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

I'm trying to make an ANN in Python to predict something from a dataset (in this case diabetes), and I'm struggling to figure out how to solve this error.

Here is the full code:

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, KFold, cross_val_score
from sklearn import preprocessing
from keras import Sequential
from keras.layers import Dense
from sklearn.metrics import confusion_matrix, accuracy_score

data = pd.read_csv('C:/Users/<<>>/Downloads/Dataset of Diabetes.csv')

# drop irrelevant columns
dropcols = ['ID', 'No_Pation']
data = data.drop(dropcols, axis=1)
data.info()

X = data.values
Y = data['CLASS'].values

label_encoder = preprocessing.LabelEncoder()
data['CLASS'] = label_encoder.fit_transform(data['CLASS'])
data['Gender'] = label_encoder.fit_transform(data['Gender'])
data['CLASS'].unique()
data['Gender'].unique()
data.info()


X = np.delete(X, 1, axis=1)

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=42)

X_train = np.asarray(X_train).astype(np.float32)
Y_train = np.asarray(Y_train).astype(np.float32)

classifier = Sequential()
classifier.add(Dense(units=10, activation='relu', input_dim=X.shape[1]))
classifier.add(Dense(units=10, activation='relu'))
classifier.add(Dense(units=1, activation='sigmoid'))
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
classifier.fit(X_train, Y_train, epochs=100, batch_size=10)

Y_pred = classifier.predict(X_test)
Y_pred_int = (Y_pred > 0.5).astype(int)
cm = confusion_matrix(Y_test, Y_pred_int)
acc = accuracy_score(Y_test, Y_pred_int)
print("Accuracy:", acc)
print("Confusion Matrix:\n", cm)

This is what the last "data.info()" line returns:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 12 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Gender  1000 non-null   int32  
 1   AGE     1000 non-null   int64  
 2   Urea    1000 non-null   float64
 3   Cr      1000 non-null   int64  
 4   HbA1c   1000 non-null   float64
 5   Chol    1000 non-null   float64
 6   TG      1000 non-null   float64
 7   HDL     1000 non-null   float64
 8   LDL     1000 non-null   float64
 9   VLDL    1000 non-null   float64
 10  BMI     1000 non-null   float64
 11  CLASS   1000 non-null   int32  
dtypes: float64(8), int32(2), int64(2)
memory usage: 86.1 KB

Here is the error message that I am getting:

Traceback (most recent call last):
  File "C:\Users\<<>>\PycharmProjects\AI2\NeuralNetwork.py", line 32, in <module>
    X_train = np.asarray(X_train).astype(np.float32)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: could not convert string to float: 'M'

Another error I just realised I have (more of warning) is: UserWarning: Do not pass an input_shape/input_dim argument to a layer. When using Sequential models, prefer using an Input(shape) object as the first layer in the model instead. super().init(activity_regularizer=activity_regularizer, **kwargs) What does this mean?

Also, I have been getting getting the recurring error of "ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float).

If there are also any other issues with what I have done so far, please let me know!

Many Thanks

Link to the dataset: https://data.mendeley.com/datasets/wj9rwkp9c2/1

I've already tried converting the X and Y trains to np arrays, but I'm not sure what else I need to do.