🌸 Iris Flower Classification - Machine Learning Project

📌 Overview

This project is an introduction to Machine Learning, where we build a model to predict the type of Iris flower based on given measurements. The dataset contains three flower types:

• Iris Setosa
• Iris Versicolor
• Iris Virginica

The dataset, created by R.A. Fisher (1936), is one of the most widely used datasets in ML.

📊 Dataset

The dataset consists of 150 flower samples, each with the following four features:

• 🌿 Sepal Length (cm)
• 🌿 Sepal Width (cm)
• 🌺 Petal Length (cm)
• 🌺 Petal Width (cm)

🏗️ Project Workflow

1. Exploratory Data Analysis (EDA): Understanding the dataset and its patterns.
2. Data Preprocessing: Cleaning and preparing data for training.
3. Splitting Data: Dividing the dataset into training and testing sets.
4. Model Training: Training a classification model using Python libraries.
5. Evaluation & Prediction: Testing model accuracy and making predictions.

🛠️ Technologies Used

• Python
• Pandas
• Scikit-learn
• Matplotlib
• Seaborn

🚀 How to Run the Project

Clone this repository:

git clone https://github.com/manojtharindu11/iris-flower-classification

Navigate to the project directory:

cd iris-flower-classification

Install dependencies:

pip install -r requirements.txt

Run the Jupyter Notebook:

jupyter notebook iris_classification.ipynb