## Table of Contents:
* Introduction
* Dependencies
* Usage
* Methods
* Results
* Contributing
## Introduction:
* This repository contains the code and analysis for the IRIS Dataset Analysis and Classification project. The IRIS dataset is a well-known dataset in the machine learning community and is often used for classification tasks. In this project, we explore the dataset, perform data analysis, and implement three different classification algorithms: K-Nearest Neighbors (KNN), Logistic Regression, and Decision Trees.
## Dependencies:
* Python (version X.X.X)
* Jupyter Notebook (to run the .ipynb notebook)
* Libraries:
* Pandas
* NumPy
* Matplotlib
* Seaborn
* Scikit-learn
## Usage:
* Open the Jupyter Notebook IRIS Dataset Analysis and Classification.ipynb to access the code and analysis. The notebook contains step-by-step explanations and code to perform the following tasks:
* Data loading and exploration
* Data visualization and analysis
* Data preprocessing and feature engineering
* Model training and evaluation using KNN, Logistic Regression, and Decision Trees
* Model comparison and analysis of results
* Simply run the notebook cells to follow along with the analysis and classification process.
## Methods:
The following methods are implemented in this project:
* K-Nearest Neighbors (KNN): A non-parametric classification algorithm that assigns labels to data points based on the majority class of their K-nearest neighbors.
* Logistic Regression: A linear classification algorithm that estimates the probability of a data point belonging to a certain class.
* Decision Trees: A tree-based classification algorithm that splits the data into subsets based on the values of features, aiming to create homogeneous groups.
## Results:
The results of the classification models are presented in the Jupyter Notebook. Performance metrics such as accuracy, precision, recall, and F1-score are used to evaluate the models' effectiveness.
## Contributing:
Contributions to this project are welcome! If you find any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request.