Data Classification Using AI

A beginner-friendly Machine Learning classification project built using Python and the Iris Dataset.
This project demonstrates the complete workflow of supervised learning including:

• Data loading
• Data preprocessing
• Train-test splitting
• Model training
• Prediction
• Accuracy evaluation

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📌 Project Objective

The main goal of this project is to build a simple AI classification model using Machine Learning.

The project uses the K-Nearest Neighbors (KNN) classification algorithm to classify iris flowers into different species based on their measurements.

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🚀 Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn

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📂 Project Structure

Data_Classification_Using_AI/
│
├── assets/
│   ├── output_5_rows.png
│   ├── output_discription_and_info.png
│   ├── output_missing_values.png
│   └── output.png
│
├── data_classifier.py
├── README.md
└── requirements.txt

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📦 Installation

Install required libraries using:

pip install pandas numpy scikit-learn

Or install using requirements file:

pip install -r requirements.txt

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▶️ Run the Project

python data_classifier.py

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📊 Dataset Used

This project uses the built-in Iris Dataset from Scikit-learn.

Features:

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

Target:

• Iris flower species classification

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🧠 Machine Learning Algorithm

K-Nearest Neighbors (KNN)

KNN is a supervised learning classification algorithm that classifies data points based on their nearest neighbors.

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🔍 Project Workflow

1. Import Libraries
2. Load Dataset
3. Explore Dataset
4. Check Missing Values
5. Split Dataset
6. Scale Features
7. Train Model
8. Make Predictions
9. Evaluate Accuracy

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📸 Project Output Screenshots

Dataset Preview

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Dataset Information & Description

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Missing Values Check

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Model Accuracy & Classification Report

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📈 Model Accuracy

The model achieved:

Accuracy: 100%

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📋 Sample Output

Training Data Shape: (120, 4)
Testing Data Shape: (30, 4)

Model Accuracy:
100.0 %

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📚 Learning Outcomes

By completing this project, you will understand:

• Supervised Learning
• Data Classification
• Feature Scaling
• Train-Test Split
• Machine Learning Workflow
• Model Evaluation

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🏆 Project Status

✅ Completed

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👨‍💻 Author

Developed as part of the DecodeLabs Artificial Intelligence Industrial Training Program.

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📜 License

This project is open-source and free to use for learning purposes.