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tejaswirupa/README.md

👋 Hi, I’m @tejaswirupa. I am a data storyteller disguised as a scientist, currently based in Seattle, WA.

👀 Interests: I believe data is more than numbers! It’s patterns, people, and potential. My favorite thing is building models that don’t just predict, but explain. I love helping teams see the “aha!” moment in the data and turning it into action.

🌱 What I'm Learning: Most of my projects are born out of curiosity - “Why is this happening?”, “What changed?”, “How can we do better?” If I can break down something complex into a simple plan that sparks better decisions, that’s a win.

📊 Tools I use to bring ideas to life: python snowflake r git SQL Tableau dbt

🧠 Currently geeking out over LLMs, time series insights, and building models that talk back (hello, chatbots 👋).

📫 Let’s connect: LinkedIn

Popular repositories Loading

  1. Bird-Species-Identification-with-Deep-Learning Bird-Species-Identification-with-Deep-Learning Public

    Classified bird species from vocal calls using deep learning on audio spectrograms. Achieved 95.45% binary and 67.24% multi-class accuracy, demonstrating strong performance in biodiversity audio an…

    Jupyter Notebook 2

  2. Data-Analysis-of-Departure-Delays-at-United-Airlines Data-Analysis-of-Departure-Delays-at-United-Airlines Public

    Explored how weather and time factors influence delays in 58,000+ UA flights. Used permutation testing and visual analytics to show how temperature, visibility, and time of day affect departure pun…

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  3. Early-Prediction-of-Diabetes-Risk-Using-Machine-Learning Early-Prediction-of-Diabetes-Risk-Using-Machine-Learning Public

    Built a predictive model using CDC health data to identify individuals at risk of developing diabetes. Achieved 90.6% F1-score using Logistic Regression and revealed key health indicators like BMI …

    Jupyter Notebook

  4. United-Airlines-Flight-Gain-Analysis United-Airlines-Flight-Gain-Analysis Public

    Analyzed 30K+ United Airlines flights to evaluate time gained or lost during flight. Used hypothesis testing to compare on-time vs. late departures, identifying routes with the highest average time…

  5. Exploring-Home-Ownership-with-Support-Vector-Machines Exploring-Home-Ownership-with-Support-Vector-Machines Public

    Used SVM with various kernels to predict home ownership status based on census data. Identified age, number of bedrooms, and household size as top factors influencing ownership.

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  6. Unsupervised-Learning-Analysis-of-causes-of-death-among-children Unsupervised-Learning-Analysis-of-causes-of-death-among-children Public

    Analyzed global child mortality data using PCA and clustering to identify cause-based patterns across 180+ countries. Revealed dominant mortality factors like respiratory infections and preterm bir…

    Jupyter Notebook