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Galaxy Redshift Prediction

This project utilizes data from the Sloan Digital Sky Survey (SDSS) to create a machine learning model that predicts the redshift of galaxies based on their photometric properties ('u', 'g', 'r', 'i', 'z'). The model is built using TensorFlow, demonstrating the application of a Multi-Layer Perceptron (MLP) in predicting astronomical measurements.

Project Overview:

The dataset comprises photometric properties and redshifts for approximately 1M galaxies, with the aim of training a machine learning model to understand and predict how these properties correlate with redshift. The model could potentially be used to estimate redshifts for other astronomical data, assisting in cosmological studies.

Data Description

The data is extracted using a SQL query from the SDSS online database, which includes:

• Photometric magnitudes in five different bands (u, g, r, i, z).
• Spectroscopic redshifts and their errors.
• Metadata such as right ascension (ra), declination (dec), and object identifiers

Dependencies:
Ensure you have the following installed:

• Python 3.8 or above
• TensorFlow 2.x
• Pandas
• NumPy
• Scikit-learn
• Matplotlib