Iris-flower Dataset Analysis and Classification

This project explores the Iris dataset to analyze its features and implement machine learning models to classify the species of Iris flowers.

Table of Contents

• Overview
• Features
• Technologies Used
• Dataset
• Results

Overview

The Iris dataset is a classic dataset for classification problems. This project includes:

• Data preprocessing and cleaning.
• Exploratory Data Analysis (EDA) using visualizations like histograms, scatter plots, and pair plots.
• Building and evaluating machine learning models (Logistic Regression, K-Nearest Neighbors, and Decision Tree).
• Visualizing model performance through metrics like accuracy, confusion matrix, and classification reports.

Features

• Load and clean the Iris dataset.
• Visualize data distributions and correlations.
• Preprocess data for machine learning.
• Implement multiple machine learning models.
• Evaluate and compare model performances.
• Test new data for predictions.

Technologies Used

• Python 3.15
• Libraries:
  • pandas
  • numpy
  • matplotlib
  • seaborn
  • scikit-learn

Dataset

The Iris dataset contains 150 samples with 4 features:

• Sepal Length (cm)
• Sepal Width (cm)
• Petal Length (cm)
• Petal Width (cm)
• Species: Target variable with three classes:
  • Iris-setosa
  • Iris-versicolor
  • Iris-virginica

Results

The implemented models achieve the following approximate accuracies:

Logistic Regression: ~97%

KNN: ~96%

Decision Tree: ~96%

The confusion matrix and classification report provide further insights into model performance.