!pip install h2o

!pip install dabl

import pandas as pd import numpy as np import matplotlib.pyplot as plt from dabl import plot

train = pd.read_excel("/content/Data_Train.xlsx") train.head()

test = pd.read_excel("/content/Test_set.xlsx") test.head()

plot(train,'Airline') plt.show()

plot(train,'Price') plt.show()

plot(train,'Source') plt.show()

plot(train,'Destination') plt.show()

train.to_csv("C:\\Users\\Lulus\\Deck\\Desktop\\MachineHack\\Flight_Ticket_Participant_Datasets\\train_csv.csv")

test.to_csv("C:\\Users\\Lulus\\Deck\\Desktop\\MachineHack\\Flight_Ticket_Participant_Datasets\\test_csv.csv")

import h2o from h2o.automl import H2OAutoML # Start the H2O cluster (locally) h2o.init()

# Import a sample binary outcome train/test set into H2O train = h2o.import_file("C:\\Users\\Lulus\\Deck\\Desktop\\MachineHack\\Flight_Ticket_Participant_Datasets\\train_csv.csv") test = h2o.import_file("C:\\Users\\Lulus\\Deck\\Desktop\\MachineHack\\Flight_Ticket_Participant_Datasets\\test_csv.csv")

preds = aml.leader.predict(test)

preds.columns=['Price']

preds.head()

submission = preds.as_data_frame(use_pandas=True)

submission.info()

submission.to_csv("C:\\Users\\Lulus\\Deck\\Desktop\\MachineHack\\Flight_Ticket_Participant_Datasets\\submission.csv")