Completed Projects -> Links to My Work

A good data science project should have a well defined data source and a clearly understandable deliverable. Below, you can peek at a sample of some of those pieces published across the Internet.

CA ZEV Population Dashboard

CA ZEV Population Dashboard

Problem Summary:

The State of California needed a way to keep track of all deployed zero-emission (ZE) vehicles.

My Approach:

Collected the data, wrote a Python script to centralize all the data and add new features, then built a Tableau dashboard dynamically plotting the output.

Result:

The Dashboard Shown Below

Click to View the Dashboard Here
First/Last-Mile Emissions Estimation Tool

First/Last-Mile Emissions Estimation Tool

Problem Summary:

FedEx wants to quantify the emissions reduction impact from deplyoing small zero emission vehicles for last mile delivery

My Approach:

I built a JavaScript calculator app which dynaamically calculates the emissions reduction for any given input.

Result:

Link to the tool is below

Click to View Interactive Tool
KEEPA API Product

Keepa API: Amazon Price Data

Problem Summary:

Amazon sellers need access to real-time analytics

My Approach:

I build a custom tool using the Keepa API to dynamically deliver analytics on a given Amazon product.

Result:

Link to my project writeup is below

Buy the Amazon Price Data - Keepa API
Articles Published on TowardsDataScience.com

How I Built My Own Fitness Tracker Using Google Fit Data

Problem Summary:

Fitness data is stored in Google Fit but not easily visualized anywhere else

My Approach:

I build a custom tool using Google Sheets to visualize the data

Result:

Link to my my Towards Data Science writeup: "How I Built My Own Fitness Tracker Using Google Fit Data"

Project Using a Data Pipeline in Google Apps Script

Read the Article
Articles Published on Medium.com

Predicting Stock Price Using Natural Language Processing

Problem Summary:

Each quarter CFOs and CEOs of major corporations talk to investors via an earnings call. This text data can be used for predicting stock price.

My Approach:

I used NLP libraries like TextBlob and ELI5 to do sentiment analysis on text data

Result:

A Comprehensive ML experiment that uses sentiment analysis to predict stock price

Link to Medium Article