import math
import random
import datatime

import pandas
import numpy
import seaborn as sns
import matplotlib
import missingno    # conda install -c conda-forge missingno

import sklearn      # conda install scikit-learn
import pickle

import mpl_toolkits
import random as rand

from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.datasets import load_boston
boston_dataset = load_boston()

import numpy as np
import pandas as pd
from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import plot_confusion_matrix
from sklearn.model_selection import GridSearchCV

conda install -c conda-forge graphviz

from wordcloud import WordCloud, STOPWORDS  # conda install -c conda-forge wordcloud
import matplotlib.pyplot as plt

import nltk
from nltk.corpus import stopwords
from nltk.corpus.reader import tagged

from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
# conda install -c conda-forge vadersentiment

from collections import OrderedDict
import csv
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer

from sklearn.datasets import make_blobs
from sklearn.linear_model import Perception
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import numpy as np
import matplotlib.pyplot as plt

# conda install -c conda-forge keras
# conda install -c anaconda keras-gpu
import tensorflow.keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout
from tensorflow.keras.optimizers import Adam

import gym          # conda install -c conda-forge gym
form IPython.display import clear_output