text_html_output_1.png application_vnd.jupyter.stderr_output_1.png
from sklearn.preprocessing import LabelEncoder

import nltk

import os

import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import lightgbm as lgb
import nltk
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
import joblib
from spacy.util import minibatch, compounding
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer
from nltk.tokenize import sent_tokenize
from nltk.sentiment import SentimentIntensityAnalyzer
import multiprocessing
from tqdm import tqdm
from sklearn.model_selection import KFold
le = LabelEncoder()
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))