ML_FOR RECOMMENDER

MUSIC_RECOMMENDER.py

import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt

data = pd.read_csv('music.csv') #THIS WORKS FOR SMALL DATA 

X = data.drop(columns=['genre'])
y = data['genre']

X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.2)
model = DecisionTreeClassifier()
model.fit(X,y)
predictions = model.predict(X_test)
score = accuracy_score(y_test, predictions) # Accuracy of 1.0
# IF THE DATA IS BIG THE SCORE MIGHT CHANGE

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