Added "Iris Dataset classification" under supervised learning/KNN/

Supervised Learning/K Nearest Neighbors/Iris Dataset Classification/README.md

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+# Iris Dataset Classification Using K-Nearest Neighbors (KNN)
+
+This project is designed to help beginners understand how the K-Nearest Neighbors (KNN) algorithm works by applying it to the famous Iris dataset. The Iris dataset is often used for learning machine learning algorithms because of its simplicity and well-defined structure.
+
+## Table of Contents
+- [Overview](#overview)
+- [Dataset](#dataset)
+- [Algorithm](#algorithm)
+- [Installation](#installation)
+- [Usage](#usage)
+
+---
+
+## Overview
+In this project, we classify the Iris dataset into one of three species: **Setosa**, **Versicolor**, and **Virginica**. K-Nearest Neighbors (KNN) is used for classification, which is a simple and effective machine learning algorithm. We will explore the dataset, preprocess the data, and evaluate the model’s performance.
+
+---
+
+## Dataset
+The Iris dataset consists of 150 samples, each having four features:
+- **Sepal Length**
+- **Sepal Width**
+- **Petal Length**
+- **Petal Width**
+
+Each sample belongs to one of three classes:
+1. Setosa
+2. Versicolor
+3. Virginica
+
+You can directly import the dataset from sklearn library 
+
+---
+
+## Algorithm
+We use the **K-Nearest Neighbors (KNN)** algorithm, which classifies a sample based on the majority class among its K nearest neighbors.
+
+### Steps:
+1. **Data Loading**: Load the Iris dataset and inspect the structure.
+2. **Data Preprocessing**: Split the data into training and testing sets.
+3. **Model Training**: Apply KNN to the training data.
+4. **Model Evaluation**: Use accuracy, precision, recall, and F1-score to evaluate the model.
+5. **Hyperparameter Tuning**: Optimize the value of K to improve classification accuracy.
+
+---
+
+## Installation
+To get started with the project, follow these steps:
+
+1. **Clone the repository**:
+   ```bash
+   git clone https://github.com/UppuluriKalyani/ML-Nexus/tree/main/Supervised%20Learning/K%20Nearest%20Neighbors/Iris%20Dataset%20Classification
+2. **Install dependencies**:
+   Ensure you have Python 3 installed. Then, install the necessary Python libraries using pip:
+   ```bash
+   pip install -r requirements.txt
+## Usage
+After installing the required dependencies, you can run the project and see the results:
+
+
+irisClassifier.ipynb
+
+ - Change the value of K in the code to see how it affects the classification accuracy:
+
+```python
+knn = KNeighborsClassifier(n_neighbors=3)  # Change 3 to any value of K you want to try
+
+

Supervised Learning/K Nearest Neighbors/Iris Dataset Classification/RESULT.md

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+# Results for Iris Dataset Classification Using K-Nearest Neighbors (KNN)
+
+In this section, we present the results of applying the K-Nearest Neighbors (KNN) algorithm on the Iris dataset. The following visualizations help us understand how well the model performs and how the data is distributed.
+
+---
+
+## 1. Scatter Plot of All Data Points
+
+The first plot shows all the data points in the Iris dataset with the following features:
+- **Sepal Length**
+- **Sepal Width**
+
+
+![All Points Scatter Plot](assets/images/1.png)
+
+---
+
+## 2. Category-wise Scatter Plots
+
+Each point is color-coded according to its species (Setosa, Versicolor, Virginica).
+
+
+![CategoryWise Scatter Plot](assets/images/2.png)
+
+---
+
+## 3. Decision Boundary Plot
+
+The following plot shows the decision boundaries created by the KNN algorithm. This helps us visualize the regions where the algorithm classifies a new data point into a specific class.
+
+![Decision Boundary Plot](assets/images/3.png)
+
+---
+
+## Summary of Results
+![Decision Boundary Plot](assets/images/4.png)
+
+- The **scatter plots** show a clear separation between the species, especially between Setosa and the other two classes.
+- The **decision boundary plot** shows how the KNN algorithm divides the feature space into different regions based on the nearest neighbors.
+
+By tuning the value of **K**, we can adjust the smoothness of the decision boundaries. In this example, we used `K=5`, which provides a good balance between underfitting and overfitting.
+