🌸 Iris Flower Species Classification using Machine Learning

An end-to-end Machine Learning classification project developed as part of the CodeAlpha Data Science Internship.

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📌 Project Overview

The Iris Flower Species Classification project demonstrates the complete Machine Learning workflow using the famous Iris dataset.

The objective is to build a classification model capable of predicting the species of an Iris flower based on its physical measurements.

This project covers every important stage of a Data Science project, from data exploration to model evaluation.

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🎯 Project Objectives

• Understand the Iris dataset
• Perform Exploratory Data Analysis (EDA)
• Build a Machine Learning classification model
• Train and evaluate the model
• Predict flower species
• Visualize important insights

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📂 Dataset Information

The dataset contains measurements of Iris flowers.

Features

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

Target

• Iris-setosa
• Iris-versicolor
• Iris-virginica

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⚙️ Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• Jupyter Notebook / Kaggle Notebook

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📊 Project Workflow

Import Libraries
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Load Dataset
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Data Exploration
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Exploratory Data Analysis
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Feature Selection
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Train-Test Split
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Decision Tree Classifier
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Model Training
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Prediction
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Model Evaluation
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Conclusion

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📈 Exploratory Data Analysis

The following analyses were performed:

• Dataset Overview
• Statistical Summary
• Missing Value Analysis
• Duplicate Value Check
• Class Distribution
• Pair Plot
• Correlation Heatmap
• Box Plot
• Histograms
• Violin Plot
• Scatter Plot

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🤖 Machine Learning Model

Algorithm Used

• Decision Tree Classifier

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📊 Model Evaluation

The model was evaluated using:

• Accuracy Score
• Confusion Matrix
• Classification Report
• Feature Importance
• Decision Tree Visualization

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📁 Repository Structure

CodeAlpha_Iris_Flower_Classification/
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├── Iris_Flower_Classification.ipynb
├── Iris.csv
├── README.md
└── images/

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🚀 How to Run

1 Clone the repository

git clone https://github.com/YourUsername/CodeAlpha_Iris_Flower_Classification.git

2 Move into the project folder

cd CodeAlpha_Iris_Flower_Classification

3 Install dependencies

pip install -r requirements.txt

4 Open the notebook

Run the notebook using:

• Jupyter Notebook
• Kaggle Notebook
• Google Colab

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📚 Key Learnings

Through this project, I learned:

• Data preprocessing
• Exploratory Data Analysis
• Decision Tree Classification
• Model Evaluation
• Feature Importance Analysis
• Machine Learning Workflow
• Data Visualization

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👩‍💻 Author

Aleesha Nadeem

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