Weather Type Classification

This repository contains the code for a machine learning project aimed at classifying weather types based on various meteorological features using multiple models including Decision Trees, Random Forest, SVM, and Neural Networks.

Overview

In this project, we aim to classify different types of weather using several machine learning algorithms. The dataset consists of various weather features like temperature, humidity, wind speed, precipitation, and more, which are used to predict the weather type.

Project Structure

• data/: Contains the weather dataset in CSV format.
• models/: Contains saved models after training.
• results/: Contains the evaluation results and confusion matrices for each model.
• notebooks/: Jupyter notebooks used for data preprocessing, training, and evaluation.
  • training_and_evaluation.ipynb: Jupyter notebook for training and evaluating the models.

Getting Started

Installation

Follow these steps to set up the environment and install the necessary dependencies for the project:

1. Clone the Repository:

   First, clone the repository from GitHub to your local machine:

   git clone https://github.com/Hermanto050302/weather-type-classification.git
   cd weather-type-classification
   

2. Install the Required Packages:

   Install the required Python packages using pip and the requirements.txt file:

   pip install -r requirements.txt
   

Training

Open and run the notebooks/training_and_evaluation.ipynb notebook to train the model on your dataset.

Contributing

Contributions are welcome! For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

The dataset used in this project is sourced from Kaggle.