<jupyter_start><jupyter_text>Build a cuisine recommender<jupyter_code>import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score
from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType

data = pd.read_csv('../data/cleaned_cuisines_own.csv')
data.head()

X = data.drop(["Unnamed: 0", "cuisine"], axis=1)
X.head()
y = data[["cuisine"]]
y.head()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

model = SVC(kernel="linear", C=10, probability=True, random_state=0)
model.fit(X_train, np.ravel(y_train))
y_pred = model.predict(X_test)

print(classification_report(y_test, y_pred))
initial_type = [('float_input', FloatTensorType([None, 380]))]
options = {id(model): {'nocl': True, 'zipmap': False}}<jupyter_output><empty_output>
StarcoderdataJupyterStructuredCleanDedup