Iris Flower Classification
This project is a simple implementation of the Iris Flower Classification task, a popular machine learning problem used for learning and testing classification algorithms. The dataset used in this project is the famous Iris dataset, which contains measurements of various features of three different species of iris flowers.
Overview
The Iris Flower Classification project aims to build a machine learning model that can accurately classify iris flowers into one of three species: setosa, versicolor, and virginica. The model is trained on a dataset containing measurements of sepal length, sepal width, petal length, and petal width.
Requirements
Python 3.x
NumPy
Pandas
Scikit-learn
Matplotlib
Jupyter Notebook (optional, for running the provided notebook)
Installation
To install the required dependencies, run the following command:
pip install -r requirements.txt
Usage
Clone this repository:
git clone https://github.com/your-username/Iris-Flower-Classification.git
Navigate to the project directory:
cd Iris-Flower-Classification
Run the Jupyter Notebook (optional):
jupyter notebook Iris_Flower_Classification.ipynb
Dataset The Iris dataset used in this project is included in the data directory. The dataset file is named iris.csv. It contains the following columns:
SepalLengthCm
SepalWidthCm
PetalLengthCm
PetalWidthCm
Species
Model
The classification model is implemented using the scikit-learn library. The notebook (Iris_Flower_Classification.ipynb) and the Python script (iris_classification.py) provide code for training and evaluating the model.
Evaluation
The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1 score. These metrics are displayed in the notebook and the script output.