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:
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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