Iris Flower Classification - Machine Learning Project

This project is a complete Machine Learning pipeline built in Google Colab. It trains a Random Forest model to classify iris flowers into 3 species (Setosa, Versicolor, and Virginica) using sepal & petal features.

Understanding the Features

The dataset relies on measuring the length and width of the flower's sepals and petals.

📌 Features

• Data Loading
• Exploratory Data Analysis (EDA)
• Visualization (pairplots, heatmaps)
• Train/Test Split
• Random Forest Classifier
• Accuracy Evaluation
• Confusion Matrix
• Save & Load Model (.pkl)

🚀 Tech Stack

• Python
• Pandas
• NumPy
• Seaborn
• Matplotlib
• Scikit-Learn
• Jupyter/Google Colab

📊 Dataset The Iris dataset is a built-in dataset in Seaborn containing 150 samples of the three species.

📈 Model Accuracy Achieved ~95–100% accuracy depending on the train/test split configuration.

📝 How to Run the Notebook

1. Open iris_ml_project.ipynb in Google Colab
2. Run all cells
3. Modify parameters and try your own samples

🏆 Author Pulkit Meena