Co-created a YouTube channel to curate music of Indian Electronic Dance Music (EDM) genre during undergrad as a passion project.

Description

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• Published over 180 videos and grew the YouTube channel to 580,000+ subscribers, 13000+ Instagram followers, and 6000+ Soundcloud followers.
• Managed promotional Facebook and Google ad campaigns for over 20 artists.
• Received Silver Play Button from YouTube creator awards team for crossing 100,000 subscribers.
• Developed a bootstrap website ( turbantrapofficial.com) to automate music submission process.
• Designed an AR filter using Spark AR Studio that was shared over 1.7 million times on Instagram and Facebook.

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A Python application deployed using Flask to compare followers.html and following.html files, and figure out the list of people who have unfollowed you.

How do I use this?

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• Step-1 Go to your profile, click the 3 lines at the top right corner of your screen. Tap on 'Your Activity' option. Hint - It has a clock as an icon. Now scroll down and tap on the 'Download your information' button and request download. It will authenticate you, confirm your email address and send you the data in about 4-6 hours.
• Step-2 Go to your email, you would have received a new email from IG with subject as 'Your Instagram Information'. Download the attached file, and extract it. It will create a folder with this format 'yourusername_date'. Open it, open a subfolder named 'followers_and_following'. You will find a file named followers.html and a file named following.html, these two are important for us.
• Step-3 Go to https://akshaysinngh.com/unfollowers/ (or simply click the Unfollowers button at the top).
• Step-4 Upload followers.html, and then following.html and press the submit button.

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Description

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• Developed and executed an end-to-end data analysis pipeline for a retail store, leveraging AWS S3 for data storage and Snowflake for data warehousing. Utilized Python in Jupyter Notebook for data transformation and cleaning, ensuring high-quality data preparation for analysis.
• Established a robust data integration process, connecting AWS S3 buckets to Snowflake, enabling seamless data extraction and loading into Snowflake tables. Enhanced data accessibility and manipulation by creating a connection between Snowflake and a local Jupyter Notebook environment.
• Designed and implemented an interactive dashboard in Tableau, utilizing Snowpipe to stream data from Snowflake, providing real-time insights and visualizations of retail trends based on comprehensive datasets, including transactions, products, coupons, and demographics.
• Automated the data update process by setting up a cron job in Jupyter Lab, integrated with Amazon SQS, to trigger updates in the Tableau dashboard whenever new data is uploaded to the AWS S3 buckets, ensuring real-time data accuracy and relevance in visual analytics.

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Technologies Used: Snowflake, AWS, Python, SQL, and Tableau.

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Think Genius.com, but for cancer research. This one is personal. As a Non-Hodgkin Lymphoma survivor (5+ years in remission), I built EliminateCancer.ai to make cancer research accessible to patients, caregivers, and anyone trying to make sense of a diagnosis.

What It Does

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The platform pulls peer-reviewed, open-access papers from the PMC Open Access Web Service API and Europe PMC API, covering 12 cancer types and growing. Each paper is run through the Claude API, which generates plain-English explanations that break down the medical jargon into something anyone can understand. A custom split-panel reader lets you view the original PDF (rendered in-browser with PDF.js) side by side with the AI-generated annotations, so you never lose context. Client-side search lets you find papers instantly across the entire library.

How It Was Built

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Built entirely using Claude Code and Claude Cowork. The frontend runs on Bootstrap 5 with Font Awesome 6 and Google Fonts for a clean, responsive design. Python automation scripts handle the data pipeline, pulling papers from PMC and Europe PMC, processing metadata, and generating the simplified explanations via the Claude API. New papers are added every week. Always free, always open-access.

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Technologies: Claude API, Claude Code, Claude Cowork, PMC OA API, Europe PMC API, PDF.js, Bootstrap 5, Python, Font Awesome 6.

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The largest speakeasy directory on the internet. 6,375+ listings across 1,978 cities, all in one place.

What It Does

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SpeakeasiesUSA.com is a comprehensive, wiki-style guide to speakeasy bars across the United States. Every listing includes ratings, review counts, addresses, and a custom popularity score powered by a scoring algorithm (rating weighted by the log of review count). Users can browse by state, search by city with autocomplete, or explore curated city pages with interactive carousels and filtering. The site spans 8,360+ total URLs and serves as the definitive resource for hidden bar discovery.

How It Was Built

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The entire site is statically generated using a Python and Pandas data pipeline that processes a master CSV of every speakeasy listing in the country. Python scripts generate all city pages, state pages, and listing cards from templates. The frontend is built on Bootstrap 5 with a dark theme, using Swiper.js for touch-friendly carousels, AOS for scroll animations, GLightbox for image previews, Isotope for dynamic filtering and sorting, and Typed.js for animated text effects. Custom Web Components built in vanilla JavaScript handle reusable UI elements across the site. Built with Claude Code.

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Technologies: Python, Pandas, Bootstrap 5, Swiper.js, AOS, GLightbox, Isotope, Typed.js, Web Components, Claude Code.

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Labelmaster is a Chicago based company, that specializes in helping businesses comply with regulations for the transportation of hazardous materials.
The main objective of our team was to come up with a machine learning model that would optimize Google Ads expenditure, aiming to either reduce costs, or increase conversions, or ideally achieve both.

Description

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• Leveraged Tableau's dashboards to conduct exploratory data and trend analysis in 6+ years of Google Ads data.
• Forecasted an 18 month bidding price trajectory for all products through ARIMA time-series analysis.
• Implemented PuLP linear programming model with custom constraints to optimize bidding prices for all 40 campaigns, resulting in a reduction of advertisement costs by about 12%.

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Technologies Used: Python, Machine Learning and Tableau.

 

This is a lighthearted project that playfully examines the distinct experiences and behaviors of the two genders.It explores aspects like swiping tendencies, age preferences, prevalent professions, and the influence of the initial message on a conversation.

Description

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• Processed unstructured JSON data with 2 million+ rows, anonymized user names to ensure data confidentiality.
• Performed trend analysis using Tableau on user behaviors, swiping patterns, time spent, selection preferences and messaging habits, differentiated by gender. Published the insights on Medium.

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Technologies Used: JSON, Python and Tableau.

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As a huge UFC fan, I've developed a machine learning model that predicts the outcomes of upcoming or hypothetical UFC matchups.

Description

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• Extracted 20 years of UFC event data using BeautifulSoup, followed by extensive data cleaning and EDA. Automated the data scraping and cleaning workflow with a CRON job setup.
• Deployed various Machine Learning models (Linear Regression, SVM, Decision Trees, Naïve Bayes and Random Forest) on Sreamlit to predict fight results with 83% accuracy.

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Technologies Used: JSON, Python, Machine Learning and Streamlit.

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