Asteroid Classification

This repository consists in the application of machine learning models and algorithms related to supervised learning.

Features

Used the NASA Asteroids dataset to predict whether an asteroid is potentially hazardous;
Performed data cleaning, preprocessing, and normalization to handle missing and inconsistent values;
Conducted exploratory data analysis (EDA) to identify patterns, correlations, and feature importance;
Implemented and compared multiple supervised learning algorithms, including Decision Trees, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM);
Split the dataset into training and testing sets for model validation and generalization testing;
Tuned model parameters to improve accuracy and reduce overfitting;
Evaluated models using key metrics such as accuracy, precision, recall, and F1-score;
Visualized results through confusion matrices and performance comparison charts;
Drew conclusions on the most effective algorithm for asteroid hazard prediction.

Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
README.md		README.md
baseline.ipynb		baseline.ipynb
nasa.csv		nasa.csv
notebook.ipynb		notebook.ipynb