AI Supervised Learning: Iris Data Classification

A Python-based machine learning project that demonstrates a complete Input-Process-Output pipeline for supervised learning. The project uses the K-Nearest Neighbors (KNN) algorithm to classify different species of Iris flowers based on their sepal features and visualizes the results.

Developed during my technical internship at Decode Labs.

System Architecture

The codebase is organized into three clear, distinct phases:

1. INPUT PHASE: Loads the classic Iris dataset and extracts the primary physical features (Sepal Length and Sepal Width) for 2D spatial analysis.
2. PROCESS PHASE:
   • Splits the data into training ($80%$) and testing ($20%$) sets with shuffling enabled.
   • Standardizes the features using StandardScaler to optimize spatial distance calculations.
   • Trains a K-Nearest Neighbors (KNN) classifier with $K = 3$.
3. OUTPUT PHASE: Evaluates the model using a comprehensive classification report (precision, recall, F1-score) and generates a standardized scatter plot using seaborn.

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Tech Stack & Dependencies

Language: Python 3.x IDE: Visual Studio Code (VS Code) Machine Learning: scikit-learn Data Visualization: matplotlib, seaborn Numerical Computing: numpy

📂 Project Structure

├── main.py              # Core AI script (Input, Process, Output logic)
├── requirements.txt     # Python project dependencies
└── README.md            # Project documentation