Across every project I’ve worked on — from machine learning pipelines to fire detection
systems — data has always been the engine. What started as curiosity in building
things has grown into a focus on data analytics, where I can combine creativity
with critical thinking to turn raw data into real value.
I’ve built dashboards in Excel, Power BI, and Tableau to visualize everything
from customer behavior to global job trends. I’ve written SQL scripts to clean
and transform messy datasets, automated data flows using Python, and trained
models that make predictions from both structured and sensor-based data.
My experience covers: Data cleaning and wrangling (Power Query, SQL, Python pandas),
Exploratory data analysis (using visualization tools to identify trends and outliers),
Statistical thinking (from distributions to correlations and regression),
Building end-to-end data projects — with clear business objectives and actionable
insights.
No matter the tool or the data source, I aim to answer one question: “So what?” What does this insight mean for the business, the user, or the system? That’s the mindset I bring to every analysis.
As I move forward in my career, I’m focused on roles where I can help organizations leverage data to solve problems, optimize processes, and uncover opportunities. If that sounds like what you're building, I’d love to be part of it.
This analysis was built to help a potential Airbnb host answer the golden question: "How much can I actually make?" Using a dataset split into three tables—listings, reviews, and calendar—I explored patterns using Tableau.
This project explored a dataset collected via an online survey of data professionals, covering
everything from job roles to satisfaction with work-life balance. The aim was to present the
experience and realities of data careers in an intuitive dashboard.
In this project, I took on the challenge of cleaning a real-world dataset of global layoffs using SQL—a
key part of preparing data for deeper analysis. The raw data had no primary key, inconsistent
entries, and a lot of duplication.
This project began with a dataset of individuals and whether or not they purchased a bike. It
included features like marital status, commute distance, home ownership, region, gender, and number
of children. I approached it like a business case—what insights can help a company understand its
bike-buying customers?.
Expanding on the IoT fire detection system, I developed a cloud-integrated machine learning
extension that predicts fire outbreaks from visual data...
This project combines machine learning, IoT, and full-stack development to support preventive
healthcare. I built a system to predict hypertension (a key risk factor for heart attacks) using
both user-provided lifestyle data and real-time sensor inputs.
Fires remain a major cause of residential fatalities, especially when no one is home to respond to a
traditional alarm. To tackle this, I built a fire detection system using IoT and automation
technologies that not only detects fire outbreaks but also sends emergency alerts to homeowners and
relevant authorities via the internet.