Using conv1D “Error when checking input: expected conv1d_input to have 3 dimensions, but got array with shape (213412, 36)”

My input is simply a csv file with 237124 rows and 37 columns :

• The first 36 columns as features

• The last column is a Binary class label

I am trying to train my data on the conv1D model.

I have tried to build a CNN with one layer, but I have some problems with it.

The compiler outputs:

ValueError:Error when checking input: expected conv1d_9_input to have shape (213412, 36) but got array with shape (36, 1)

Code:

import pandas as pd
import numpy as np
import sklearn
from sklearn import metrics
from sklearn.model_selection import KFold
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import StandardScaler
import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.layers import Conv2D,Conv1D, MaxPooling2D,MaxPooling1D
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Dropout,BatchNormalization

dataset=pd.read_csv("C:/Users/User/Desktop/data.csv",encoding='cp1252')

dataset.shape
#output: (237124, 37)

array = dataset.values
X = array[:,0:36]
Y = array[:,36]

kf = KFold(n_splits=10)
kf.get_n_splits(X)

for trainindex, testindex in kf.split(X):
Xtrain, Xtest = X[trainindex], X[testindex]
Ytrain, Ytest = Y[trainindex], Y[testindex]

Xtrain.shape[0]
#output: 213412

Xtrain.shape[1]
#output: 36

Ytrain.shape[0]
#output: 213412

n_timesteps, n_features, n_outputs =Xtrain.shape[0], Xtrain.shape[1], 
Ytrain.shape[0]

model = Sequential()
model.add(Conv1D(filters=64, kernel_size=1, 
activation='relu',input_shape=(n_timesteps,n_features)))

model.add(Conv1D(filters=64, kernel_size=1, activation='relu'))
model.add(Dropout(0.5))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dense(n_outputs, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=  
['accuracy'])
# fit network
model.fit(Xtrain, Ytrain, epochs=10, batch_size=32, verbose=0)

# Testing CNN model BY X test

Predictions = model.predict(Xtest,batch_size =100)
rounded = [round(x[0]) for x in Predictions]
Y_predection = pd.DataFrame(rounded)
Y_predection = Y_predection.iloc[:, 0]

.
.
.

I tried to modify the code this way:

Xtrain = np.expand_dims(Xtrain, axis=2) 

But the error remains the same.