Please help me solve the problem

General Discussion
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#1

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)

#3

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.