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.

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🤖 Algorithms Covered

This project explores the following classification algorithms:

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

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⚙️ 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.