This is my code and it has an error that I don't know how to fix
PRACTICE OF REGRESION
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime
from time import time
from sklearn.svm import SVR
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
Data
proof_df = pd.read_excel("WORK_NUM_3.xlsx")
Show information (only 10 of 1550)
proof_df.head(10)
results
ORDEN DATA N1 N2 N3 N4 N5
0 1 1994-03-13 25 45 60 76 79
1 2 1994-03-17 13 30 58 63 64
2 3 1994-03-20 5 15 32 33 48
3 4 1994-03-24 27 57 60 61 77
4 5 1994-03-27 19 44 53 54 71
5 6 1994-04-03 4 45 54 65 67
6 7 1994-04-07 9 21 37 42 68
7 8 1994-04-10 5 16 26 28 62
8 9 1994-04-14 4 15 44 64 73
9 10 1994-04-17 20 32 49 54 62
declare variables
y = proof_df.iloc[:, 2:len(quina_df.columns)]
X = proof_df[['ORDEN','DATA']]
regression algorithm
regresor = SVR(kernel='linear')
hora_inicio = time()
train of algorithm
regresor.fit(X_train.values, y_train.values.ravel())
print('train finish in {time() - hora_inicio} segundos')
result:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [37], in <cell line: 2>()
1 # Entrenamiento del algoritmo
----> 2 regresor.fit(X_train.values, y_train.values.ravel())
3 print('Entrenamiento finalizado en {time() - hora_inicio} segundos')
File ~\anaconda3\lib\site-packages\sklearn\svm\_base.py:190, in BaseLibSVM.fit(self, X, y, sample_weight)
188 check_consistent_length(X, y)
189 else:
--> 190 X, y = self._validate_data(
191 X,
192 y,
193 dtype=np.float64,
194 order="C",
195 accept_sparse="csr",
196 accept_large_sparse=False,
197 )
199 y = self._validate_targets(y)
201 sample_weight = np.asarray(
202 [] if sample_weight is None else sample_weight, dtype=np.float64
203 )
File ~\anaconda3\lib\site-packages\sklearn\base.py:581, in BaseEstimator._validate_data(self, X, y, reset, validate_separately, **check_params)
579 y = check_array(y, **check_y_params)
580 else:
--> 581 X, y = check_X_y(X, y, **check_params)
582 out = X, y
584 if not no_val_X and check_params.get("ensure_2d", True):
File ~\anaconda3\lib\site-packages\sklearn\utils\validation.py:964, in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
961 if y is None:
962 raise ValueError("y cannot be None")
--> 964 X = check_array(
965 X,
966 accept_sparse=accept_sparse,
967 accept_large_sparse=accept_large_sparse,
968 dtype=dtype,
969 order=order,
970 copy=copy,
971 force_all_finite=force_all_finite,
972 ensure_2d=ensure_2d,
973 allow_nd=allow_nd,
974 ensure_min_samples=ensure_min_samples,
975 ensure_min_features=ensure_min_features,
976 estimator=estimator,
977 )
979 y = _check_y(y, multi_output=multi_output, y_numeric=y_numeric)
981 check_consistent_length(X, y)
File ~\anaconda3\lib\site-packages\sklearn\utils\validation.py:746, 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)
744 array = array.astype(dtype, casting="unsafe", copy=False)
745 else:
--> 746 array = np.asarray(array, order=order, dtype=dtype)
747 except ComplexWarning as complex_warning:
748 raise ValueError(
749 "Complex data not supported\n{}\n".format(array)
750 ) from complex_warning
TypeError: float() argument must be a string or a number, not 'Timestamp'
I I am trying to work with the dates as the variable ( X ) that the model should receive since the variable ( y ) are the other results
I really appreciate any help to help understand what is going on