Asteroid Classification using k-Nearest Neighbors (kNN) project. The project focuses on developing a machine learning model to classify asteroids based on their features and characteristics. Asteroid classification plays a crucial role in understanding these celestial bodies and their potential impact on Earth. The goal of this project is to develop a machine learning model using the k-Nearest Neighbors (kNN) algorithm to classify asteroids into different classes based on their attributes. By analyzing features such as size, shape, and spectral characteristics, the model can make predictions about an asteroid's class based on its similarity to labeled data points.
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Data Preprocessing The project includes comprehensive data preprocessing steps to clean and transform the raw asteroid data. This ensures that the data is in a suitable format for training and evaluation.
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Feature Extraction Relevant features are extracted from the asteroid dataset to capture important characteristics of each asteroid. Feature extraction techniques may involve dimensionality reduction, statistical calculations, or domain-specific knowledge.
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k-Nearest Neighbors Algorithm The kNN algorithm is implemented to classify the asteroids. The algorithm determines the class of a test asteroid by considering the k closest training asteroids in the feature space
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Model Evaluation The performance of the kNN model is evaluated using appropriate metrics such as accuracy, precision, recall, and F1 score. Cross-validation techniques may be employed to assess the model's generalization capabilities.
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Visualization The project utilizes data visualization techniques to provide insights into the distribution of asteroid classes, decision boundaries of the kNN model, or other visually informative representations.