  Photometric redshift estimation plays a pivotal role in modern astronomy,
enabling the determination of celestial object distances by analyzing their
magnitudes across various wavelength filters. This study leveraged a dataset of
50,000 objects sourced from the Sloan Digital Sky Survey (SDSS), encompassing
magnitudes in five distinct bands alongside their corresponding redshift
labels. Traditionally, redshift prediction relied on the use of spectral
distribution templates (SED), which, while effective, pose challenges due to
their cost and limited availability, particularly when dealing with extensive
datasets. This paper explores innovative data-driven methodologies as an
alternative to template-based predictions. By employing both a decision tree
regression model and a Fully Connected Neural Network (FCN) for analysis, the
study reveals a notable discrepancy in performance. The FCN outperforms the
decision tree regressor significantly, demonstrating a notable improvement in
root mean square error (RMSE) compared to the decision tree. This improvement
highlights the FCN's ability to effectively capture complex relationships
within space data. The potential of data-driven redshift estimation is
underscored, positioning it as a valuable tool for advancing astronomical
surveys and enhancing our comprehension of the universe. With the adaptability
to either replace or complement template-based methods, FCNs are poised to
reshape the field of photometric redshift estimation, opening up new
possibilities for precision and discovery in astronomy.
