This repository contains a machine learning and deep learning project focused on the Iris dataset. The project implements two different methods for classifying Iris flowers: Logistic Regression and Feed Forward Neural Network. It serves as a practical exercise to apply machine learning and deep learning concepts learned in my studies.
This project aims to demonstrate the application of machine learning and deep learning techniques to the Iris dataset, a classic dataset often used for introductory data science and classification tasks. It provides a practical example for understanding and implementing two different classification methods: Logistic Regression and Feed Forward Neural Network.
The Iris dataset used in this project is a well-known dataset in the field of machine learning and consists of 150 samples of Iris flowers, with 50 samples from each of three different species (Setosa, Versicolor, and Virginica). Each sample includes four features: sepal length, sepal width, petal length, and petal width.
In the logistic_regression.ipynb notebook, you'll find the implementation of Logistic Regression to classify Iris flowers based on their features. Logistic Regression is a simple yet powerful classification algorithm, and this notebook serves as an introduction to its usage in machine learning.
In the neural_network.ipynb notebook, you'll find the implementation of a Feed Forward Neural Network (FFNN) using a deep learning framework like TensorFlow or PyTorch. This notebook demonstrates how to build and train a neural network for Iris flower classification, showcasing the power of deep learning.
To run the code in this project, you will need the following libraries and tools:
- Python 3.x
- Jupyter Notebook (for running the provided notebooks)
- Dependencies listed in
requirements.txt
You can install the required Python packages using pip:
pip install -r requirements.txt
- Clone this repository to your local machine:
git clone https://github.com/amirfeqhi/iris-dataset-ml.git
- Navigate to the project directory:
cd iris-dataset-ml
-
Install the required packages as mentioned in the "Requirements" section.
-
Open and run the Jupyter notebooks (
logistic_regression.ipynbandneural_network.ipynb) to explore the code and execute the machine learning models.
If you'd like to contribute to this project, please follow these steps:
- Fork the repository.
- Create a new branch for your changes:
git checkout -b feature/new-feature. - Make your changes and commit them:
git commit -m "Add new feature". - Push to your forked repository:
git push origin feature/new-feature. - Create a pull request to merge your changes into the main repository.