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%load_ext watermark
%watermark -v -m -p numpy,scipy,pandas,matplotlib,sklearn,keras
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!free -m
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import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve
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import time
from tqdm import tqdm_notebook
for i in tqdm_notebook(range(100)):
time.sleep(0.05)
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cancer = load_breast_cancer()
X, y = cancer.data, cancer.target
pd.DataFrame(X, columns=cancer.feature_names).head()
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import xgboost, lightgbm
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X_train, X_test, y_train, y_test = train_test_split(X, y)
xgb= xgboost.XGBClassifier(n_estimators=200)
xgb.fit(X_train, y_train)
prediction = xgb.predict_proba(X_test)
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fpr, tpr, _ = roc_curve(y_test, prediction[:,1])
plt.plot(fpr, tpr);
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import fbprophet
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!vw --version
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import keras