Artificial Intelligence model for finding Exoplanets

Tech Stack - Python, Machine Learning workflows, API integration.

GitHub - https://github.com/swrraab/AI-Model-for-Finding-Exoplanets

Activity:

• Conducted an AI research project focused on classifying and identifying exoplanets orbiting distant stars using real astronomical data.

• My curiosity about space and the universe led me to explore how artificial intelligence could be used in this field, which inspired me to start this initiative and explore.

• I also took courses from Queen's University and the Inspirit AI Scholar program to support my research.

• Developed and evaluated AI-based models, machine learning models like K-Nearest Neighbors, Logistic Regression, Decision Trees, and introductory Convolutional Neural Networks using Python to train to use data to generate predictions.

• I used measurements and data that I got from the Kepler Telescope dataset to learn more about Transit Photometry and Radial Velocity.

• I also applied data preprocessing techniques such as normalization and SMOTE to address class imbalance.

Impact:

• This project helped me understand how AI can assist scientists in making sense of enormous and complex datasets produced by modern telescopes.

• By addressing the issue of false positives in exoplanet detection, I saw how machine learning can help distinguish meaningful signals from noise and make large-scale astronomical searches of thousands of stars in a more efficient and accurate way.

Outcome:

• I have developed a significant understanding of machine learning foundations, including data cleaning, model selection, evaluation, and limitations such as overfitting and imbalanced datasets.

• I have gained hands-on experience on how to apply AI to a real scientific problem in astronomy and data science.

• I have also strengthened my skills in Python, analytical reasoning, and interpreting model results, and my passion for pursuing Computer Engineering with AI as my field of passion.