Implememtation of machine learning algorithms using Python libraries

This repository contains implementation code for basic machine learning algorithms in Python. The primary goal is to provide clear, beginner-friendly examples of core ML techniques using Jupyter Notebooks. Each notebook explains the intuition, workings, and practical application of different algorithms.

Purpose

• Educational Resource: Learn the fundamental concepts of machine learning by studying and running real code.
• Reference Implementations: Use simple, readable Python examples as a starting point for your own projects or studies.
• Experimentation: Modify the notebooks to see how changes affect results and deepen your understanding.

Contents

• Supervised Algorithms: Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines, k-Nearest Neighbors, etc.
• Unsupervised Algorithms: k-Means Clustering, Principal Component Analysis (PCA), etc.
• Additional Concepts: Data preprocessing, model evaluation, visualization techniques.

Each algorithm is presented in its own Jupyter Notebook with explanations, code, and sample outputs. The data used to implement the algorithms is placed in the data folder.

How to Run

1. Clone this repository:

   git clone https://github.com/SujataSaurabh/ML-Algorithms-in-Python.git

2. Install dependencies:

   • Make sure you have Python 3.x installed.
   • Install Jupyter Notebook and required libraries (like numpy, pandas, scikit-learn, matplotlib):

     pip install jupyter numpy pandas scikit-learn matplotlib

3. Start Jupyter Notebook:

   jupyter notebook

   Then open the desired notebook from the repository.

4. Run cells: Step through each cell to execute the code and view results. You can modify parameters or data to experiment.

What You Can Learn

• Machine Learning Basics: Understand the theory and implementation of widely-used ML algorithms. Learn how to implement and apply the ML algorithms using primary machine learning libraries in Python.
• Practical Application: Learn how to preprocess data, train models, and evaluate performance.
• Visualization: Explore how to visualize datasets and model predictions.
• Python for ML: Strengthen your Python and Jupyter Notebook skills.

Contributing

Contributions are welcome! If you’d like to add new algorithms, improve explanations, or fix bugs, feel free to submit a pull request.

License

This project is licensed under the MIT License.