Our ML models are designed to assist in selecting the most promising space mining sites based on a combination of mineral content, sustainability, and logistical considerations. It leverages machine learning to evaluate and rank potential mining locations, enabling users to make informed decisions tailored to their specific goals.
We analyze critical data points including:
You get to customize your search by setting preferences for each feature. Whether you prioritize high iron content or proximity to Earth, our model adjusts to meet your needs.
This model is particularly useful for organizations involved in space exploration or mining, where selecting the right location can significantly impact the success and profitability of operations. It combines advanced machine learning techniques with practical user inputs to deliver actionable recommendations.
Random Forest is a popular machine learning algorithm that belongs to the supervised learning technique. It can be used for both Classification and Regression problems in ML. It is based on the concept of ensemble learning, which is a process of combining multiple classifiers to solve a complex problem and to improve the performance of the model.
Random Forest is a classifier that contains a number of decision trees on various subsets of the given dataset and takes the average to improve the predictive accuracy of that dataset. The greater number of trees in the forest leads to higher accuracy and prevents the problem of overfitting.
The model has achieved an exceptional accuracy rate of 99.99%, signifying its strong capability to reliably predict potential mining sites. This high accuracy indicates that the model correctly classifies nearly all instances in the test data, making it a robust tool for identifying viable locations for space mining.
The model was trained and tested using a comprehensive dataset comprising features related to various celestial bodies. This dataset includes inputs like Celestial Body Type, Distance from Earth, Iron Content, Nickel Content, Water Ice Percentage, Other Minerals Percentage, Estimated Economic Value, Sustainability Index, and Efficiency Index.
The dataset was carefully split into 80% training and 20% testing sets to ensure the model's performance was evaluated on unseen data, thus providing a realistic measure of its predictive accuracy.