Machine Learning Algorithms

A collection of machine learning implementations and practical exercises completed using Python and Jupyter Notebook. This repository demonstrates the complete workflow of building machine learning models, from data preprocessing to model evaluation.

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Repository Contents

• Exploratory Data Analysis (EDA)
• Data Preprocessing
• Regression
• Classification
• Clustering
• Genetic Algorithm

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Technologies

• Python
• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib
• Scikit-learn

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Repository Structure

notebooks/
│
├── 01_eda_preprocessing.ipynb
├── 02_regression.ipynb
├── 03_classification.ipynb
├── 04_clustering.ipynb
└── 05_genetic_algorithm.ipynb

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Topics Covered

Exploratory Data Analysis

• Data Cleaning
• Missing Values
• Feature Engineering
• Data Visualization

Regression

• Linear Regression
• Model Evaluation

Classification

• Classification Models
• Performance Metrics

Clustering

• Unsupervised Learning
• Cluster Analysis

Genetic Algorithm

• Evolutionary Optimization
• Selection
• Mutation
• Crossover

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Getting Started

Install the required libraries:

pip install notebook numpy pandas matplotlib scikit-learn

Launch Jupyter Notebook:

jupyter notebook

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Author

Muhammad Awais

• GitHub: https://github.com/devawais01
• LinkedIn: https://www.linkedin.com/in/dev-awais01

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License

This project is licensed under the MIT License.

See the LICENSE file for more information.