Mushroom Classification Project part 1 — Project Environment and Workflow Setup
In this article, we will be getting our project environment and workflow set up. For understanding what machine learning is, check out my previous article
First, download the Miniconda Installer. While choosing the installer, do so according to you computer OS and bit specification (32-bit or 64-bit). Make sure you install a python 3.x installer and not python 2.7 installer.
Next, run the Miniconda installer and click “Next” and “Agree”.
Then, proceed with the default options for both the “Install for:” page and the “Destination location” page.
Check the “Anaconda as my default Python 3.x” and click “Install”.
The Installation will take a couple of minutes and a new page will be displayed. Click “Finish” to complete the installation.
Setting up a project environment (For Windows)
Search for “Anaconda Prompt (miniconda3)”
Double-click on it to start it. The prompt will look something like this:
Make a project folder with
mkdir project/folder/path and change directory into it.
To create a conda environment with all of the needed packages, type:
conda create --prefix ./env pandas numpy matplotlib seaborn scikit-learn jupyter . This create an environment folder within the project folder called “env”. The env folder will have the following packages installed along with their dependencies:
- Pandas — a library for data analysis
- NumPy — a library for data manipulation
- Matplotlib — a library for data visualisation
- Seaborn — a library for data visualisation built on top of matplotlib
- Scikit-learn — a library for creating, training, evaluating and improving machine learning models
- Jupyter — a library to launch Python Notebooks, where we will be writing our code.
Conda will list the packages that will be installed and ask for permission to continue. Enter “y”
after the packages are installed and the environment is created, we can activate it using
conda activate env/file/path .
jupyter notebook in the prompt to open up something like this in your browser:
Click “New” and “Python 3” to create a new notebook.
Click on Untitled and rename the notebook to whatever you want.
And there it is! Our very own Jupyter Notebook.
Alright. This is it for this article. In the next one, we will actually start writing machine learning code.
See you in Part 2!