mlcode 0.1.1

A collection of 12 educational Machine Learning experiments for learning and demonstration.

🤖 MLCode Library

A professional, modular Python library designed for educational machine learning demonstrations. This package contains 10 structured experiments covering various core ML algorithms and Python concepts, complete with an interactive command-line interface to inspect source code and execute models dynamically.

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✨ Features

• 🎓 Interactive Learning: Instantly view the source code of any experiment with a single call (ip.exp1()).
• 🚀 Dynamic Execution: Run any machine learning experiment and view its plots/outputs instantly (ip.run1()).
• 📂 Clean Module Layout: Designed using standard Python packaging best practices with automatic dependency discovery.
• ⚡ Hot Reloading: Easily install in editable mode for developers.

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📦 Installation

1. Local Development (Editable Mode)

To install the package locally so that any updates to the experiment files are immediately available:

cd "c:\Users\Himeshwar\Desktop\mlcodes\mlcode"
pip install -e .

2. Standard Global Installation

To install the package permanently on your machine:

pip install .

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🛠️ Usage

Open a Jupyter Notebook, a VS Code Interactive Window, or a standard Python terminal and run:

import mlcode as ip

# 1. List all available experiments
ip.show()

# 2. View the source code of Experiment 1
ip.exp1()

# 3. Dynamically run Experiment 1 and show the plot
ip.run1()

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📚 Educational Experiments Included

Here are the 10 experiments included in this library and what they demonstrate:

Experiment	Focus Area / Algorithm	Description	Python Concept Shown
exp1	📈 Linear Regression	Univariate salary prediction model.	Basic Arithmetic & Imports
exp2	🛢️ LSM Regression	Fuel consumption least squares prediction model.	String Manipulation
exp3	🤖 Machine Learning	Basic model training loops and data iteration.	Loops
exp4	⚖️ Conditionals	Data thresholding and model routing logic.	If-Else Conditions
exp5	🧱 Modular ML	Function-wrapped model training pipeline.	Functions
exp6	🌳 Decision Trees	Classification and tree plotting on a custom dataset.	Lists
exp7	🌀 Dimensionality Reduction	Principal Component Analysis (PCA) visualization.	Dictionaries
exp8	🛡️ Robust Pipelines	Safe training wrapper with input error validation.	Error Handling
exp9	🏷️ KMeans Clustering	Unsupervised K-Means clustering algorithm.	List Comprehensions
exp10	🏗️ Class-based ML	Object-Oriented Random Forest / Classifier.	Basic Classes

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🚀 Publishing Globally

1. Build Package Distributions

Build the standard Wheel (.whl) and Source Archive (.tar.gz):

pip install build
python -m build

2. Upload to PyPI (Python Package Index)

Publish the compiled build artifacts so anyone in the world can run pip install mlcode:

pip install twine
python -m twine upload dist/*

(Note: If the name mlcode is already taken on PyPI, rename the name field in setup.py to something unique like mlcode-himeshwar and rebuild!)

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.