AstroWeaver: The Space Suit That’s Smarter Than Your Smartphone

Tired of space suits getting torn by sneaky meteorites, I developed AstroWeaver, a self-healing suit fabric that patches itself faster than you can say, “Houston, we have a problem.” Microcapsules filled with adhesive polymers burst open at the first sign of trouble, repairing damage instantly. Because who has time to sew in zero gravity?

I designed MarsHab, a spacecraft habitat module built for long-haul trips to Mars. It comes packed with intelligent thermal management, advanced radiation shielding, and an AI-based life-support system that constantly adjusts conditions—because manually setting a thermostat 140 million miles from home gets tricky. MarsHab is so comfy, some astronauts joked they’d rather stay in orbit than deal with Earth traffic again.

Achieved an 80% accuracy rate in classifying celestial objects as Stars, Galaxies, or Quasi-Stellar Objects using a machine learning model that analyzes solar data. It was done through Data Collection, Data Preprocessing, Model Optimization, Iterative Improvement and Documentation.I Gathered a comprehensive dataset of solar data, including various features and attributes. I then Cleaned the dataset by handling missing values and outliers. Further Chosing appropriate machine learning algorithms for classification tasks and then Fine-tune hyperparameters of the model using techniques like grid search or random search to improve its performance. Lastly, Documented the entire process, including data sources, preprocessing steps, model architecture, and results, to facilitate reproducibility and collaboration.

Tackled the learning issue by implementing various personalized learning styles and improved the product checkout process by decreasing the clicks required for payment from 4 to 2 through A/B testing. This streamlined the checkout process, making it more efficient and user-friendly.

In collaboration with a fellow Data Scientist, we developed a powerful machine learning model. This model proved to be highly effective in analyzing medical data and accurately predicting whether a patient had a medical condition, achieving a remarkable accuracy rate of over 90%. Using Machine Learning and TensorFlow we facilitated the model's development. Data Analysis and Data Visualization techniques enabled us to gain valuable insights from the medical data, while Python played a pivotal role in implementing and fine-tuning the model, resulting in its impressive predictive accuracy.