Welcome to the Iris Flower Project! This project focuses on solving a classification problem using various machine learning supervised models, primarily Logistic Regression, and Cross-Validation techniques.
The objective of this project is to predict the species of iris flowers based on their characteristics such as sepal length, sepal width, petal length, and petal width. By utilizing machine learning algorithms, we aim to accurately classify the iris flowers into their respective species.
- Utilizes logistic regression and cross-validation for classification.
- Input features include sepal length, sepal width, petal length, and petal width.
- Output classes consist of different species of iris flowers.
Throughout the development of this project, various learning sources have been utilized to understand the concepts, algorithms, and best practices in machine learning and data science. These sources include but are not limited to:
- Online courses
- Documentation and tutorials from platforms like Scikit-learn and TensorFlow
This project wouldn't have been possible without the invaluable assistance and resources provided by:
- Mentors and instructors who guided through the learning process.
- Open-source communities for sharing knowledge and tools.
- Contributors to libraries and frameworks utilized in the project.
This project is licensed under the MIT License. Feel free to use, modify, and distribute the code for both commercial and non-commercial purposes.