This project aims to develop a machine-learning model for classifying iris flowers based on their botanical features. Flower classification holds significant importance in botany and related fields as it facilitates the understanding of plant diversity, distribution, and ecological relationships. Traditionally, botanists have relied on manual classification methods, which can be time-consuming and subjective. By leveraging machine learning techniques, this project seeks to automate the classification process, providing a more efficient and objective approach.
The primary objective of this project is to build a robust classification model capable of accurately identifying iris flowers into their respective species based on features such as length, sepal width, pedal length, and petal width. This model will contribute to advancing research in botany by providing a reliable tool for species identification and classification. Additionally, it will serve as a practical demonstration of the application of machine learning in the field of biology and encourage further exploration of its potential in plant science.
To implement the Iris Flower Classification project, the following technologies and frameworks will be utilized:
🌐 Programming Language
- Python will serve as the primary programming language for its simplicity, versatility, and extensive support for machine learning and data processing libraries.
🤖 Machine Learning Libraries
- The project will leverage machine learning libraries such as scikit-learn for its comprehensive set of tools and algorithms for data preprocessing, model selection, and evaluation. Additionally, TensorFlow used for deep learning-based approaches for experimentation or enhancing model performance.
📈 Data Visualization
- Matplotlib will be employed for data visualization tasks, including exploratory data analysis, feature visualization, and model performance analysis. These libraries offer a wide range of plotting functions and customization options for creating informative visualizations.
This project requires Python 3.8 or later. Here are the steps to set up the project:
- Clone the repository: First, clone this repository to your local machine using
git clone.
git clone https://github.com/LizzGarleb/iris-flower-classification.git
- Create a virtual environment: It's recommended to create a virtual environment to keep the dependencies required by this project separate from your other Python projects. Use the following command to create a virtual environment:
python3 -m venv env
Then, activate the virtual environment. On macOS and Linux:
source env/bin/activate
- Install the dependencies: Once you've activated the virtual environment, you can install the required dependencies using pip. In the project directory, run:
pip install -r requirements.txt
Explain how to use the project. Include code examples and screenshots if possible.
After you've installed the project, you can run it using Python. Here's a basic example:
python3 main.py
- 0 is Setosa & 1 is Versicolor
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