Setting Up Python and Scikit-learn Environment

# CHAPTER 2

Setting Up Python and Scikit-learn Environment #

1. Introduction #

2. Learning Objectives #

• Install the latest version of Python on Windows, macOS, or Linux.

• Understand and create Python Virtual Environments.

• Install VS Code and the Jupyter Notebook extension.

• Use pip to install Scikit-learn, NumPy, and Pandas.

• Verify your installation with a simple test script.

3. Installing Python #

Windows:

1. 1. Go to python.org/downloads.

1. 2. Download the Windows installer.

1. 3. CRITICAL STEP: When you run the installer, you must check the box that says "Add Python to PATH" at the bottom of the window before clicking "Install Now".

macOS:

1. 1. macOS comes with Python pre-installed, but it is often an older version.

1. 2. The best way to install modern Python on a Mac is using Homebrew. Open your terminal and run: brew install python

Linux (Ubuntu/Debian): Open your terminal and run: sudo apt update sudo apt install python3 python3-pip python3-venv

4. Virtual Environments #

Let's create a folder for our course and set up an environment:

1. 1. Open your terminal/command prompt.

1. 2. Create a folder: mkdir ml_course and enter it: cd ml_course

1. 3. Create the virtual environment (named env):

• Windows: python -m venv env

• Mac/Linux: python3 -m venv env

1. 4. Activate the environment:

• Windows Command Prompt: env\Scripts\activate

• Windows PowerShell: .\env\Scripts\Activate.ps1

• Mac/Linux: source env/bin/activate

*You will know it worked because your terminal prompt will now start with (env).*

5. Installing the ML Libraries #

Run this command in your terminal:

pip install scikit-learn pandas numpy matplotlib jupyter

*Note: This will download and install the core libraries we need for machine learning, data manipulation, and visualization.*

6. Setting Up VS Code and Jupyter Notebooks #

1. 1. Download and install Visual Studio Code (VS Code) from code.visualstudio.com.

1. 2. Open VS Code and go to the Extensions tab (the squares icon on the left).

1. 3. Search for and install the Python extension (by Microsoft).

1. 4. Search for and install the Jupyter extension (by Microsoft).

7. Step-by-Step Implementation: Your First Notebook #

1. 1. In VS Code, open the ml_course folder you created earlier.

1. 2. Create a new file named test.ipynb. The .ipynb extension tells VS Code this is a Jupyter Notebook.

1. 3. Open test.ipynb. In the top right corner, click "Select Kernel" -> "Python Environments" -> and select the env virtual environment we created earlier.

1. 4. Type the following code into the first cell and click the "Play" button next to the cell to run it:

import sklearn
import pandas as pd
import numpy as np

print(f"Scikit-learn version: {sklearn.__version__}")
print(f"Pandas version: {pd.__version__}")
print("Environment setup successful! 🚀")

*If this prints the versions without an error, you are ready to go!*

8. Alternative Setup: Google Colab #

9. Common Mistakes #

• Forgetting to activate the virtual environment: If you open a new terminal tomorrow and run python script.py, you will get a ModuleNotFoundError because you forgot to run the activate command first.

• Installing globally using sudo pip on Mac/Linux: This can overwrite system Python libraries and break your operating system. Always use virtual environments.

10. Best Practices #

• requirements.txt: When you share your code, others need to know what libraries to install. You can automatically generate a list of your installed libraries by running pip freeze > requirements.txt.

11. Exercises #

1. 1. Open your terminal, create a new folder called ml_practice, create a virtual environment inside it, activate it, and install only numpy.

1. 2. Create a Jupyter Notebook, import numpy as np, and print np.array([1, 2, 3]).

12. MCQ Quiz with Answers #

13. Interview Questions #

• Q: Explain what pip is and how it relates to Scikit-learn.

• Q: What is the difference between writing Python code in a .py file versus a .ipynb (Jupyter Notebook) file?

14. FAQs #

15. Summary #

16. Next Chapter Recommendation #