PORTFOLIO
BIO
Personal Profile
• Highly motivated Computer Science & Engineering graduate seeking job opportunities in the field of
Data Science & Artificial Intelligence, where I can utilize my analytical & reasoning skills to achieve the
organizational goals.
• Currently pursuing Master of Data Science at RMIT University, Melbourne.
• Proficient in Python, R, SQL, and Java.
EDUCATION
What I’ve Learned
MASTER OF DATA SCIENCE - RMIT
March 2019 - December 2020
Avg. GPA - 3.38
Favorite fields of study: Data Preprocessing, Data Visualisation, Database Concepts, Practical Data Science.
B.TECH COMPUTER SCIENCE & ENGINEERING - MANIPAL ACADEMY OF HIGHER EDUCATION, DUBAI
September 2014 - October 2018
Excelled in big data coursework.
Thesis on ‘Identity Management Using Blockchain’- based on the Ethereum Platform. Its an Application allowing consumers and merchants to authenticate each other and thereby preventing ID fraud.
Favorite fields of study: Big Data Analytics, Database Management Systems, Information Retrieval.
Specialization in Information Management & Analytics.
MY EXPERIENCE
Background & Expertise
DATA SCIENCE INTERN
August 2020 - December 2020
Worked on the Project "The Effect of COVID-19 on Melbourne Footpath Traffic" in collaboration with Slalom.
Built a predictive model to predict when the foot traffic will return to normal.
Utilized analytical and technical expertise to provide insights on how the foot traffic varied under different
government issued health restrictions.Conducted analysis to gain a better understanding of different correlations affecting the foot traffic.
SOFTWARE ENGINEER TRAINEE - CORAL BUSINESS SOLUTIONS
February 2018 - May 2018
Worked on the development of an Android based Invoice Application.
SALES SUPPORT ENGINEER (INTERN) - SCHNEIDER ELECTRIC
July 2017 - August 2017
Underwent KNX Training and successfully cleared the KNX Basic course exam.
MY SKILLS
What I Do
PYTHON
SQL
R
JAVA
MY PROJECTS
There has been a steady decline in the pedestrian numbers throughout Melbourne, especially in the
CBD during the COVID-19 pandemic.
With the help of pedestrian sensor devices located at different parts of the city, we obtained the
footfall data. Using this data, we determine variations in pedestrian activity throughout the day,
week, month etc. Thereby, gaining an understanding of the extent to which the foot traffic in the CBD has been
impacted due to the health restrictions.
A web app which allows users to input various parameters and determines/predicts the probability of them getting a stroke.
Made using Flask and deployed using Heroku https://ml-stroke-prediction.herokuapp.com/
Uses Logistic Regression model for prediction.
Model trained using the Stroke Prediction Dataset found on kaggle. https://www.kaggle.com/fedesoriano/stroke-prediction-dataset
Australia is one of the largest exporters of red meat in the world. However, over the past two decades, the meat production has significantly reduced mostly due to the persisting dry conditions particularly in the interior regions of Australia. This report analyses the monthly production of mutton in Australia since 1972 and forecasts the amount of mutton that would be produced in the near future using time series analysis techniques.
In this study, we examine a bank telemarketing dataset. The data was from a previous marketing campaign run by a bank to promote term deposits to its clients. We try to predict whether or not a client would subscribe to a term deposit. Prediction was done with the help of various classification models. We explored a couple of these models and found out that a decision tree classifier with a test size of 0.50 provided the highest accuracy (87 percent) out of the other models employed in this study.
The objective of this analysis is to fit and compare 4 different models to predict the salaries offered by various data science job positions using the data collected from job ads posted on Glassdoor by several US companies.
This report contains a statistical analysis of the prices between Coles and Woolworths, two of Australia’s leading
supermarket chains. This study is to assess which supermarket chain is cheaper than the other based on the mean of
the prices of products available at both the supermarkets.
