import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import joblib

df = pd.read_csv('music.csv')
X = df.drop(columns="genre")
y = df["genre"]

#split our data into train and test DataFrames (20% for testing)
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)

model = DecisionTreeClassifier()

# train model
model.fit(X_train,y_train)

# run predict using test data
# predictions = model.predict(X_test)
# score = accuracy_score(y_test, predictions)

#save our model
joblib.dump(model,"music-recomender.joblib")

import joblib

#load our model
model = joblib.load("music-recomender.joblib")

# run predict using test data
predictions = model.predict([[20,1],[21,0]])

predictions

import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree

df = pd.read_csv('music.csv')
X = df.drop(columns="genre")
y = df["genre"]

model = DecisionTreeClassifier()

# train model
model.fit(X,y)

# export graph of data in dot format
tree.export_graphviz(model,out_file='music_recomender.dot', 
                    feature_names=['age','gender'], 
                    class_names=sorted(y.unique()),
                    label='all',
                    rounded=True,
                    filled=True)