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Lab to create a simple Python application using H2O, including loading a sample dataset, training a model, and obtaining an output example.

Step-by-step instructions to create a simple Python application using H2O, including loading a sample dataset, training a model, and obtaining an output example.

Lab Workbook: Building a Python Application with H2O

Introduction:

In this lab, we will create a Python application using the H2O library. H2O is a powerful machine learning platform that provides APIs to run data science operations. H2O gives us the tooling to build and deploy machine learning models. We will walk through the steps of:

loading a sample dataset

training a model

obtaining a prediction output.

Prerequisites:

Python installed on your machine.

H2O Python library installed (pip install h2o).

Basic understanding of Python and machine learning concepts.

Step 1: Import the Required Libraries

Open your favorite Python IDE or text editor.

Create a new Python file, e.g., h2o_application.py.

Import the necessary libraries by adding the following lines of code at the beginning of your file:

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import h2o

from h2o.estimators import H2OGradientBoostingEstimator

Step 2: Initialize H2O and Load the Dataset

Initialize the H2O library by adding the following code:

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h2o.init()

Download the sample dataset from [URL] and save it in your project directory.

Load the dataset into an H2OFrame object by adding the following code:

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data_frame = h2o.import_file("/path/to/your/dataset.csv")

Replace /path/to/your/dataset.csv with the actual path to the downloaded dataset file.

Step 3: Explore the Dataset

To get a summary of the dataset, use the describe() function as follows:

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data_frame.describe()

This will provide descriptive statistics for each column in the dataset.

Step 4: Split the Dataset into Training and Testing Sets

Split the dataset into training and testing sets by adding the following code:

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train, test = data_frame.split_frame(ratios=[0.8], seed=42)

This will create two H2OFrame objects: train for training data and test for testing data.

We're splitting the data with an 80:20 ratio, and setting the random seed to ensure reproducibility.

Step 5: Train a Gradient Boosting Model

Create an instance of the H2OGradientBoostingEstimator class by adding the following code:

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model = H2OGradientBoostingEstimator()

Train the model using the training data by adding the following code:

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model.train(x=data_frame.columns[:-1], y=data_frame.columns[-1], training_frame=train)

*** Replace data_frame.columns[:-1] with the list of feature column names and data_frame.columns[-1] with the target column name.

Step 6: Evaluate the Model

To evaluate the model's performance on the test data, use the model.model_performance() function as follows:

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performance = model.model_performance(test_data=test)

print(performance)

This will print the evaluation metrics such as accuracy, precision, recall, etc.

Step 7: Make Predictions

To make predictions on new data, use the model.predict() function as follows:

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predictions = model.predict(test)

print(predictions)

This will print the predicted values for the test data.

Step 8: Save the Model

To save the trained model for future use, use the save_mojo() function as follows:

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model.save_mojo("/path/to/save/model.mojo")

Replace /path/to/save/model.mojo with the desired location and name of the MOJO file.

Conclusion:

Congratulations! You have successfully built a Python application using H2O, loaded a sample dataset, trained a model, evaluated its performance, made predictions, and saved the model.

This serves as a basic example to get you started with H2O and Python for machine learning tasks.

Note: Remember to update the file paths and customize the code according to your specific dataset and requirements.

dataset.csv

Here's an example of a dataset in CSV format that you can use for the lab:

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feature1,feature2,feature3,target

1.2,3.4,5.6,0

2.3,4.5,6.7,1

3.4,5.6,7.8,0

4.5,6.7,8.9,1

5.6,7.8,9.0,0

6.7,8.9,1.2,1

7.8,9.0,2.3,0

8.9,1.2,3.4,1

9.0,2.3,4.5,0

In this example dataset, there are four columns: feature1, feature2, feature3, and target. The first three columns represent the features or inputs, while the last column (target) represents the target variable or output. Each row represents an individual data instance.

Feel free to modify the values or add more rows/columns to the dataset as needed for your lab. Remember to save this dataset as a CSV file and provide the correct path when loading it in the Python code.

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