import os import pandas as pd import numpy as np import matplotlib.pyplot as plt %matplotlib inline import seaborn as sns from tensorflow import keras import tensorflow as tf from tensorflow.keras.applications import * from tensorflow.keras.preprocessing.image import * from tensorflow.keras.optimizers import * from tensorflow.keras.models import * from tensorflow.keras.layers import * img_dim_ordering = "tf" from tensorflow.keras import backend as K #K.set_image_dim_ordering(img_dim_ordering) from tensorflow.keras.callbacks import ModelCheckpoint from tensorflow.keras.applications import Xception from tensorflow.keras import initializers from tensorflow.keras import datasets, layers, models from tensorflow.keras import regularizers from tensorflow.keras.layers import BatchNormalization from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from tensorflow.keras.layers.experimental import preprocessing from PIL import Image from PIL import ImageFile ImageFile.LOAD_TRUNCATED_IMAGES = True from scipy import ndimage from sklearn.metrics import f1_score from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.preprocessing import PolynomialFeatures from sklearn.metrics import mean_absolute_error from sklearn.preprocessing import MinMaxScaler

# อ่านข้อมูล train data = pd.read_csv('/work/DatasetA.csv') print('data = ', len(data))

data

data

data.info()

df = data df['Result'] = 0 df.loc[df[(df.Age >= 40) & (df['Car Segment'] == 'Eco Car') & (df.Occupation != 'Self Employee') & (df.Income >= 60000) & (df.Income <= 200000)].index,'Result'] = 1

count = 0 for i in df.Result: if i == 1: count = count + 1 count

df = data df['Result1'] = 0 df.loc[df[(df.Age >= 29) & (df.Age < 40) & (df['Car Segment'] == 'Eco Car') & (df.Occupation != 'Self Employee') & (df.Income >= 60000) & (df.Income <= 200000) & (df.Flag_Customer_NPL == 0)].index,'Result1'] = 1

count = 0 for i in df.Result1: if i == 1: count = count + 1 count

data['Result'] = df.Result+df.Result1 data = data.drop(['Result1'],axis = 1)

data