skklearn

An educational source-code library of ten Python machine learning and data analysis syllabus programmes.

Disclaimer: skklearn is an independent educational package and is not affiliated with or endorsed by scikit-learn.

🎯 Purpose

skklearn allows students and lab instructors to install the package, view the exact original source code of any syllabus programme directly in Python IDLE or the terminal, and copy it into a new .py file to work with in the lab.

Key Design Principles:

• Source Code Library: Designed to display and copy unmodified source code, not automatically execute it.
• Zero Bundled Datasets: No CSV files or datasets are included in the package.
• Exact Code Preservation: All 10 original programmes are preserved byte-for-byte with their original variable names, logic, and file paths.

💻 Installation

Install skklearn using pip:

py -m pip install skklearn

Or install locally from the built wheel:

py -m pip install dist/skklearn-0.1.0-py3-none-any.whl

🚀 How to Use in the College Lab / Python IDLE

Step 1: Open Python IDLE or Interactive Shell

Import skklearn and call show_code(program_number):

import skklearn

# Display Programme 1 (Find-S Algorithm)
skklearn.show_code(1)

The exact source code will print directly in your IDLE Shell.

Step 2: Copy Code into a New File

1. In Python IDLE, highlight and copy the printed code.
2. Select File > New File (Ctrl + N).
3. Paste the code into your new editor window.
4. Save the file (e.g., lab_prog1.py).

Step 3: Set Up Required Datasets (If Applicable)

For programmes that use external CSV files, create the dataset at the path hardcoded in the original syllabus programme (see table below).

Step 4: Run the Programme

Press F5 (or Run > Run Module) in IDLE to execute your script.

📋 Syllabus Programmes & Dataset Requirements

🛡️ Error Handling

Passing an invalid programme number (outside the range 1 to 10) raises a clear ValueError:

skklearn.show_code(15)
# ValueError: Invalid programme number '15'. Please choose a number from 1 to 10.

📦 Dependencies

The package declares the third-party libraries needed when you run the copied syllabus programmes:

• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn
• scipy
• statsmodels