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[#]: subject: "Using a Machine Learning Model to Make Predictions"
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[#]: via: "https://www.opensourceforu.com/2022/05/using-a-machine-learning-model-to-make-predictions/"
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[#]: author: "Jishnu Saurav Mittapalli https://www.opensourceforu.com/author/jishnu-saurav-mittapalli/"
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[#]: collector: "lkxed"
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[#]: translator: "geekpi"
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[#]: reviewer: " "
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[#]: publisher: " "
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[#]: url: " "
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Using a Machine Learning Model to Make Predictions
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======
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Machine learning is basically a subset of artificial intelligence that uses previously existing data to make a prediction on new data. Of course, all of us know this by now! This article demonstrates how a machine learning model developed in Python can be used as a part of a Java code to make predictions.
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![Machine-learning][1]
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This article assumes you are familiar with the basic development skills and understanding of machine learning. We will start with training our model, and then make a machine learning model in Python.
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This article assumes you are familiar with the basic development skills and understanding of machine learning. We will start with training our model, and then make a machine learning model in Python.
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I am taking the example of a flood prediction model. First, import the following libraries:
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```
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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```
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Once we have successfully imported the libraries, we need to take in the data sets, as shown in the code below. To predict floods, I am using the river level data set.
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```
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from google.colab import files
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uploaded = files.upload()
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for fn in uploaded.keys(): print(‘User uploaded file “{name}” with length {length} bytes’.format(
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name=fn, length=len(uploaded[fn])))
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Choose files No file chosen
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```
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The upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable*Saving Hoppers Crossing-Hourly-River-Level.csv to Hoppers Crossing-Hourly-River-Level.csv User uploaded file “Hoppers Crossing-Hourly-River-Level.csv”* with length 2207036 bytes.
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Once this is done, we can train our model using the *sklearn library*. For this, we first need to import the library and the algorithm model, as shown in Figure 1.
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![Figure 1: Training the model][2]
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```
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from sklearn.linear_model import LinearRegression
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regressor = LinearRegression()
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regressor.fit(X_train, y_train)
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```
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Once that is done we have trained our model, and it’s now ready to make predictions, as shown in Figure 2.
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![Figure 2: Making predictions][3]
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### Using ML model in Java
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What we need to do now is to convert the ML model into a model that can be used by a Java program. There is a library called sklearn2pmml that helps us do this:
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```
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# Install the library
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pip install sklearn2pmml
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```
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Once the library is installed we can convert our already trained model, as shown below:
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```
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sklearn2pmml(pipeline, ‘model.pmml’, with_repr = True)
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```
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This is it! We can now use the generated `model.pmml` file in our Java code to make predictions. Do try it out!
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（LCTT 译注：Java 中有第三方库 [jpmml/jpmml-evaluator][4]，它能帮助你使用生成的 `model.pmml` 进行预测。）
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--------------------------------------------------------------------------------
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via: https://www.opensourceforu.com/2022/05/using-a-machine-learning-model-to-make-predictions/
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作者：[Jishnu Saurav Mittapalli][a]
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选题：[lkxed][b]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://www.opensourceforu.com/author/jishnu-saurav-mittapalli/
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[b]: https://github.com/lkxed
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[1]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Machine-learning.jpg
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[2]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-1Training-the-model.jpg
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[3]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-2-Making-predictions.jpg
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[4]: https://github.com/jpmml/jpmml-evaluator
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@ -0,0 +1,87 @@
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[#]: subject: "Using a Machine Learning Model to Make Predictions"
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[#]: via: "https://www.opensourceforu.com/2022/05/using-a-machine-learning-model-to-make-predictions/"
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[#]: author: "Jishnu Saurav Mittapalli https://www.opensourceforu.com/author/jishnu-saurav-mittapalli/"
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[#]: collector: "lkxed"
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[#]: translator: "geekpi"
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[#]: reviewer: " "
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[#]: publisher: " "
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[#]: url: " "
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使用机器学习模型进行预测
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======
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机器学习基本上是人工智能的一个子集，它使用以前存在的数据对新数据进行预测。当然，现在我们所有人都知道这个道理了！这篇文章展示了如何将 Python 中开发的机器学习模型作为 Java 代码的一部分来进行预测。
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![Machine-learning][1]
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本文假设你熟悉基本的开发技巧并理解机器学习。我们将从训练我们的模型开始，然后在 Python 中制作一个机器学习模型。
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我以一个洪水预测模型为例。首先，导入以下库：
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```
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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```
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当我们成功地导入了这些库，我们就需要输入数据集，如下面的代码所示。为了预测洪水，我使用的是河流水位数据集。
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```
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from google.colab import files
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uploaded = files.upload()
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for fn in uploaded.keys(): print(‘User uploaded file “{name}” with length {length} bytes’.format(
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name=fn, length=len(uploaded[fn])))
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Choose files No file chosen
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```
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只有在当前浏览器会话中执行了该单元格时，上传部件才可用。请重新运行此单元，上传文件 *“Hoppers Crossing-Hourly-River-Level.csv”*，大小 2207036 字节。
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完成后，我们就可以使用 *sklearn 库*来训练我们的模型。为此，我们首先需要导入该库和算法模型，如图 1 所示。
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![Figure 1: Training the model][2]
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```
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from sklearn.linear_model import LinearRegression
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regressor = LinearRegression()
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regressor.fit(X_train, y_train)
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```
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完成后，我们就训练好了我们的模型，现在可以进行预测了，如图 2 所示。
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![Figure 2: Making predictions][3]
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### 在 Java 中使用 ML 模型
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我们现在需要做的是把 ML 模型转换成一个可以被 Java 程序使用的模型。有一个叫做 sklearn2pmml 的库可以帮助我们做到这一点：
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```
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# Install the library
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pip install sklearn2pmml
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```
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库安装完毕后，我们就可以转换我们已经训练好的模型，如下图所示：
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```
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sklearn2pmml(pipeline, ‘model.pmml’, with_repr = True)
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```
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这就完成了！我们现在可以在我们的 Java 代码中使用生成的 `model.pmml` 文件来进行预测。请试一试吧！
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（LCTT 译注：Java 中有第三方库 [jpmml/jpmml-evaluator][4]，它能帮助你使用生成的 `model.pmml` 进行预测。）
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--------------------------------------------------------------------------------
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via: https://www.opensourceforu.com/2022/05/using-a-machine-learning-model-to-make-predictions/
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作者：[Jishnu Saurav Mittapalli][a]
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选题：[lkxed][b]
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译者：[geekpi](https://github.com/geekpi)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://www.opensourceforu.com/author/jishnu-saurav-mittapalli/
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[b]: https://github.com/lkxed
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[1]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Machine-learning.jpg
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[2]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-1Training-the-model.jpg
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[3]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-2-Making-predictions.jpg
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[4]: https://github.com/jpmml/jpmml-evaluator
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