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import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow import keras
from tensorflow.keras import layers, models
from tensorflow.keras.utils import to_categorical

# Kerasに付属の手書き数字画像データをダウンロード

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!apt install aptitude
!aptitude install mecab libmecab-dev mecab-ipadic-utf8 git make curl xz-utils file -y
!pip install mecab-python3==0.7

import pandas as pd
import numpy as np
import collections
import MeCab
import lightgbm as lgb
from sklearn.model_selection import train_test_split

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import glob
import pandas as pd
import numpy as np
import category_encoders as ce
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score

files = glob.glob("train/*.csv")
data_list = []

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import glob
import pandas as pd
import numpy as np
import xgboost as xgb
import category_encoders as ce
from sklearn.model_selection import train_test_split

files = glob.glob("train/*.csv")
data_list = []
for file in files:

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###  1.データ集計・加工・描画

# ライブラリの読み込み
from sklearn import datasets
import pandas as pd

# irisデータの読み込み
iris = datasets.load_iris()
iris
##irisデータの可視化

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from selenium import webdriver
import time

driver = webdriver.Chrome()

#Googleのブラウザを開く
driver.get('https://www.google.com/')
time.sleep(2)

#スタビジを検索

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import pandas as pd
import numpy as np
from sklearn.datasets import load_iris
from sklearn.cluster import KMeans
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score

iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
pred_cluster = KMeans(n_clusters=3).fit_predict(df)

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import xgboost as xgb
import pandas as pd
import numpy as np
from tensorflow.keras.datasets import mnist
from sklearn.model_selection import train_test_split

# Kerasに付属の手書き数字画像データをダウンロード
np.random.seed(0)
(X_train_base, labels_train_base), (X_test, labels_test) = mnist.load_data()

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import pandas as pd
import numpy as np
from tensorflow.keras.datasets import mnist
from sklearn.model_selection import train_test_split

# Kerasに付属の手書き数字画像データをダウンロード
np.random.seed(0)
(X_train_base, labels_train_base), (X_test, labels_test) = mnist.load_data()

# Training set を学習データ（X_train, labels_train）と検証データ（X_validation, labels_validation）に8:2で分割する