Iris Classifier Package

A comprehensive machine learning package for classifying iris flowers by species using the classic Iris dataset.

Overview

This package provides a complete workflow for iris flower classification using a Decision Tree model. It includes data loading, feature processing, model training, evaluation, and prediction capabilities.

Features

• Data Loading: Load and preprocess the Iris dataset
• Feature Processing: Scale features and apply PCA dimensionality reduction
• Model Training: Train a Decision Tree classifier with cross-validation
• Model Evaluation: Evaluate model performance with accuracy, confusion matrix, and classification report
• Visualization: Generate PCA plots, confusion matrices, and decision tree visualizations
• Model Storage: Save and load trained models and preprocessors
• Prediction: Predict iris species based on flower measurements

Installation

# Clone the repository
git clone https://github.com/KrishTalwar03/data-sci-Ces-2.git
cd data-sci-Ces-2

# Install the package and dependencies
pip install -e .

Usage

Basic Usage

python -m src.main

Making Predictions

# Predict the species for a new flower
species = classifier.predict_iris_species(
    sepal_length=5.1, 
    sepal_width=3.5, 
    petal_length=1.4, 
    petal_width=0.2
)
print(f"Predicted species: {species}")

Project Structure

data-sci-Ces-2/
├── src/
│   ├── __init__.py
│   ├── classifier.py       # Main classifier class
│   ├── data_loading.py     # Data loading utilities
│   ├── feature_proc.py     # Feature processing
│   ├── logger.py           # Colored logging setup
│   ├── model_eval.py       # Model evaluation tools
│   ├── model_storage.py    # Model saving/loading
│   └── model_train.py      # Model training utilities
├── README.md
└── setup.py

Requirements

• Python 3.6+
• pandas
• scikit-learn
• matplotlib
• seaborn
• numpy
• colorlog
• joblib

License

This project is licensed under the MIT License - see the LICENSE file for details.

Authors

@Harsh-Bhatia7 - [email protected] @KrishTalwar03 - [email protected]