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# subplots
plt.figure(figsize=(20,10))
for i,col in enumerate(wine_df.columns[:-1]):
  plt.subplot(2,6,i+1)
  sns.boxplot(x=wine_df['quality'], y=wine_df[col])
plt.tight_layout()

# boxplots for each column
plt.figure(figsize = (20,10))

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df['variable_bins'] = pd.qcut(df['variable'], 5)

def calculate_woe_iv(dataset, feature, target):
    lst = []
    for i in range(dataset[feature].nunique()):
        val = list(dataset[feature].unique())[i]
        lst.append({
            'Value': val,
            'All': dataset[dataset[feature] == val].count()[feature],
            'Good': dataset[(dataset[feature] == val) & (dataset[target] == 0)].count()[feature],

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import pandas as pd
import numpy as np
import pandas.core.algorithms as algos
from pandas import Series
import scipy.stats.stats as stats
import re
import traceback
import string

max_bin = 20

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def plot_decision_regions(X, y, clf,
                          feature_index=None,
                          filler_feature_values=None,
                          filler_feature_ranges=None,
                          ax=None,
                          X_highlight=None,
                          res=0.02, legend=1,
                          hide_spines=True,
                          markers='s^oxv<>',
                          colors='red,blue,limegreen,gray,cyan'):

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# display branches
git branch

# checkout to master
git checkout master

# create and checkout to a new branch
git checkout -b new_branch
# share the branch and track it
git push -u origin new_branch

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from sklearn.metrics import roc_curve, roc_auc_score, auc
from scipy import stats
import matplotlib.pyplot as plt
# %matplotlib inline

def plot_roc_curve(y, y_pred, gini, ks):
    fpr, tpr, thresholds = roc_curve(y, y_pred)
    roc_auc = auc(fpr, tpr)

    fig = plt.figure()

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from IPython.display import Audio, display
def all_done():
    display(Audio(url='https://sound.peal.io/ps/audios/000/000/537/original/woo_vu_luvub_dub_dub.wav', autoplay=True))