/Iriscope

Primary LanguageJupyter Notebook

Iris Flower Classification

This repository contains Python code for the Iris Flower Classification project. The code reads the Iris dataset, performs data analysis, visualizations, and applies machine learning models to classify iris flowers into different species. The project uses various machine learning algorithms to achieve this classification, including Logistic Regression, Support Vector Machines, Naive Bayes, K-Nearest Neighbors (KNN), and Decision Trees.

Prerequisites

Before running the code, make sure you have the following Python libraries installed:

  • pandas
  • numpy
  • seaborn
  • matplotlib
  • scikit-learn

You can install these libraries using pip:

pip install pandas numpy seaborn matplotlib scikit-learn

Usage

  1. Clone this repository:
https://github.com/vinod-polinati/Iriscope.git
  1. Navigate to the project directory:
cd Iriscope
  1. Run the Jupyter Notebook or Python script to execute the code:
jupyter notebook Iris-Flower-Classification.ipynb

or

python Iris-Flower-Classification.py

The code will load the Iris dataset, perform data analysis, visualize the data, and apply machine learning models to classify iris flowers based on their features.

Results

The code provides an analysis of the Iris dataset and the performance of different machine learning models. The accuracy of each model is as follows:

  • Logistic Regression: 94.7%
  • Support Vector Machines: 94.7%
  • Naive Bayes: 94.7%
  • K-Nearest Neighbors (KNN): 94.7%
  • Decision Trees: 92.1%

For a detailed summary of the models and their scores, please refer to the Jupyter Notebook or Python script.

##Contributions Contributors are welcomed šŸ«¶