application_vnd.jupyter.stderr_output_1.png
import numpy as np
import pandas as pd
import pandas
import os
import gc
import pylab
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import pearsonr, probplot, norm, shapiro
import statsmodels.api as sm
pal = sns.color_palette()
pd.set_option('display.max_columns', 50)
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
from plotly.subplots import make_subplots
import plotly.tools as tls
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn import preprocessing
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, accuracy_score, f1_score
from sklearn.linear_model import LinearRegression
import statsmodels.api as sm
from sklearn import linear_model
import tkinter as tk
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
from matplotlib import style
from sklearn.model_selection import train_test_split
style.use('fivethirtyeight')
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
import statsmodels.api as sm
from statsmodels.formula.api import ols
import scipy.stats as stats
from bioinfokit.analys import stat
from scipy.stats import chi2_contingency
import io
import re
import nltk
from nltk.corpus import stopwords
import string
import sklearn
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import random
from wordcloud import WordCloud, STOPWORDS
from textblob import TextBlob
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))