This program demonstrates how to load, analyze, and build a machine learning model using the famous Iris dataset. The dataset consists of 150 samples from three species of Iris flowers (setosa, versicolor, and virginica) with four features each: sepal length, sepal width, petal length, and petal width. The goal is to classify the Iris species based on these features using machine learning techniques.
- Load the Iris dataset from sklearn's datasets module.
- Perform data exploration and visualization (optional).
- Train a machine learning classifier (e.g., k-Nearest Neighbors, Decision Tree, Logistic Regression, etc.).
- Evaluate the model's performance using metrics like accuracy, precision, recall, and F1-score.
- Visualize the classification results (optional).
- Python 3.x
- Scikit-learn: For machine learning models and dataset utilities.
- Matplotlib/Seaborn: For data visualization (optional).
- Pandas: For data manipulation (optional).
- NumPy: For numerical computations (optional).
To run the program, you'll need to have Python installed on your machine. If Python is not installed, you can download it from the official website: https://www.python.org/downloads/
git clone https://github.com/your-username/iris-classification.git
cd iris-classification