Machine_Learning_Algorithms

This repository contains a collection of machine learning algorithms implemented from scratch and with popular libraries. Each algorithm is demonstrated and explained in detail using Jupyter Notebooks (.ipynb files).

Contents

• Supervised Learning (Regression, Classification)
• Unsupervised Learning (Clustering, Dimensionality Reduction)
• Model Evaluation and Validation
• Data Preprocessing Techniques
• Example Datasets and Visualizations

The following Python libraries are used throughout the notebooks:

• numpy
• pandas
• scikit-learn
• matplotlib
• seaborn
• ipykernel
• jupyter

Additional libraries may be used in specific notebooks and will be listed in their respective sections or requirements files.

How to Run the Notebooks

All notebooks are developed and tested in Visual Studio Code using the Jupyter extension. The Python environment is managed with Conda:

1. Create a Conda Environment

   conda create -n ml_env python=3.10
   conda activate ml_env

2. Install Required Packages

   pip install -r requirements.txt

   Note:
   To run Jupyter Notebooks, you also need to install the ipykernel package in your environment:

   pip install ipykernel

3. Open VS Code
   Open the folder in VS Code, and select the ml_env Conda environment as the Python interpreter.

4. Run Notebooks
   Open any .ipynb file and run the cells interactively.

Requirements

• Python 3.10+
• Conda
• VS Code with Jupyter extension

Feel free to explore, modify, and contribute!