kshitizrohilla / iris-flower-classification-using-k-nearest-neighbor-algorithm

This project uses the K-Nearest Neighbors (KNN) algorithm to classify Iris flowers based on their sepal and petal measurements. The dataset used in this project is the Iris Dataset, which includes 150 samples of Iris flowers, each with four features: sepal length, sepal width, petal length, and petal width.

Iris Flower Classification using K-Nearest Neighbors Algorithm

This project uses the K-Nearest Neighbors (KNN) algorithm to classify Iris flowers based on their sepal and petal measurements. The dataset used in this project is the Iris Dataset, which includes 150 samples of Iris flowers, each with four features: sepal length, sepal width, petal length, and petal width.

The project involves the following steps:

• Data collection and preparation
• Data exploration and visualization
• Training and testing data division
• Feature scaling
• Model building using KNN algorithm
• Model evaluation using accuracy score and confusion matrix

This project is implemented using Python and popular libraries: NumPy, Pandas, Matplotlib, and Scikit-learn. The code and instructions are provided in the Jupyter notebook.

Feel free to use this project as a reference or starting point for your own data classification projects.