Python机器学习项目

让我们从安装 Python 模块 Scikit-learn 开始，它是 Python 中最好且文档最完善的机器学习库之一。

为了开始我们的编码项目，请激活你的 Python 3 编程环境。确保你位于环境所在的目录中，并运行以下命令：

. my_env/bin/activate

激活编程环境后，检查 Scikit-learn 模块是否已安装：

(my_env) $ python -c "import sklearn"

如果 sklearn 已安装，此命令将成功完成，不会出现错误。如果未安装，你将看到以下错误消息：

Output
Traceback (most recent call last): File "<string>", line 1, in <module>
ImportError: No module named 'sklearn'

错误消息表明 sklearn 未安装，因此使用 pip 下载该库：

(my_env) $ pip install scikit-learn[alldeps]

安装完成后，启动 Jupyter Notebook：

(my_env) $ jupyter notebook

在 Jupyter 中，创建一个名为 ML Tutorial 的新 Python Notebook。在 Notebook 的第一个单元格中，导入 sklearn 模块：

ML Tutorial

import sklearn

你的 Notebook 应该如下图所示：

Jupyter Notebook，包含一个 Python 单元格，导入了 sklearn

现在我们已经在 Notebook 中导入了 sklearn，我们可以开始处理用于机器学习模型的数据集了。

Step 1 — Importing Scikit-learn
Let’s begin by installing the Python module Scikit-learn, one of the best
and most documented machine learning libraries for Python.
To begin our coding project, let’s activate our Python 3 programming
environment. Make sure you’re in the directory where your environment
is located, and run the following command:
. my_env/bin/activate
With our programming environment activated, check to see if the
Sckikit-learn module is already installed:
(my_env) $ python -c "import sklearn"
If sklearn is installed, this command will complete with no error. If it
is not installed, you will see the following error message:
Output
Traceback (most recent call last): File "<string>", line 1, in <module>
ImportError: No module named 'sklearn'
The error message indicates that sklearn
 is
 not installed, so
download the library using pip:
(my_env) $ pip install scikit-learn[alldeps]
Once the installation completes, launch Jupyter Notebook:
(my_env) $ jupyter notebook
In Jupyter, create a new Python Notebook called ML Tutorial. In the
first cell of the Notebook, import the sklearn module:
ML Tutorial
import sklearn
Your notebook should look like the following figure:
Jupyter Notebook with one Python cell, which imports sklearn
Now that we have sklearn imported in our notebook, we can begin
working with the dataset for our machine learning model.