• Create simple machine learning model
• Export the model in binary format
• load the model into the code

pip install scikit-learn

pip install pandas

We need to download the Music file

https://github.com/mosh-hamedani/python-supplementary-materials

https://github.com/mosh-hamedani/python-supplementary-materials/blob/main/music.csv

• Sample learning code

  import pandas as pd
  from sklearn.tree import DecisionTreeClassifier 
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score
  
  music_data = pd.read_csv('music.csv')
  X = music_data.drop(columns = ['genre'])
  y = music_data['genre']
  
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
  
  model = DecisionTreeClassifier()
  model.fit(X_train,y_train)
  predictions = model.predict(X_test)
  score = accuracy_score(y_test, predictions)
  print(score)
  

• Expected result

  

• Export the model into joblib format

  import pandas as pd
  from sklearn.tree import DecisionTreeClassifier 
  from sklearn import tree
  import joblib
  
  music_data = pd.read_csv('music.csv')
  X = music_data.drop(columns = ['genre'])
  y = music_data['genre']
  
  model = DecisionTreeClassifier()
  model.fit(X,y)
  
  joblib.dump(model,'music-recommender.joblob')

• Expected Result (New file will be created)

  

• Load the model into the code

import joblib

model = joblib.load('music-recommender.joblob')

predictions = model.predict([[21,1]])
print(predictions)

• Expected result

  

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