import pandas as pd
import os
import requests

#Read in data using pandas
train_df = pd.read_csv(‘hourly_rate.csv’)

#Check data has been read in properly
print(train_df.head())

#Split the data into inputs and targets

#Create a dataframe with all training data except the target column
train_X = train_df.drop(columns=[‘wage_per_hour’])

#Check that the target variable has been removed
print(train_X.head())

#Create a dataframe with only the target column
train_y = train_df[[‘wage_per_hour’]]
print(train_y.head())

from keras.models import Sequential
from keras.layers import Dense

model = Sequential()

n_cols = train_X.shape[1]

model.add(Dense(250, activation=’relu’, input_shape=(n_cols,)))
model.add(Dense(10, activation=’relu’))
model.add(Dense(1))

model.compile(optimizer=’adam’, loss=’mean_squared_error’)
model.fit(train_X, train_y, validation_split=0.2, epochs=30)