Iris-Flower-Classification

Iris Flower Classification using Machine Learning

Project Overview

This project uses Machine Learning algorithms to classify Iris flowers into three species:

• Iris-setosa
• Iris-versicolor
• Iris-virginica

The project includes data preprocessing, exploratory data analysis (EDA), visualization, model training, model comparison, and feature importance analysis.

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Dataset

Dataset: Iris Flower Dataset Source : https://www.kaggle.com/datasets/saurabh00007/iriscsv Features:

• Sepal Length (cm)
• Sepal Width (cm)
• Petal Length (cm)
• Petal Width (cm)

Target Variable:

• Species

Total Records: 150

Number of Classes: 3

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Technologies Used

• Python
• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-Learn
• XGBoost

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Machine Learning Models Used

1. Logistic Regression
2. K-Nearest Neighbors (KNN)
3. Decision Tree
4. Random Forest
5. Support Vector Machine (SVM)
6. XGBoost

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Project Workflow

1. Data Loading
2. Data Exploration
3. Data Visualization
4. Data Preprocessing
5. Feature Scaling
6. Train-Test Split
7. Model Training
8. Model Evaluation
9. Model Comparison
10. Feature Importance Analysis

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Results

All machine learning models achieved excellent performance on the Iris dataset.

The dataset is well-structured and highly separable, allowing multiple algorithms to classify the flower species with very high accuracy.

Feature Importance Analysis identified:

1. Petal Length
2. Petal Width

as the most influential features for classification.

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Visualizations Generated

• Species Distribution
• Pair Plot
• Correlation Heatmap
• Model Accuracy Comparison
• Confusion Matrix
• Feature Importance

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Project Structure

Iris_classification/

├── Data/

│ └── Iris.csv

├── images/

│ ├── species_distribution.png

│ ├── pairplot.png

│ ├── heatmap.png

│ ├── model_comparison.png

│ ├── confusion_matrix.png

│ └── feature_importance.png

├── Iris_classification.ipynb

├── requirements.txt

├── .gitignore

└── README.md

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Installation

Clone the repository:

git clone

Install dependencies:

pip install -r requirements.txt

Run Jupyter Notebook:

jupyter notebook

Open:

Iris_classification.ipynb

and execute all cells.

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Author

Rawal JayKumar NarendraKumar

Data Science & Business Intelligence Learner

CodeAlpha Internship Project