This repository contains Jupyter notebooks for performing exploratory data analysis (EDA) on space data images dataset called Galaxies Center and a Galaxy Morphology predictor trained on Galaxy Zoo. The notebooks demonstrate how to perform EDA on space images on specific features, using different filters, and visualization libraries to perform Spectroscopy. The aim of this project is to provide researchers and data enthusiasts with a starting point for their own space data image analysis projects. Every Notebook on Astronomical data listed here and more are also available on my Kaggle Page too
- Project 1 : Fireworks Galaxy EDA 1: Introduction [Kaggle Notebook]
- Project 2 : Fireworks Galaxy EDA 2: Feature Engineering [Kaggle Notebook]
- Project 3 : Fireworks Galaxy EDA 3: Filters [Kaggle Notebook]
- Project 4 : Fireworks Galaxy EDA 4: Visualization [Kaggle Notebook]
- Project 5 : Galaxy Zoo Morphology Predictor [Kaggle Notebook]
The projects in this repository use the following dependencies:
- Python 3.x
- Tensorflow
- Keras
- OpenCV
- NumPy
- Matplotlib
- Seaborn
- Astropy
- Librosa
- Scipy
- Sklearn
- Skimage
You can install these dependencies using pip.
pip install tensorflow opencv-python numpy matplotlib seaborn astropy librosa scipy sklearn skimage
Each Notebook has introduction, a brief on concepts covered, a thorough walkthrough with clear explanations and conclusions. Every notebook is also available on Kaggle with the links provided.
This Space Image Data Processing and Projects repository showcases my general IQ with Astronomy and space related fields along with my skills in image processing and manipulation, computer vision, and machine learning. I have implemented various projects using popular libraries such as TensorFlow and OpenCV, and niche libraries such as Astropy, Librosa, Specutils, Scipy, etc. and have included detailed instructions on how to use and run each project.