While trainig my model got an error "failed to convert a numpy array to a tensor (unsupported object type int)"

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Python

tensorflow

Keras

<big> know there are ans to the following q but none of them working for me. so could someone help me?
context:-
I was coding a neural net and I was getting an error "Failed to convert a NumPy array to a Tensor (Unsupported object type int)"
this model was made to predict based on user-provided 7 values and would predict 4 values.</big>

I

```




```
when I try to do this ```data = data.astype(float)```
I get an error ValueError: could not convert string to float: 'Unknown'
when I try to do this ```data = data.astype(int)```
I get an error pandas.errors.IntCastingNaNError: Cannot convert non-finite values (NA or inf) to integer



full traceback :-
```
Traceback (most recent call last):
File "c:\Users\kani2\Desktop\Forest-Fire-Prediction-Website-master\forest_fire.py", line 61, in <module>
log_reg.fit(x_train,y_train,epochs=10,shuffle=True,batch_size=3)
File "C:\Users\kani2\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\kani2\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\framework\constant_op.py", line 102, in convert_to_eager_tensor
return ops.EagerTensor(value, ctx.device_name, dtype)
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type int).
```

from sklearn.model_selection import train_test_split
import warnings
import pickle
import pandas as pd

import matplotlib as mp
from tensorflow.keras.layers import Dense
from keras import Sequential
import tensorflow as tf
from tensorflow.keras import regularizers
from tensorflow import keras
warnings.filterwarnings("ignore")
###########################imports done#############################################

data = pd.read_csv("datset.csv")


#################### data imported ################################################
label_encode = {"City": {'Ahmedabad':0,'Aizawl':1, 'Amaravati':2, 'Amritsar':3, 'Bengaluru':4, 'Bhopal':5,'Brajrajnagar':6, 'Chandigarh':7, 'Chennai':8, 'Coimbatore':9, 'Delhi':10, 'Ernakulam':11,'Gurugram':12, 'Guwahati':13, 'Hyderabad':14, 'Jaipur':15, 'Jorapokhar':16, 'Kochi':17 ,'Kolkata':18,'Lucknow':19, 'Mumbai':20 ,'Patna':21, 'Shillong':22, 'Talcher':23, 'Thiruvananthapuram':24,'Visakhapatnam':25}}

data.replace(label_encode,inplace=True)
abel_encode = {"Region": {'5. Western' :0,'2. North Eastern':1, '1. Northern':2 ,'6. Southern':3, '3. Central':4,'4. Eastern':5}}

data.replace(label_encode,inplace=True)
label_encode = {"State": {'Gujarat':0, 'Mizoram':1 ,'Andhra Pradesh':2 ,'Punjab':3, 'Karnataka':4,'Madhya Pradesh':5, 'Odisha':6, 'Chandigarh':7, 'Tamil Nadu':8, 'Delhi':9 ,'Kerala':10,'Haryana':11, 'Assam':12 ,'Telangana':13, 'Rajasthan':14 ,'Jharkhand':15, 'West Bengal':16,'Uttar Pradesh':17, 'Maharashtra':18, 'Bihar':19, 'Meghalaya':20}}

data.replace(label_encode,inplace=True)
label_encode = {"Month": {'01. Jan':1, '02. Feb':2, '03. Mar':3, '04. Apr':4, '05. May':5, '06. Jun':6, '07. Jul':7,'08. Aug':8, '09. Sep':9 ,'10. Oct':10, '11. Nov':11, '12. Dec':12}}

data.replace(label_encode,inplace=True)
label_encode = {"Season": {'1. Winter':0, '2. Summer':1, '3. Monsoon':2, '4. Post-Monsoon':3}}

data.replace(label_encode,inplace=True)
label_encode = {"Weekday_or_weekend": {'Weekday':0, 'Weekend':1}}

data.replace(label_encode,inplace=True)

########################values replaced############################################
x_values = data[['City','State','Region','Month','Year','Season','Weekday_or_weekend']]
y_values = data[['PM2.5','CO','SO2','AQI']]

x_train, x_test, y_train, y_test = train_test_split(x_values,y_values,test_size=0.15,random_state=1)


###################values split ###################################################
log_reg = Sequential()

log_reg.add(Dense(10,input_dim=7,activation='relu'))

log_reg.add(Dense(20,activation='relu'))
log_reg.add(Dense(10,activation='relu'))

log_reg.add(Dense(4,kernel_initializer='normal',activation='relu'))

log_reg.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])
log_reg.summary()
log_reg.fit(x_train,y_train,epochs=10,shuffle=True,batch_size=3)

####################### model made ###################################################
pickle.dump(log_reg,open('model.pkl','wb'))
model=pickle.load(open('model.pkl','rb'))


#*********************** end ****************

What I have tried:

i hav tried seeing ans in google tho none help me