Machine Learning Software that predicts planets based on their distance from the sun, number of satellites and various properties
I developed Machine Learning Software that predicts planets based on their distance from the sun, number of satellites and various properties. This machine learning software is based on Random Forest Classifier and Random Forest Regression. Based on the principles of Supervised Learning, machine learning software predicts planets by their distance from the sun, Confirmed Moons, Provisional Moons, Total Moons, Volume (cubic kilometers) and planet's diameter.
The values you enter should be (respectively):
1) Enter to Distance From The Sun
2) Enter to Confirmed Moons
3) Enter to Provisional Moons
4) Enter to Total Moons
5) Enter to Volume (Enter the state / 1.000.000.000) - Cubic (km)
6) Enter to Diameter Of Planet (km)
Example: model_run = model.predict([[Distance_From_The_Sun,Confirmed_Moons, Provisional_Moons, Total_Moons, Volume_1000000000_cubic_km, Diameter_of_Planet_km]])
Outpot : Predicted Planet: ['Mercury']
I am happy to present this software to you!
Data Source: DataSource , DataSource1 ###The coding language used:
Python 3.9.6
###Libraries Used:
Sklearn
Pandas
Name-Surname: Emirhan BULUT
Contact (Email) : emirhan.bulut@turkiyeyapayzeka.com
LinkedIn : https://www.linkedin.com/in/artificialintelligencebulut/
Official Website: https://www.emirhanbulut.com.tr
