🤖 Data Classification Using AI

A basic machine learning classification project developed in Python as part of DecodeLabs Artificial Intelligence Project 2.

📌 About the Project

This project demonstrates a basic supervised learning workflow using the Iris dataset.

A Decision Tree Classifier is trained to recognize patterns in the dataset and classify Iris flowers into their respective species.

🎯 Project Objectives

• Load and understand a small dataset
• Prepare the data for machine learning
• Split the dataset into training and testing sets
• Apply a classification algorithm
• Train a machine learning model
• Make predictions on test data
• Evaluate the model using accuracy and a classification report

🧠 Technologies Used

• Python
• Pandas
• Scikit-learn
• Decision Tree Classifier

📊 Dataset

The project uses the built-in Iris dataset provided by Scikit-learn.

The dataset contains four features:

• Sepal length
• Sepal width
• Petal length
• Petal width

The model classifies flowers into three species:

• Setosa
• Versicolor
• Virginica

⚙️ Machine Learning Process

Iris Dataset
     ↓
Data Preparation
     ↓
Train/Test Split
     ↓
Decision Tree Classifier
     ↓
Model Training
     ↓
Prediction
     ↓
Model Evaluation

📈 Model Performance

The Decision Tree Classifier achieved an accuracy of:

1.00 (100%)

on the 20% test dataset for the selected train/test split.

The classification report evaluates the model using:

• Precision
• Recall
• F1-score
• Support

📸 Demo

Here is the Data Classification AI model running in the terminal:

▶️ How to Run

1. Install the required libraries

pip install pandas scikit-learn

2. Run the program

python classification.py