├── Decision.py ├── README.md ├── Visualization.py ├── load.py └── udoooo ├── README.md ├── udoooo └── udoooo.pub /Decision.py: -------------------------------------------------------------------------------- 1 | """HOW TO TRAIN A MODEL TO MAKE ACCURATE DECISION""" 2 | import pandas as pd 3 | from sklearn.tree import DecisionTreeClassifier 4 | from sklearn.model_selection import train_text_split 5 | from sklearn.metrics import accuracy_score 6 | 7 | music_data = pd.read_csv('music.csv') 8 | x = music_data.drop(columns=['genre']) 9 | y = music_data['genre'] 10 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) 11 | 12 | model = DecisionTreeClassifier() 13 | model.fit(X_train, y_train) 14 | # 21 and 22 is assigned to the age, and 0, 1 is assigned to gender 15 | predictions = model.predict(X_test) #( [21, 1], [22, 0] ) 16 | 17 | score = accuracy_score(y_test, predictions) 18 | score 19 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Machine Learning Project Using JUPYTER NOOTBOOK 2 | #### (c) These readme explains the Steps i followed in this project. 3 | ___ 4 | [![standard-readme compliant](https://img.shields.io/badge/readme%20style-standard-brightgreen.svg?style=flat-square)](https://github.com/Innocentsax/standard-readme) 5 | 1. Import the Data 6 | 2. Clean the Data 7 | 3. Split the Data into training/Test Set 8 | 4. Create a Model 9 | 5. Train the Model 10 | 6. Make Predictions 11 | 7. Evaluate and Improve 12 | -------------------------------------------------------------------------------- /Visualization.py: -------------------------------------------------------------------------------- 1 | """Visualization of Decision Tree""" 2 | import pandas as pd 3 | from sklearn.tree import DecisionTreeClassifier 4 | from sklearn.externals import joblib 5 | 6 | music_data = pd.read_csv('music.csv') 7 | x = music_data.drop(columns=['genre']) 8 | y = music_data['genre'] 9 | 10 | 11 | model = DecisionTreeClassifier() 12 | model.fit(X, y) 13 | 14 | tree.export_graphviz(model, out_file='music-recommender.dot', 15 | feature_names=['age','gender'], 16 | class_names=sorted(y.unique()), 17 | label='all', rounded=True, filled=True) 18 | -------------------------------------------------------------------------------- /load.py: -------------------------------------------------------------------------------- 1 | """HOW TO LOAD MODEL""" 2 | import pandas as pd 3 | from sklearn.tree import DecisionTreeClassifier 4 | from sklearn.externals import joblib 5 | 6 | music_data = pd.read_csv('music.csv') 7 | x = music_data.drop(columns=['genre']) 8 | y = music_data['genre'] 9 | 10 | 11 | model = DecisionTreeClassifier() 12 | model.fit(X, y) 13 | 14 | joblib.dump(model, 'music-recommender.joblib') 15 | # predictions = model.predict( [21, 1], [22, 0] ) 16 | -------------------------------------------------------------------------------- /udoooo/README.md: -------------------------------------------------------------------------------- 