├── planets_name.csv ├── emirhan_project_planet_classifier0.png ├── emirhan_project_planet_classifier1.png ├── emirhan_project_planet_classifier2.png ├── emirhan_project_planet_classifier3.png ├── emirhan_project_planet_classifier4.png ├── emirhan_project_planet_classifier5.png ├── emirhan_project_planet_regressor0.png ├── emirhan_project_planet_regressor1.png ├── emirhan_project_planet_regressor2.png ├── emirhan_project_planet_regressor3.png ├── emirhan_project_planet_regressor4.png ├── emirhan_project_planet_regressor5.png ├── planets_large_data.csv ├── LICENSE ├── .gitignore ├── README.md └── planet_prediction.py /planets_name.csv: -------------------------------------------------------------------------------- 1 | Earth,Jupiter,Mars,Mercury,Neptune,Saturn,Uranus,Venus 2 | -------------------------------------------------------------------------------- /emirhan_project_planet_classifier0.png: -------------------------------------------------------------------------------- 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Earth,149600000,1,0,1,1083,12735 5 | Mars,227900000,2,0,2,163,6767 6 | Jupiter,778500000,53,26,79,1431280,142324 7 | Saturn,1434000900,53,29,81,827130,124832 8 | Uranus,2871000900,27,0,27,68330,51726 9 | Neptune,4495000900,14,0,14,62540,49243 -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 Emirhan BULUT 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # **Machine Learning Software that predicts planets based on their distance from the sun, number of satellites and various properties** 2 | 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. 3 | 4 | The values you enter should be (respectively): 5 | 6 | **1) Enter to Distance From The Sun** 7 | 8 | **2) Enter to Confirmed Moons** 9 | 10 | **3) Enter to Provisional Moons** 11 | 12 | **4) Enter to Total Moons** 13 | 14 | **5) Enter to Volume (Enter the state / 1.000.000.000) - Cubic (km)** 15 | 16 | **6) Enter to Diameter Of Planet (km)** 17 | 18 | 19 | _Example:_ `model_run = model.predict([[Distance_From_The_Sun,Confirmed_Moons, Provisional_Moons, Total_Moons, Volume_1000000000_cubic_km, Diameter_of_Planet_km]])` 20 | 21 | _Outpot :_ `Predicted Planet: ['Mercury']` 22 | 23 | **I am happy to present this software to you!** 24 | 25 | Data Source: [DataSource] , [DataSource1] 26 | ###**The coding language used:** 27 | 28 | `Python 3.9.6` 29 | 30 | ###**Libraries Used:** 31 | 32 | `Sklearn` 33 | 34 | `Pandas` 35 | 36 | ### **Developer Information:** 37 | 38 | Name-Surname: **Emirhan BULUT** 39 | 40 | Contact (Email) : **emirhan.bulut@turkiyeyapayzeka.com** 41 | 42 | LinkedIn : **[https://www.linkedin.com/in/artificialintelligencebulut/][LinkedinAccount]** 43 | 44 | [LinkedinAccount]: https://www.linkedin.com/in/artificialintelligencebulut/ 45 | 46 | Official Website: **[https://www.emirhanbulut.com.tr][OfficialWebSite]** 47 | 48 | [OfficialWebSite]: https://www.emirhanbulut.com.tr 49 | 50 | [DataSource]: https://www.nasa.gov/ 51 | 52 | [DataSource1]: https://en.wikipedia.org/wiki/Main_Page 53 | 54 | Random Forest Classifier 55 | Random Forest Regressor 56 | -------------------------------------------------------------------------------- /planet_prediction.py: -------------------------------------------------------------------------------- 1 | from sklearn.preprocessing import LabelEncoder 2 | from sklearn.ensemble import RandomForestClassifier 3 | from sklearn.ensemble import RandomForestRegressor 4 | from sklearn.tree import export_graphviz 5 | import pandas as pd 6 | 7 | def save_decision_trees_as_dot(clf, iteration, feature_name, target_name): 8 | file_name = open("emirhan_project_planet" + str(iteration) + ".dot",'w') 9 | dot_data = export_graphviz( 10 | clf, 11 | out_file=file_name, 12 | feature_names=feature_name, 13 | class_names=target_name, 14 | rounded=True, 15 | proportion=False, 16 | precision=2, 17 | filled=True,) 18 | file_name.close() 19 | print("Decision Tree in forest :) {} saved as dot file".format(iteration + 1)) 20 | 21 | 22 | df = pd.read_csv('planets_large_data.csv') 23 | 24 | X= df.drop(['Planet'], axis = 'columns') 25 | #print(X) 26 | y= df.drop(['Distance From The Sun','Confirmed Moons','Provisional Moons','Total Moons','(Volume/1000000000-cubic km)','Diameter of Planet(km)'], axis= 'columns') 27 | #print(y) 28 | 29 | y_data = LabelEncoder() 30 | #LabelEncoder() function :)) 31 | 32 | y['Planet_Data'] = y_data.fit_transform(y['Planet']) 33 | # Planet Columns value change to Planet_Data with fit_transform function 34 | 35 | #print(connects) 36 | 37 | y_n = y.drop(['Planet'],axis='columns') 38 | #New Columns of Target :)) 39 | 40 | # In additionnn: print(y_n) 41 | 42 | 43 | feature_names = X.columns 44 | #a few fetaure names.. 45 | 46 | target_names = y_n.columns 47 | # one of the columns is target name :) 48 | 49 | model = RandomForestClassifier(n_estimators=1) 50 | 51 | # our model like to above :) 52 | 53 | model.fit(X,y_n) 54 | #our model training to the above... 55 | 56 | #print(model.estimators_[2]) 57 | 58 | #The collection of fitted sub-estimators = estimators_ 59 | 60 | for i in range(len(model.estimators_)): 61 | save_decision_trees_as_dot(model.estimators_[i], i, feature_names, target_names) 62 | print(i) 63 | 64 | 65 | #prediction is the PLANET! 66 | 67 | 68 | Distance_From_The_Sun = int(input("Enter to Distance From The Sun: ")) 69 | Confirmed_Moons = int(input("Enter to Confirmed Moons: ")) 70 | Provisional_Moons = int(input("Enter to Provisional Moons: ")) 71 | Total_Moons = int(input("Enter to Total Moons: ")) 72 | Volume_1000000000_cubic_km = int(input("Enter to Volume (Enter the state / 1.000.000.000) - Cubic (km) : ")) 73 | Diameter_of_Planet_km = int(input("Enter to Diameter Of Planet (km): ")) 74 | 75 | try: 76 | while True: 77 | model_run = model.predict([[Distance_From_The_Sun,Confirmed_Moons, Provisional_Moons, Total_Moons, Volume_1000000000_cubic_km, Diameter_of_Planet_km]]) 78 | planets = pd.read_csv('planets_name.csv',index_col=None, na_values=None) 79 | planet_detect_algorithm = planets.columns.values[model_run] 80 | print("Predicted Planet: {}".format(planet_detect_algorithm)) 81 | break 82 | 83 | except: 84 | print("Try again!") 85 | #print(model.predict([[predict_2014,predict_2020,predict_population]])) 86 | --------------------------------------------------------------------------------