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+[#]: subject: "Using a Machine Learning Model to Make Predictions"
+[#]: via: "https://www.opensourceforu.com/2022/05/using-a-machine-learning-model-to-make-predictions/"
+[#]: author: "Jishnu Saurav Mittapalli https://www.opensourceforu.com/author/jishnu-saurav-mittapalli/"
+[#]: collector: "lkxed"
+[#]: translator: " "
+[#]: reviewer: " "
+[#]: publisher: " "
+[#]: url: " "
+
+Using a Machine Learning Model to Make Predictions
+======
+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.
+
+![Machine-learning][1]
+
+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.
+
+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.
+
+I am taking the example of a flood prediction model. First, import the following libraries:
+
+```
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+```
+
+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.
+
+```
+from google.colab import files
+uploaded = files.upload()
+for fn in uploaded.keys(): print(‘User uploaded file “{name}” with length {length} bytes’.format(
+name=fn, length=len(uploaded[fn])))
+Choose files No file chosen
+```
+
+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.
+
+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.
+
+![Figure 1: Training the model][2]
+
+```
+from sklearn.linear_model import LinearRegression
+regressor = LinearRegression()
+regressor.fit(X_train, y_train)
+```
+
+Once that is done we have trained our model, and it’s now ready to make predictions, as shown in Figure 2.
+
+![Figure 2: Making predictions][3]
+
+### Using ML model in Java
+
+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:
+
+```
+# Install the library
+pip install sklearn2pmml
+```
+
+Once the library is installed we can convert our already trained model, as shown below:
+
+```
+sklearn2pmml(pipeline, ‘model.pmml’, with_repr = True)
+```
+
+This is it! We can now use the generated `model.pmml` file in our Java code to make predictions. Do try it out!
+
+（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。）
+
+--------------------------------------------------------------------------------
+
+via: https://www.opensourceforu.com/2022/05/using-a-machine-learning-model-to-make-predictions/
+
+作者：[Jishnu Saurav Mittapalli][a]
+选题：[lkxed][b]
+译者：[译者ID](https://github.com/译者ID)
+校对：[校对者ID](https://github.com/校对者ID)
+
+本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
+
+[a]: https://www.opensourceforu.com/author/jishnu-saurav-mittapalli/
+[b]: https://github.com/lkxed
+[1]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Machine-learning.jpg
+[2]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-1Training-the-model.jpg
+[3]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-2-Making-predictions.jpg
+[4]: https://github.com/jpmml/jpmml-evaluator