CODSOFT - Task 1 - IRIS Flower Classification

This repository contains the IRIS Flower Classification project developed by Suvranil Saha for a data science internship at CodSoft. The project involves clustering and visualization of the Iris dataset using various machine learning techniques.

Project Description

The IRIS Flower Classification project classifies iris flowers into three species: Setosa, Versicolour, and Virginica. The dataset used is the famous Iris dataset. The project demonstrates the use of clustering algorithms, particularly K-Means, and various visualization techniques to analyze the data.

Code and Data

Code

The main code is contained in the IRIS_FLOWER_CLASSIFICATION.ipynb. It includes the following steps:

1. Importing Libraries
2. Loading the Dataset
3. Data Preprocessing
4. Checking for Null Values
5. 3D Scatter Plot
6. 2D Scatter Plot
7. Applying Elbow Technique
8. K-Means Algorithm
9. Confusion Matrix

Dataset

The dataset used in this project is the Iris dataset, which consists of 150 samples of iris flowers with the following features:

• sepal_length
• sepal_width
• petal_length
• petal_width
• species

Acknowledgements

• Thanks to CodSoft for providing the opportunity to work on this project.
• The dataset used in this project is publicly available and sourced from Kaggle.