🧠 Data Classification Using AI | DecodeLabs Project 2 Python Scikit-Learn Status

Project 2 Predictive Phase: Teaching a machine to recognize patterns in data and categorize new information using Supervised Learning.

📌 Overview A basic classification model built using the Iris Flower Dataset. The system loads the dataset, splits it into training and testing sets, applies the K-Nearest Neighbors (KNN) classification algorithm, and evaluates model accuracy. It also includes an interactive feature to predict the species of a new flower based on user input.

✨ Key Features Loads and explores the Iris dataset (150 samples, 4 features, 3 classes) Splits data into 80% training and 20% testing sets Applies K-Nearest Neighbors (KNN) classification algorithm Displays accuracy and classification report Interactive mode to predict custom flower measurements

🛠️ Tech Stack Language: Python 3.x Libraries: NumPy, Scikit-Learn Concepts: Supervised Learning, Data Preprocessing, Model Evaluation

🚀 How to Run Clone the repository: git clone https://github.com/kanwalwasim05/Kanwal-Wasim-DecodeLabs-Project2-Classification.git

Navigate to the folder: cd DecodeLabs-Project2-Classification

Install dependencies: pip install -r requirements.txt

Run the model: python classification.py Output Screenshot Classification output

🧠 Skills Demonstrated Data handling and preprocessing Supervised learning basics (KNN) Model training, testing, and validation

Built with ❤️ during DecodeLabs AI Internship