CodeAlpha_IrIsClassification

Machine Learning project to classify Iris flowers using Random Forest Classifier with EDA, visualization and feature importance analysis.

Iris Flower Classification using Machine Learning 🌸

Project Overview

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

• 🌺 Iris Setosa
• 🌼 Iris Versicolor
• 🌸 Iris Virginica

The model predicts flower species based on sepal and petal measurements.

Dataset Information

The dataset contains 150 flower samples with the following features:

• Sepal Length
• Sepal Width
• Petal Length
• Petal Width

Target Variable: Species

Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-Learn

Project Workflow

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

Machine Learning Model

Algorithm: Random Forest Classifier

Parameters:

• n_estimators = 100
• random_state = 42

Results

• Model Accuracy: 90%
• Classification Report Generated
• Confusion Matrix Visualized
• Feature Importance Analysis Completed

Key Insights

• Petal Length and Petal Width are the most important features
• The dataset is balanced with 50 samples per species
• Random Forest classified Iris-Setosa with 100% confidence
• Clear separation visible between species in pairplot visualizations

How to Run

1. Clone this repository
2. Install required libraries:

pip install pandas numpy matplotlib seaborn scikit-learn

3. Open the notebook in Jupyter:

jupyter notebook iris_classification.ipynb

4. Run all cells sequentially

Author

Shreyansh Gupta B.Tech (CSDS) Galgotias College of Engineering & Technology