import app
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras import layers
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.optimizers import SGD
from sklearn.compose import ColumnTransformer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import Normalizer
from sklearn.metrics import r2_score
dataset = pd.read_csv(‘admissions_data.csv’)
feature = dataset.iloc[:,0:-1]
label =dataset.iloc[:,-1]
feature_train,feature_test,label_train,label_test = train_test_split (feature,label,test_size=0.2,random_state=43)
columns = feature.columns
ct = ColumnTransformer ([(‘only numeric’,StandardScaler(),columns)],remainder=‘passthrough’)
feature_train_scaled = ct.fit_transform(feature_train)
feature_test_scaled = ct.transform(feature_test)
model= Sequential()
input = layers.InputLayer(input_shape = (feature.shape[1]))
model.add(input)
model.add(layers.Dense(64,activation=‘relu’))
model.add(layers.Dense(1))
opt= SGD(learning_rate=0.1)
model.compile(loss=‘mse’,metrics=‘mae’,optimizer=opt)
es = EarlyStopping(monitor=‘val_loss’,mode=‘min’,verbose=1,patience=20)
model.fit(feature_train_scaled,label_train, epochs = 20 ,batch_size=4, verbose = 1, )
res_mse,res_mae = model.evaluate(feature_test_scaled,label_test,verbose=0)
print(res_mse,res_mae)