina image and helping an opthalmologist in the diagnosis. Finally, it was deployed as a web application to upload an image and get an inference on the go.

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Sentiment Analysis, NLP, Deployment(Flask)

Twitter Sentiment Analysis

Human preferences are practically unpredictable. That's where the science of Psychology and Sociology comes in. This project demonstrated how we can prototype an NLP based sentiment analyzer within minutes to analyze sentiments on a variety of tweets. It is relatively a small side-project but useful. Finally, I delpoyed it using Flask API to make it more usable by anyone across the web.

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Designed with & by Rahul Bhatia