Iris-Classifier-KNN

Iris_Flower_Classification_Using_KNearest_Neighbours

Introduction

The Iris flower classification project uses the Iris dataset to demonstrate a simple machine-learning workflow. It covers data loading, exploration, preprocessing, model building, evaluation, and data visualization.

Prerequisites

• Python (>= 3.6)
• Required Python packages can be installed using: pip install -r requirements.txt

Installation

1. Clone this repository: git clone https://github.com/abhiverse01/iris-flower-classification_using_knearest_neighbours.git
2. Navigate to the project directory: cd iris-flower-classification-using-knearest-neighbours

Usage

1. Install the required packages as mentioned in the Prerequisites section.
2. Run the iris_classification.py script: python iris_classification.py
3. Follow the on-screen instructions to see the classification results and visualizations.

Data Exploration

The Iris dataset consists of four features: sepal length, sepal width, petal length, and petal width. The target variable is the species of the Iris flower.

Data Preprocessing

The dataset is split into training and testing sets. Features are standardized using the StandardScaler.

Model Building

The K-Nearest Neighbors (KNN) algorithm is used for classification. The model is trained using the training data.

Evaluation

The model's accuracy is calculated on the test data. The classification report provides additional performance metrics.

Visualization

Data visualizations are included to provide insights into the dataset and model performance:

• Pair plot to visualize feature relationships
• Box plots to show feature distributions for different species
• Correlation heatmap to display feature correlations

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, feel free to open an issue or create a pull request.

License

This project is licensed under the Apache License.