I'm using Sklearn as a machine learning tool, but every time I run my code, it gives this error:
Traceback (most recent call last):
  File "C:\Users\FakeUserMadeUp\Desktop\Python\Machine Learning\MachineLearning.py", line 12, in <module>
    model.fit(X_train, Y_train)
  File "C:\Users\FakeUserMadeUp\AppData\Roaming\Python\Python37\site-packages\sklearn\tree\_classes.py", line 942, in fit
    X_idx_sorted=X_idx_sorted,
  File "C:\Users\FakeUserMadeUp\AppData\Roaming\Python\Python37\site-packages\sklearn\tree\_classes.py", line 166, in fit
    X, y, validate_separately=(check_X_params, check_y_params)
  File "C:\Users\FakeUserMadeUp\AppData\Roaming\Python\Python37\site-packages\sklearn\base.py", line 578, in _validate_data
    X = check_array(X, **check_X_params)
  File "C:\Users\FakeUserMadeUp\AppData\Roaming\Python\Python37\site-packages\sklearn\utils\validation.py", line 746, in check_array
    array = np.asarray(array, order=order, dtype=dtype)
  File "C:\Users\FakeUserMadeUp\AppData\Roaming\Python\Python37\site-packages\pandas\core\generic.py", line 1993, in __ array __
    return np.asarray(self._values, dtype=dtype)
ValueError: could not convert string to float: 'Paris'
Here is the code, and down below there's my dataset:
(I've tried multiple different datasets, also, this dataset is a txt because I made it myself and am to dumb to convert it to csv.)
    import pandas as pd
    from sklearn.tree import DecisionTreeClassifier as dtc
    from sklearn.model_selection import train_test_split as tts
    city_data = pd.read_csv('TimeZoneTable.txt')
    X = city_data.drop(columns=['Country'])
    Y = city_data['Country']
    X_train, X_test, Y_train, Y_test = tts(X, Y, test_size = 0.2)
    model = dtc()
    model.fit(X_train, Y_train)
    predictions = model.predict(X_test)
    print(Y_test)
    print(predictions)
Dataset:
CityName,Country,Latitude,Longitude,TimeZone
Moscow,Russia,55.45'N,37.37'E,3
Vienna,Austria,48.13'N,16.22'E,2
Barcelona,Spain,41.23'N,2.11'E,2
Madrid,Spain,40.25'N,3.42'W,2
Lisbon,Portugal,38.44'N,9.09'W,1
London,UK,51.30'N,0.08'W,1
Cardiff,UK,51.29'N,3.11'W,1
Edinburgh,UK,55.57'N,3.11'W,1
Dublin,Ireland,53.21'N,6.16'W,1
Paris,France,48.51'N,2.21'E,2