text: import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  import seaborn as sbn
  
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import MinMaxScaler
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense
  from sklearn.metrics import mean_squared_error, mean_absolute_error
  from sklearn.metrics import classification_report, confusion_matrix
  
  data = pd.read_csv("EarthQs.csv")
  data2 = pd.read_csv("EarthQs.csv")
  print(data.describe())
  print(data.isnull().sum()) # checks null cells
  
  print('\n', data.corr()['Buyukluk'].sort_values()) # korelasyon -> data.corr()
  
  y_price = data['Buyukluk'].values
  x = data.drop('Buyukluk', axis = 1).values 
  xtr,xte,ytr,yte = train_test_split(x, y_price, test_size = 0.3, random_state = 30)
  
  scaler = MinMaxScaler()
  xtr = scaler.fit_transform(xtr)
  xte = scaler.fit_transform(xte)
  
  model = Sequential()
  model.add(Dense(8, activation="relu"))
  model.add(Dense(4, activation="relu"))
  model.add(Dense(4, activation="relu"))
  model.add(Dense(4, activation="relu"))
  model.add(Dense(1))
  
  model.compile(optimizer = 'adam', loss = 'mse')
  model.fit(x = xtr, y = ytr, validation_data=(xte, yte), epochs = 300, batch_size = 300)
  # otomatik xte ve yte karşılaştırması validate_data
  
  """ creating model """
  lossData = pd.DataFrame(model.history.history)
  #lossData.plot()
  
  predArr = model.predict(xte) 
  mae = mean_absolute_error(yte, predArr)
  print(mae, mae/data2['Buyukluk'].mean()) # meanabso/meanprice ile yüzdelikli fark bulunur fazlaysa sıkıntı var
  plt.scatter(yte, predArr)
  predicts = model.predict_classes(xte)
  print(confusion_matrix(yte, predicts))