Scikit-learn and pandas based on Windows stand-alone machine learning environment

Many friends want to learn machine learning, but suffer from the construction of the environment, here is the Windows Scikit-learn Research and development environment to build steps.

Step 1. Installation of Python

Python has versions of 2.x and 3.x, but many good machine learning Python libraries do not support 3.x, so it is recommended to install version 2.7 of Python. Currently the latest Python is 2.7.12. Links are as follows:

https://www.python.org/downloads/release/python-2712/

You can see the 32-bit version and the 64-bit version. If your machine is a 64-bit version, then either the 32-bit or the 64-bit version of the installation is optional. If the machine is a 32-bit version, you can only install the 32-bit version. If you can't figure out the number of bits in your machine, then install the 32-bit version. That is, "Windows x86 MSI Installer".

Windows x86-64 MSI Installer	Windows	For amd64/em64t/x64, not Itanium processors	8fa13925db87638aa472a3e794ca4ee3	19820544	Sig
Windows x86 MSI Installer	Windows		fe0ef5b8fd02722f32f7284324934f9d	18907136	Sig

After installation, you can set the environment variables, add Python directory to path, such as my python loaded in C:\Python27, then I add C:\Python27\Scripts and C:\Python27 to the environment variable. Of course, not to add. This adds Python's full path name each time you use Python.

After the installation is complete, enter Python on the command line of Windows, if you can come up with Python's basic information stating that the installation was successful.

Step 2. Installation of the Python Package management tool PIP

We need the package management tool to facilitate the installation of the Python library, there are many package management tools, I am accustomed to the use of the PIP.

Download the PIP installation script. The links are as follows. Download get-pip.py. Then go to your download directory and enter "Python get-pip.py" on the command line to finish the installation.

https://pip.pypa.io/en/stable/installing/

After downloading, remember to run this command "Pip install-u Pip", one is to see if PIP can work properly, and the second is to upgrade the PIP to the latest version.

Step 3. InstallationVisual C + + Compiler for Python

Links in this: https://www.microsoft.com/en-us/download/details.aspx?id=44266

This does not fit behind a lot of scientific calculations will not fit.

Step 4. Installing NumPy and SciPy

These two buddies are essential tools for scientific computing and matrix operations.

Because NumPy and scipy directly with PIP installation often have a variety of problems, it is generally recommended to download the offline version of the WHL to install NumPy and scipy.

First install the offline version of the NumPy, here I generally download the link below NumPy, of course scipy is also in this.

http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy

You can see that there are many versions of numpy available to download, our Python is 2.7,windows 32-bit, so download "NUMPY-1.11.2+MKL-CP27-CP27M-WIN32.WHL"

After downloading, go to the download directory and run "Pip install NUMPY-1.11.2+MKL-CP27-CP27M-WIN32.WHL" on the command line so that the NumPy is installed successfully.

Install the scipy in the same way. Download scipy on the link below.

http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy

Our Python is 2.7,windows 32-bit, so choose SCIPY-0.18.1-CP27-CP27M-WIN32.WHL Download.

Run "Pip Install SCIPY-0.18.1-CP27-CP27M-WIN32.WHL"

So NumPy and scipy two good base friends will be done.

Step 4. Installing Matplotlib,pandas and Scikit-learn

There's nothing to say, just run the following command at the command line. Note that installing matplotlib before installing pandas

Pip Install-u matplotlib

　　　　　

Pip Install-u jinja2

Pip Install-u Jsonschema

Pip Install-u pyzmq

　　　　Pip install-u pandas

　　　　Pip Install -U scikit-Learn

Step 5: Install Ipython and Ipython notebook

Ipython Notebook is the most commonly used Python interactive learning tool, of course, now called Jupyter Notebook. The Scikit-learn official example gives a version that runs with Ipython notebook.

The installation method is simple:

Pip Install Ipython

Pip Install Jupyter

Official website in this: http://ipython.org/notebook.html

After installation, enter "jupyter notebook" or "Jupyter-notebook" at the command line, and the output will prompt you notebook to run thehttp://localhost:8888

Step 6. Hello world! Try running a scikit-learn machine learning program

Download a machine Learning example on Scikit-learn's website, such as: HTTP://SCIKIT-LEARN.ORG/STABLE/_DOWNLOADS/PLOT_CV_PREDICT.IPYNB

Then run "jupyter Notebook" in the download directory, then the browser opens http://localhost:8888 .

You can see the contents of your download directory in the browser, we open the newly downloaded plot_cv_ PREDICT.IPYNB This file link, you can see the contents of the Python program, then we can point to the above triangle button, step by step running the program, if there is no error, we can finally see a linear regression prediction diagram.

This program can be modified to run a step further, to achieve the purpose of research and learning.

The above is the construction process of scikit-learn and pandas environment. Hope that we can build success, to study machine learning.

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Scikit-learn and pandas based on the Windows stand-alone machine learning environment