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from math import sqrt
from numpy import concatenate
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import mean_squared_error
import numpy as np
%matplotlib inline
data = pd.read_csv(r'C:\Users\Abhishek\Desktop\nfjksnjkfn\mini project\lstm\data\data3.csv', index_col="Date")
data.columns
Index(['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')
data.info()
<class 'pandas.core.frame.DataFrame'> Index: 1097 entries, 2017-11-06 to 2020-11-06 Data columns (total 6 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Open 1097 non-null float64 1 High 1097 non-null float64 2 Low 1097 non-null float64 3 Close 1097 non-null float64 4 Adj Close 1097 non-null float64 5 Volume 1097 non-null int64 dtypes: float64(5), int64(1) memory usage: 60.0+ KB
data.tail()