📊 Data Mining & Machine Learning

PUCIT 5th Semester Coursework — Python, NumPy & ML from the Ground Up

Browse Assignments • Getting Started • Report an Issue

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📋 Table of Contents

• About
• Assignments
• Tech Stack
• Getting Started
  • Prerequisites
  • Installation
  • Running the Notebooks
• Project Structure
• Roadmap
• Contributing
• License
• Contact

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🎯 About

This repository tracks my coursework for Data Mining & Machine Learning, a core course in the 5th semester of my Data Science degree at PUCIT. It collects weekly and periodic assignments as Jupyter notebooks, progressing from Python fundamentals toward data mining and machine learning techniques such as preprocessing, classification, regression, and clustering.

• 📚 Learning in public — every assignment is committed as it's completed, in order.
• 🧪 Hands-on notebooks — runnable code with explanations, not just theory.
• 📈 Growing scope — starts with Python/NumPy foundations and builds toward full ML workflows.

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📂 Assignments

#	Folder	Topics Covered	Status
01	Assignment_01/	Python basics, conditionals, iteration, functions, strings, files, lists, dictionaries, tuples, NumPy	✅ Complete

This table is updated as new assignments are added throughout the semester.

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🛠️ Tech Stack

Category	Technologies
Language
Environment
Core Libraries	(more added as the course progresses: pandas, scikit-learn, matplotlib)

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🚀 Getting Started

Prerequisites

• ✅ Python 3.9+
• ✅ Jupyter Notebook or JupyterLab (or the Jupyter extension for VS Code)
• ✅ Git

Installation

1️⃣ Clone the repository

git clone https://github.com/ZohaibCodez/data-mining-machine-learning.git
cd data-mining-machine-learning

2️⃣ Set up a virtual environment (recommended)

python -m venv venv
# Windows
venv\Scripts\activate
# macOS/Linux
source venv/bin/activate

3️⃣ Install common dependencies

pip install jupyter numpy pandas matplotlib scikit-learn

Running the Notebooks

jupyter notebook

Then open any assignment folder (e.g. Assignment_01/) and launch the .ipynb file you want to explore.

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🗂️ Project Structure

data-mining-machine-learning/
├── 📁 Assignment_01/        # Python fundamentals & NumPy
│   ├── 📓 Lecture 1.ipynb
│   ├── 📓 Lecture 2 Conditional execution.ipynb
│   ├── 📓 ...
│   └── 📄 output.txt
├── 📄 README.md
└── 📄 .gitignore

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🗺️ Roadmap

• ✅ Assignment 01 — Python fundamentals & NumPy
• 📅 Data preprocessing & cleaning
• 📅 Classification algorithms
• 📅 Regression algorithms
• 📅 Clustering techniques
• 📅 Final project

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🤝 Contributing

This is a personal coursework repository, so it isn't open for external contributions. That said, feel free to:

• ⭐ Star the repo if you find it useful for learning
• 🐛 Open an issue if you spot an error in a notebook
• 🍴 Fork it for your own coursework reference

See CONTRIBUTING.md for details, and please follow the CODE_OF_CONDUCT.md.

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📄 License

Distributed under the MIT License. See LICENSE for more information.

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📧 Contact

Muhammad Zohaib Khan (@ZohaibCodez)

Project Link: https://github.com/ZohaibCodez/data-mining-machine-learning

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Data Mining & Machine Learning • Built with 🖤 at PUCIT

⭐ Star this repo if you find it helpful!