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from sklearn.metrics import classification_report

from sklearn.metrics import classification_report, confusion_matrix

from xgboost import XGBClassifier

from xgboost import XGBClassifier

import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sns

df = pd.read_csv('../input/parkinsonsxyz/parkinsons2.csv')

df.shape

df.describe().T

df.isnull().sum()

from sklearn.metrics import classification_report, confusion_matrix
xgb = XGBClassifier()
xgb.fit(x_train, y_train)
y_pred = xgb.predict(x_test)
print(classification_report(y_test, y_pred))
accuracy1 = xgb.score(x_test, y_test)
print(accuracy1 * 100, '%')
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(cm, annot=True)