Iris Dataset Analysis and Classification

This project demonstrates a comprehensive analysis of the famous Iris dataset using Python, including exploratory data analysis (EDA), data visualization, and machine learning classification models.

Overview

The project analyzes the Iris dataset, which contains measurements for three different species of iris flowers. It includes:

• Data exploration and statistical analysis
• Various visualization techniques using Seaborn and Matplotlib
• Implementation of multiple machine learning models for classification

Technologies Used

• Python
• Libraries:
  • Pandas
  • NumPy
  • Matplotlib
  • Seaborn
  • Scikit-learn

Features

1. Exploratory Data Analysis

   • Basic statistics and data summary
   • Group-wise analysis by species

2. Data Visualization

   • Scatter plots
   • Pair plots
   • Histograms
   • Heat maps for correlation analysis
   • Distribution plots

3. Machine Learning Models

   • K-Nearest Neighbors (KNN)
   • Logistic Regression
   • Support Vector Machine (SVM)

Dataset

The dataset includes the following features:

• Sepal Length (cm)
• Sepal Width (cm)
• Petal Length (cm)
• Petal Width (cm)
• Species (target variable)

Setup and Usage

1. Ensure you have Python installed on your system
2. Install required dependencies:

   pip install pandas numpy matplotlib seaborn scikit-learn

3. Run the Jupyter notebook sample.ipynb

File Structure

• sample.ipynb: Main Jupyter notebook containing all analysis and models
• data/Iris.csv: Dataset file (required for running the notebook)

Results

The project implements various classification models with their respective accuracies:

• KNN Classifier
• Logistic Regression
• SVM with RBF kernel

Each model demonstrates effective classification of iris species based on the flower measurements.