This collection of Jupyter Notebooks showcases the implementation of various machine learning classification models. Below is a detailed overview of the files, data used, and instructions on how to get started.

1. MLP.ipynb:

   • Multilayer Perceptron implementation.

2. Naive_Bayes_Classifier_.ipynb:

   • Naive Bayes Classifier implementation.

3. Neural_networks_classifier_.ipynb:

   • Neural Networks Classifier implementation.

4. RandomTree.ipynb:

   • Random Tree implementation.

5. SVM.ipynb:

   • Support Vector Machine (SVM) implementation.

• smoke_detection_iot.csv:
  • Dataset used in the notebooks for smoke detection in IoT.

To explore and run the models, follow these steps:

• Open and run the notebooks based on the classification model you are interested in.

• Utilize the notebooks to gain insights into the implementation of various classification models.

• Customize the code and datasets to address specific use cases.

If you're interested in contributing:

1. Fork the repository.

2. Create a new branch:

   git checkout -b feature/your-feature