1 | These contain currupted files 2 | -------------------------------------------------------------------------------- /udoooo/udoooo: -------------------------------------------------------------------------------- 1 | -----BEGIN OPENSSH PRIVATE KEY----- 2 | b3BlbnNzaC1rZXktdjEAAAAACmFlczI1Ni1jdHIAAAAGYmNyeXB0AAAAGAAAABBWoqBeyP 3 | qXNd80NQN7unDNAAAAEAAAAAEAAAEXAAAAB3NzaC1yc2EAAAADAQABAAABAQDeS0bui5xx 4 | 5t+YvX271RCuS/amj1D7QgSO/eJsLyeBwsqJmfXsunidMPL9qw3gEyxzRgXNdqEJYckA2Q 5 | H1KE4ADT1rmRHpMgUcNT98ariIk8FXpcfi8FpbH4wLLs4wGCmmhhqND3D5f15yEtHdZypG 6 | p0G4bsCm0Tb6LMfXfs9BpwH8yHIRQVXrhgLUazcfJCDUUtIEaWp8cPr/yfg/ip8ZnWLUKs 7 | cy187DuT7Ywl0RgGflgCqYg1Wc740BbZqOhG/Lwt9DB5jXLWzel6i7D0FsIy1S0pDhX9jr 8 | v4mmO/f/Gut79HHGBCmPwJPMHHw0RaZ16Nl0G/QfrZid1sZP2DHpAAAD4MTQlL7OM0LjC4 9 | /IauAVFHwRwxVa0FMn4wwi4FSYwdVLQNzFHvOJF9NyU1nZ9QRSS9GeGEIqLgzFucMFC7Ff 10 | Nxxr0bcWArlOAYNdSrrRDjGg1IZI752S/+p6G0lleDK7/oJqnH0BZJaxEB8YBSnXsq12U+ 11 | AIMEbN9XDjZKYXVS7RXIEKfGeIMFUK4VWSP8zbXAMi257ja0hnG+fAZQL1DmFA4miEOEkc 12 | u6SamP34Gbs6poQNyefyeGjXN9lNBaUvAB6tYqDARUnmftMbJ69XYvKo399e0TkAFqByx2 13 | WHMrsiFERlak/7jjhqQT01VpQi1G3gEEvg7f+g2uKDP2L6FqLgsPbaSIhMukdSHPUvp+7l 14 | Y+a3cyOKtuRvFd9IED/QRSYjT29q3alhGoC1DNEtijcQVd0OQxP+e1DG0lMIKwtViIY5Mp 15 | tLJQTdzIOjiiuhrtAQlagu/749I1ApKsCQ6pZ8L+sL6Pvp9g90EdZUvL1aBsUTMStP/SIH 16 | 1DxHkVamS+yvSoKEmp96w4W18+lYXG1Ih2uWh+vLT3V6RHAPBrg2O/Q2+wy+VYxj29F5iq 17 | tQ2h4XU0TMSAcVRqqHfRaZs2Ql+OTN6XQuuQwPJmnAHjgKo5vEddXuhvpNDbhWbhOqQqG3 18 | +RNPok/0u73THIShAP0Xrni3JHVspR6pbG47Cx8/vg0NFkgr/HWyK2DxHE0oYfYMqp6Mh+ 19 | RtFd6xMOKKelALol/EE1Ybd8bk1JT1Nmbh+r4FzC3GMTo5rJUXhWv7ffwTk8ETFjf8DnUT 20 | T3FLQ/n/mfvw1fk9UR39/lf+Q5Au/EU+mlElR5ukHNj8uCUjWzRErxbhuwVHmYX6XbT01V 21 | y960CvT/oN4w8YxtLK8dRAsDM5VbWFb6WWg51UctakSPdv2te/jJybpRQ53ezy3vyJyHFS 22 | ToOSHwsSmsUPKzl4sJSF1/4GiwuD5g7QY9Gg83x78RXrn0EEGHD5yxpAlyhWlpSu7471pI 23 | 1wS5Zf7XjF+gJ5Qb19lwZ1RkP3y4eUHHisxArNAPUP4aTxD8jv/3WCp/R6MNcC3eDSryLd 24 | P7auWhzA66vPRfAfR6GOfQwfRpf5sBZNjo/4THTQgJsGwNVO6vRKREY9VqH/wjOkl/O/EU 25 | Aztcuc3BmL6Gag1DElFdoefAqGpHRlGPK9aMPPi8xjDQKYWuf0GShGP7DoNbHtKtwEfYa5 26 | DCANVcDtSHqzlDTx8VTFpyg15oIV7B0Cqeo9g2mMc9NuqH+Bhm3Y+rbzH7lC/5+WLMzQkW 27 | PDQMOuEPXqzUW4HE7+e937z4ECla35adysoBR9uLn4L5+GBRpf 28 | -----END OPENSSH PRIVATE KEY----- 29 | -------------------------------------------------------------------------------- /udoooo/udoooo.pub: -------------------------------------------------------------------------------- 1 | ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABAQDeS0bui5xx5t+YvX271RCuS/amj1D7QgSO/eJsLyeBwsqJmfXsunidMPL9qw3gEyxzRgXNdqEJYckA2QH1KE4ADT1rmRHpMgUcNT98ariIk8FXpcfi8FpbH4wLLs4wGCmmhhqND3D5f15yEtHdZypGp0G4bsCm0Tb6LMfXfs9BpwH8yHIRQVXrhgLUazcfJCDUUtIEaWp8cPr/yfg/ip8ZnWLUKscy187DuT7Ywl0RgGflgCqYg1Wc740BbZqOhG/Lwt9DB5jXLWzel6i7D0FsIy1S0pDhX9jrv4mmO/f/Gut79HHGBCmPwJPMHHw0RaZ16Nl0G/QfrZid1sZP2DHp innocentsax@DESKTOP-R76GFQP 2 | --------------------------------------------------------------------------------