Please help me solve the problem

import tensorflow as tf

import pandas as pd

import numpy as np

from keras.models import Sequential

from keras.layers import LSTM, Dense

from keras.optimizers import Adam

data = pd.read_csv(‘/content/drive/MyDrive/pokemon.csv’)

df = pd.DataFrame(data)

df.fillna(data, inplace=True)

e = pd.get_dummies(df, columns=[‘Type1’, ‘Type2’])

testik = pd.read_csv(‘/content/drive/MyDrive/pokemon.csv’)

X_train = np.array(e.drop(‘Name’, axis=1))

y_train = np.array(e[‘Name’])

X_train = X_train.reshape(-1, 1, X_train.shape[1])

X_test = np.array(data.drop(‘Name’, axis=1))

y_test = np.array(data[‘Name’])

X_test = X_test.reshape(-1, 1, X_train.shape[1])

model = Sequential()

model.add(Dense(units = 64, activation = ‘relu’))

model.add(Dense(units = 128, activation = ‘relu’))

model.add(LSTM(units = 256))

model.add(Dense(units = 512, activation = ‘relu’))

model.add(Dense(units = 1, activation = ‘tanh’))

model.compile(loss = ‘categorical_crossentropy’, optimizer = Adam(learning_rate = 0.001), metrics = [‘accuracy’])

model.fit(X_train, y_train, epochs=10, batch_size=32)

test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)

print('\nAccuracy based on test data: ', test_acc)

Your code has several issues: incorrect quotation marks, misuse of fillna(), incorrect loss function choice, improper target variable encoding, and a mix of Dense and LSTM layers without a clear architecture.