title: CNN for classification of numerical dataset in CSV file
body: <p>I am trying to apply a CNN on my numerical dataset from a CSV file, but I have problems with the dimensions. My Dataset consists of 26 Features/Columns and 1200 rows/samples. The dataset has 3 labels.</p>
  <pre><code>Dataset = pd.read_csv(&quot;...&quot;, header=0)
  features = ['...']
  x = Dataset [features]
  y = Dataset .Classifier
  sc = PowerTransformer()
  x = sc.fit_transform(x)
  
  x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.75)
  
  y_train = to_categorical(y_train)
  y_test = to_categorical(y_test)
  
  model = Sequential()
  model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
  model.add(MaxPooling1D(pool_size=4))
  model.add(LSTM(64))
  model.add(Dense(3, activation='softmax'))
  model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
  model.fit(x_train, y_train, epochs=100, batch_size=8, verbose=1)
  
  accuracy = model.evaluate(x_test, y_test)
  print(accuracy)
  </code></pre>
  <p>I get the following error:
  ValueError: Error when checking input: expected conv1d_1_input to have 3 dimensions, but got array with shape (900, 26)</p>
  <p>I am not sure how to reshape the data. As far as I know I only need a vector.</p>