Iris Flower Classification

Unified Mentor Internship Project

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Project Overview

The Iris Flower Classification project focuses on building a machine learning model that can classify iris flowers into their respective species based on physical measurements. This project demonstrates the complete machine learning workflow, from data understanding and exploratory data analysis to model training and evaluation.

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Problem Statement

The Iris flower dataset consists of three species:

• Iris Setosa
• Iris Versicolor
• Iris Virginica

Each species differs in measurements such as sepal length, sepal width, petal length, and petal width. Manual classification of these species can be inefficient, making it an ideal problem for machine learning classification.

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Objective

The objective of this project is to develop a supervised machine learning model capable of accurately classifying iris flowers into one of the three species using their physical characteristics.

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Dataset Description

• Dataset: Iris Flower Dataset
• Total Records: 150
• Input Features:
  • Sepal Length
  • Sepal Width
  • Petal Length
  • Petal Width
• Target Variable:
  • Species

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Tools & Technologies Used

• Programming Language: Python
• Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
• Environment: Jupyter Notebook / VS Code
• Domain: Machine Learning, Data Analysis

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Project Workflow

1. Imported required Python libraries
2. Loaded and explored the dataset
3. Performed exploratory data analysis (EDA)
4. Visualized feature relationships
5. Prepared data for modeling
6. Trained multiple classification models
7. Evaluated model performance
8. Selected the best-performing model

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Machine Learning Models Implemented

• Decision Tree Classifier
• Random Forest Classifier
• Naive Bayes Classifier

Multiple models were tested to compare performance and reduce overfitting.

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Performance Metrics

Model performance was evaluated using:

• Recall Score
• Precision
• F1-Score
• Accuracy

Recall was considered the primary evaluation metric to ensure correct classification across all species.

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Results

The following results were obtained after model evaluation:

Model	Recall (Train %)	Recall (Test %)
Decision Tree (Tuned)	95.24	95.56
Random Forest (Tuned)	97.14	97.78
Naive Bayes	94.28	97.78
Naive Bayes (Tuned)	94.28	97.78

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Conclusion

The tuned Random Forest classifier was selected as the final model due to its strong performance and generalization capability. The project successfully demonstrates how machine learning techniques can be applied to classify iris flowers based on their physical characteristics.

Key takeaways from this project include:

• Clear separability of Iris Setosa from other species
• Importance of exploratory data analysis in classification problems
• Effectiveness of ensemble models for structured datasets

This project provides a strong foundation in machine learning classification and highlights practical skills applicable to real-world data science problems.

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Future Scope

• Implement advanced models such as Support Vector Machines or XGBoost
• Perform feature importance analysis
• Deploy the model using a web application or interactive dashboard

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Author

Minakshi
Data Science Intern