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Machine Learning Classification Algorithms

This repository contains implementations of various machine learning classification algorithms to compare their performance on a given dataset.

📖 Overview

The primary goal of this project is to apply several common classification models to a dataset and evaluate their effectiveness. The models are trained and tested, and their performance is measured using standard classification metrics.

🤖 Algorithms Covered

This project explores the following classification algorithms:

  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Support Vector Machine (SVM)
  • Decision Tree
  • Random Forest

⚙️ Setup with Anaconda

Follow these instructions to set up the project and its dependencies using the Anaconda distribution.

  1. Clone the repository:

    git clone [https://github.com/SafSaba/ML-Algorithm-Classification.git](https://github.com/SafSaba/ML-Algorithm-Classification.git)
cd ML-Algorithm-Classification

  2. Create the Conda Environment: The environment.yml file in this repository contains all the necessary packages. You can create an identical environment with a single command.

    # Create the environment from the yml file
conda env create -f environment.yml

  3. Activate the Environment: Once the environment is created, activate it to start using it. The name of the environment is specified inside the environment.yml file.

    # Activate the new environment
conda activate <your-env-name>

    (Note: Replace <your-env-name> with the actual name defined in your environment.yml file.)

After activating the environment, you are ready to run the project's scripts or notebooks as described in the Usage section.