Question: i am trying to run linear regression and i am having issues with data type i think. I have tested line by line and everything works until i reach last line where i get the issue TypeError: invalid Type promotion. Based on my research i think it is due to date format. Here is my code: import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression data=pd.read_excel('C:\\Users\\Proximo\\PycharmProjects\Counts\\venv\\Counts.xlsx') data['DATE'] = pd.to_datetime(data['DATE']) data.plot(x = 'DATE', y = 'COUNT', style = 'o') plt.title('Corona Spread Over the Time') plt.xlabel('Date') plt.ylabel('Count') plt.show() X=data['DATE'].values.reshape(-1,1) y=data['COUNT'].values.reshape(-1,1) X_train,X_test,Y_train,Y_test=train_test_split(X,y,test_size=.2,random_state=0) regressor = LinearRegression() regressor.fit(X_train,Y_train) y_pre = regressor.predict(X_test)  When i run it this is the full error i get:  --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-21-c9e943251026> in <module> ----> 1 y_pre = regressor.predict(X_test) 2 c:\users\slavi\pycharmprojects\coronavirus\venv\lib\site-packages\sklearn\linear_model\_base.py in predict(self, X) 223 Returns predicted values. 224 """ --> 225 return self._decision_function(X) 226 227 _preprocess_data = staticmethod(_preprocess_data) c:\users\slavi\pycharmprojects\coronavirus\venv\lib\site-packages\sklearn\linear_model\_base.py in _decision_function(self, X) 207 X = check_array(X, accept_sparse=['csr', 'csc', 'coo']) 208 return safe_sparse_dot(X, self.coef_.T, --> 209 dense_output=True) + self.intercept_ 210 211 def predict(self, X): c:\users\Proximo\pycharmprojects\Count\venv\lib\site-packages\sklearn\utils\extmath.py in safe_sparse_dot(a, b, dense_output) 149 ret = np.dot(a, b) 150 else: --> 151 ret = a @ b 152 153 if (sparse.issparse(a) and sparse.issparse(b) TypeError: invalid type promotion  My date format which looks like this: array([['2020-01-20T00:00:00.000000000'], ['2020-01-21T00:00:00.000000000'], ['2020-01-22T00:00:00.000000000'], ['2020-01-23T00:00:00.000000000'], ['2020-01-24T00:00:00.000000000'], ['2020-01-25T00:00:00.000000000'], ['2020-01-26T00:00:00.000000000'], ['2020-01-27T00:00:00.000000000'], ['2020-01-28T00:00:00.000000000'], ['2020-01-29T00:00:00.000000000'], ['2020-01-30T00:00:00.000000000'], ['2020-01-31T00:00:00.000000000'], ['2020-02-01T00:00:00.000000000'], ['2020-02-02T00:00:00.000000000']], dtype='datetime64[ns]')  Any suggestion on how to resolve this issue?

Answer: I think linear regression not work for date type data.You need to convert it to numerical data. for example import numpy as np import pandas as pd import datetime as dt X_test = pd.DataFrame(np.array([ ['2020-01-24T00:00:00.000000000'], ['2020-01-25T00:00:00.000000000'], ['2020-01-26T00:00:00.000000000'], ['2020-01-27T00:00:00.000000000'], ['2020-01-28T00:00:00.000000000'], ['2020-01-29T00:00:00.000000000'], ['2020-01-30T00:00:00.000000000'], ['2020-01-31T00:00:00.000000000'], ['2020-02-01T00:00:00.000000000'], ['2020-02-02T00:00:00.000000000']], dtype='datetime64[ns]')) X_test.columns = ["Date"] X_test['Date'] = pd.to_datetime(X_test['Date']) X_test['Date']=X_test['Date'].map(dt.datetime.toordinal)  Try this approach.this should work. Note - it is better to covert training set dates to numeric and train on that data.