Machine Learning Projects

This repository contains various machine learning regression models implemented in Python. Each sub-directory represents a different regression algorithm, complete with its dataset, trained model, and a Jupyter Notebook demonstrating the implementation.

Project Structure

Each regression model is organized into its own directory with the following structure:

<model_name>/
├── dataset/
│   └── <dataset_file>.<ext>
├── model/
│   └── model.pkl
└── notebook/
    └── <model_name>-1.ipynb

Implemented Models

• Decision Tree Regression: Implementation of Decision Tree for regression tasks.
• ElasticNet Regression: Implementation of ElasticNet regularization for linear regression.
• Gradient Boosting Regression: Implementation of Gradient Boosting for regression tasks.
• K-Nearest Neighbors (KNN) Regression: Implementation of KNN for regression tasks.
• Lasso Regression: Implementation of Lasso regularization for linear regression.
• LightGBM Regression: Implementation of LightGBM for regression tasks.
• Linear Regression: Basic implementation of Linear Regression.
• Logistic Regression: Implementation of Logistic Regression (though typically used for classification, it might be used for regression in some contexts here).
• Multiple Regression: Implementation of Multiple Linear Regression.
• Naive Bayes Classification: (Note: This is a classification algorithm, included for completeness if part of a broader ML repository).
• Polynomial Regression: Implementation of Polynomial Regression.
• Random Forest Regression: Implementation of Random Forest for regression tasks.
• Ridge Regression: Implementation of Ridge regularization for linear regression.
• Support Vector Machine (SVM) Regression: Implementation of SVM for regression tasks.
• XGBoost Regression: Implementation of XGBoost for regression tasks.

How to Use

To explore any of the models:

1. Navigate to the specific model's directory (e.g., linear_regression).
2. Open the Jupyter Notebook located in the notebook/ directory (e.g., linear-regression-1.ipynb).
3. Follow the steps in the notebook to understand data loading, preprocessing, model training, and evaluation.

Dependencies

Each notebook specifies its dependencies, but common libraries used across these projects include:

• numpy
• pandas
• matplotlib
• scikit-learn
• lightgbm (for LightGBM Regression)
• xgboost (for XGBoost Regression)

You can install these using pip:

pip install numpy pandas matplotlib scikit-learn lightgbm xgboost

Data Sources

Datasets are typically sourced from Kaggle and are included in the dataset/ subdirectory of each model.