machine learning - CSV dataset scaling as input

Question

I want to make a project for classification of csv file using deep learning but I have faced a problem in input scaling part. Now I have taken the dataset into two categories and that's are X_data and Y_data. Please concern below code and who know please help me, below the imported package, import tensorflow as tf

import keras.backend as K
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping
from keras.utils import to_categorical
import keras

import numpy as np

from keras.layers import BatchNormalization
from keras.layers import Dropout
from keras import regularizers

import pandas as pd

import sklearn
from sklearn import preprocessing
from sklearn.model_selection import train_test_split

import matplotlib
from matplotlib import pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format='retina'

Import input (x) and output (y) data, and asign these to df1 and df2

df1 = pd.read_csv('X_data.csv')
df2 = pd.read_csv('Y_data.csv')

Scale the input data

df1 = preprocessing.scale(df1)   //I have faced error here

Below the error is,

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-3-aec70d746687> in <module>
      1 # Scale input data
      2 
----> 3 df1 = preprocessing.scale(df1)

~/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
     71                           FutureWarning)
     72         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 73         return f(**kwargs)
     74     return inner_f
     75 

~/anaconda3/lib/python3.8/site-packages/sklearn/preprocessing/_data.py in scale(X, axis, with_mean, with_std, copy)
    139 
    140     """  # noqa
--> 141     X = check_array(X, accept_sparse='csc', copy=copy, ensure_2d=False,
    142                     estimator='the scale function', dtype=FLOAT_DTYPES,
    143                     force_all_finite='allow-nan')

~/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
     71                           FutureWarning)
     72         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 73         return f(**kwargs)
     74     return inner_f
     75 

~/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
    597                     array = array.astype(dtype, casting="unsafe", copy=False)
    598                 else:
--> 599                     array = np.asarray(array, order=order, dtype=dtype)
    600             except ComplexWarning:
    601                 raise ValueError("Complex data not supported
"

~/anaconda3/lib/python3.8/site-packages/numpy/core/_asarray.py in asarray(a, dtype, order)
     83 
     84     """
---> 85     return array(a, dtype, copy=False, order=order)
     86 
     87 

ValueError: could not convert string to float: 'discrete'

# Split the data into input (x) training and testing data, and ouput (y)

Answer 1

等待大神答复