Facial-recognition with Python, OpenCV and Raspberry Pi

Built a facial-recognition solution to tackle crime tourism using a $35 Raspberry Pi, a $9 camera and Python & OpenCV. You can find all the details of this Machine Learning project on this page.

Using Python and Natural Language Processing for Mental Health Data Analysis

I used Python, NLP (natural language processing), matplotlib, seaborn, WordCloud, and Tweepy, to perform some basic analysis followed by a round of sentiment analysis on data extracted from recent tweets. In this project, I played with some hands-on implementation of pandas, natural language processing, and #matplotlib.

COVID-19 Data Analysis with Spotfire and IronPython

Explored trends, made predictions, built a user feedback form using Spotfire and IronPython on a COVID-19 dataset. Full article here.

Developed marketing insights using Predictive Analytics with SAS programming language on retail data

Worked on a meticulous group project involving 6.5 million rows of complicated retail data (spaghetti sauce)using SAS programming language and interpreted the results to answer:
What is the quantifiable effect of advertising on a specific brand of spaghetti sauce?
How can we describe typical customer behavior?
When are customers most likely to churn?

Game of Thrones – Travelers and Travel Routes

For summer 2019, I worked on a Travelytics project with this amazing data set compiled by Jeffrey Lancaster :
https://jeffreylancaster.github.io/game-of-thrones/
My aim was to draw out most dangerous travel routes in Westeros and most well-traveled GoT characters.

“That’s What She Said” Analysis with Tableau

Love The Office?
Do you know –
Which season has the most “that’s what she said”s?
Who is the most talkative character?
Who laughs the most on the show?
I unraveled these mysteries using Tableau in this project.

Predicting ETA Using R programming language

Used the R programming language to run Linear Regression, Multiple Regression, LDA and finally, the Random Forest model on 2.8 million entries of an Uber dataset of the London region to accurately predict Estimated Time of Arrival from source to destination.

Data munging, database modeling, and regression using SQL and R

Cleaned real world data set with several errors using SQL and R, followed by modeling and regression techniques to come up with interesting insights on purchases and employee performances, and used predictive statistics to forecast sales.