Iris Flower Classification

📌 Project Overview

This project is part of my Data Science Internship at CodeAlpha.

The objective of this project is to build a machine learning model that classifies Iris flowers into three species:

• Setosa
• Versicolor
• Virginica

The classification is performed using flower measurements such as sepal length, sepal width, petal length, and petal width.

🎯 Objective

To develop and evaluate a machine learning classification model that can predict the species of an Iris flower based on its measurements.

📊 Dataset

The Iris dataset provided by Scikit-learn was used for this project.

The dataset contains measurements of Iris flowers belonging to three different species.

🛠️ Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• Google Colab
• GitHub

🤖 Machine Learning Model

Logistic Regression was used as the classification algorithm.

The dataset was divided into:

• 80% Training Data
• 20% Testing Data

📈 Model Performance

The Logistic Regression model achieved:

Accuracy: 96.67%

The model was also evaluated using:

• Classification Report
• Confusion Matrix

🔍 Project Workflow

1. Load the Iris dataset
2. Explore the dataset
3. Visualize the data
4. Split the dataset into training and testing sets
5. Train the Logistic Regression model
6. Make predictions
7. Evaluate model performance
8. Test the model with a sample input

✅ Results

The trained model successfully classified the Iris flower species with an accuracy of 96.67% on the test dataset.

📌 Conclusion

This project demonstrates a basic machine learning classification workflow using Python and Scikit-learn. It helped in understanding data exploration, visualization, model training, prediction, and model evaluation.

👨‍💻 Internship

Completed as part of the Data Science Internship at CodeAlpha.