Data-Classification-Using-AI

A beginner-friendly machine learning classification project that predicts Iris flower species using a Decision Tree Classifier.

This project demonstrates a complete ML workflow including data loading, exploratory data analysis (EDA), visualization, model training, evaluation, prediction, and performance analysis.

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Project Overview

This project performs end-to-end classification using the Iris dataset and covers the complete machine learning pipeline.

The system:

• Loads and explores data
• Generates visual analysis
• Trains a classification model
• Evaluates prediction performance
• Produces graphs and insights

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Key Highlights

• Complete machine learning workflow
• Beginner-friendly implementation
• Uses Decision Tree Classification
• Multiple visualizations included
• Performance evaluation and reporting
• Feature importance analysis

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Dataset Information

Property	Details
Dataset	Iris Dataset
Source	Scikit-learn
Total Records	150
Features	Sepal Length, Sepal Width, Petal Length, Petal Width
Classes	Setosa, Versicolor, Virginica

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Classification Model

Algorithm Used

Decision Tree Classifier

A supervised machine learning algorithm used to classify flower species based on feature values.

Model Configuration

DecisionTreeClassifier(
max_depth = 3
)

Model depth is restricted to reduce complexity and minimize overfitting.

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Project Workflow

Load Dataset
↓
Data Exploration
↓
Data Visualization
↓
Train-Test Split
↓
Model Training
↓
Prediction
↓
Evaluation
↓
Performance Analysis

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Model Performance

Metric	Score
Training Accuracy	95.83%
Testing Accuracy	100.00%

Additional evaluation includes:

• Classification Report
• Confusion Matrix
• Feature Importance
• Overfitting Check

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Generated Visualizations

File	Purpose
histogram.png	Feature distribution
boxplot.png	Feature spread analysis
heatmap.png	Correlation analysis
pairplot.png	Feature relationship analysis
scatterplot.png	Petal comparison

All graphs are stored inside the graphs/ folder.

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Project Structure

Data-Classification-Using-AI/
│
├── classification.py
├── README.md
├── requirements.txt
│
└── graphs/
    ├── histogram.png
    ├── boxplot.png
    ├── heatmap.png
    ├── pairplot.png
    └── scatterplot.png

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Installation Guide

Clone repository:

git clone https://github.com/anmol396/Data-Classification-Using-AI.git

Move to project folder:

cd Data-Classification-Using-AI

Install dependencies:

pip install -r requirements.txt

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Run Project

python classification.py

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Technologies Used

Category	Technology
Language	Python
Data Processing	Pandas, NumPy
Machine Learning	Scikit-learn
Visualization	Matplotlib, Seaborn

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Learning Outcomes

After completing this project:

• Understand classification workflow
• Build Decision Tree models
• Perform exploratory data analysis
• Evaluate model performance
• Generate visual insights
• Interpret feature importance

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Future Improvements

• Add Random Forest comparison
• Hyperparameter tuning
• Interactive dashboard
• Model deployment
• Support additional datasets

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Developed as part of Project 2 – Data Classification Using AI