INTRODUCTION

Exoplanets, also known as extrasolar planets, are planets that exist outside our solar system. They orbit stars other than our Sun and can provide valuable insights into the formation and characteristics of planetary systems beyond our own. The discovery and study of exoplanets have revolutionized our understanding of the universe and the possibilities of finding habitable environments and potential signs of life.

PROJECT DESCRIPTION

The "Hunt for Exoplanets using Machine Learning" project aims to utilize machine learning algorithms to identify and classify exoplanets from astronomical data. This project holds significant importance in the field of astronomy as it can assist scientists in identifying potential exoplanet candidates more efficiently and accurately.

SUPERVISED

The project was conducted under the expert supervision of Spartifical, an esteemed organization specializing in artificial intelligence and machine learning research. Spartifical provided valuable guidance throughout the project, ensuring the adoption of best practices and the utilization of cutting-edge techniques.

METHODS

During the course of this project, several skills were employed to effectively solve the problem at hand. These skills include:

-Data preprocessing and feature engineering techniques to prepare the dataset for machine learning algorithms.

-Implementation and evaluation of the K-Nearest Neighbors (KNN) model. KNN is a non-parametric algorithm used for both classification and regression tasks, where predictions are made based on the similarity of the new instance to its neighboring data points.

-Training the KNN model twice, once on the training dataset and later on the testing dataset, to validate its performance and assess generalization.

-Utilization of the Decision Tree algorithm, a versatile and interpretable machine learning model that builds a tree-like structure to make decisions based on feature conditions.

CONCLUSION

The "Hunt for Exoplanets using Machine Learning" project presents a valuable approach to identify and classify exoplanets using machine learning techniques. With the guidance of Spartifical, the project successfully employed the KNN model for classification tasks and applied the Decision Tree algorithm to enhance the accuracy of exoplanet identification. This project holds promise for the future exploration of exoplanets and contributes to expanding our understanding of the vast universe we inhabit.

DATA SOURCE

The dataset used in the "Hunt for Exoplanets using Machine Learning" project is sourced from Kaggle, a popular platform for data science and machine learning enthusiasts. The dataset provides a comprehensive collection of astronomical observations and associated attributes that are crucial for identifying exoplanets. With its availability on Kaggle, the project benefits from the accessibility and collaborative nature of the platform. The Kaggle dataset plays a pivotal role in facilitating the exploration and analysis of exoplanet data, enabling researchers and data scientists to contribute their insights and advancements to the wider scientific community.