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Objective

The challenge is to create a model that uses data from the first 24 hours of intensive care to predict patient survival. MIT's GOSSIS community initiative, with privacy certification from the Harvard Privacy Lab, has provided a dataset of more than 130,000 hospital Intensive Care Unit (ICU) visits from patients, spanning a one-year timeframe. This data is part of a growing global effort and consortium spanning Argentina, Australia, New Zealand, Sri Lanka, Brazil, and more than 200 hospitals in the United States.

Data Description

MIT's GOSSIS community initiative, with privacy certification from the Harvard Privacy Lab, has provided a dataset of more than 130,000 hospital Intensive Care Unit (ICU) visits from patients, spanning a one-year timeframe. This data is part of a growing global effort and consortium spanning Argentina, Australia, New Zealand, Sri Lanka, Brazil, and more than 200 hospitals in the United States.

The data includes:

Training data for 91,713 encounters.
Unlabeled test data for 39,308 encounters, which includes all the information in the training data except for the values for hospital_death.
WiDS Datathon 2020 Dictionary with supplemental information about the data, including the category (e.g., identifier, demographic, vitals), unit of measure, data type (e.g., numeric, binary), description, and examples.
Sample submission files

H2O :

H2O is ‘the open source in-memory, prediction engine for Big Data science’. H2O is a feature-rich, open source machine learning platform known for its R and Spark integration and its ease of use. It is a Java virtual machine that is optimised for doing in-memory processing of distributed, parallel machine learning algorithms on clusters.

The motive of H2O is to provide a platform which made easy for the non-experts to do experiments with machine learning.H2O architecture can be divided into different layers in which the top layer will be different APIs, and the bottom layer will be H2O JVM.

# importing libraries
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.impute import SimpleImputer
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier ,AdaBoostClassifier
from sklearn.model_selection import train_test_split
import lightgbm as lgb
from sklearn import preprocessing

Starting H2O and Inspecting the Cluster

There are many tools for directly interacting with user-visible objects in the H2O cluster. Every new python session begins by initializing a connection between the python client and the H2O cluster.The h2o.init() function to initialize H2O.