Machine Learning Algorithms

This repository contains implementations of various machine learning algorithms using Python. Each algorithm is implemented as a separate script, making it easy to understand and experiment with different techniques.

📂 Folder Structure

Machine-Learning-Algorithms/
│── MachineLearning/
│   ├── DecisionTree.py
│   ├── DummyVariables.py
│   ├── Ensemble_learning.py
│   ├── Gradient_descent.py
│   ├── HyperParameterTuning.py
│   ├── KNN.py
│   ├── K_Fold_crossvalidation.py
│   ├── K_Means_Clustering.py
│   └── GD3.py
│── README.md

🚀 Implemented Algorithms

• Decision Tree - Implementation of decision tree classification.
• Dummy Variables - Handling categorical variables for ML models.
• Ensemble Learning - Combining multiple models for better predictions.
• Gradient Descent - Optimization algorithm for model training.
• Hyperparameter Tuning - Methods to optimize model performance.
• K-Nearest Neighbors (KNN) - Instance-based learning method.
• K-Fold Cross-Validation - Model evaluation technique.
• K-Means Clustering - Unsupervised learning for clustering.

🛠️ Installation & Usage

1. Clone the repository:

   git clone https://github.com/ayushbhopal/Machine-Learning-Algorithms.git

2. Navigate to the folder:

   cd Machine-Learning-Algorithms/MachineLearning

3. Install required dependencies:

   pip install numpy pandas scikit-learn matplotlib

4. Run any script:

   python DecisionTree.py

📌 Contribution

Feel free to fork this repository and contribute by improving the implementations or adding new algorithms!