CodeAlpha_IrisFlowerClassification

🌼 Iris Flower Classification

A simple machine learning project that predicts the species of Iris flowers using four measurements:

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

📌 Project Overview

This project uses the Decision Tree Classifier to classify Iris flowers into:

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

Dataset used: Kaggle Iris Dataset (CSV format).

🛠️ Technologies Used

• Python
• Pandas
• Scikit-learn
• NumPy

🚀 Steps Performed

1. Loaded and cleaned the dataset
2. Encoded species labels
3. Split data into training & testing sets
4. Trained a Decision Tree Classifier
5. Evaluated accuracy on test data

📊 Model Performance

Prints:

• Accuracy
• Classification Report
• Confusion Matrix

🧩 Code (Main Part)

model = DecisionTreeClassifier()
model.fit(X_train, y_train)
pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, pred))

📁 How to Run

1. Clone the repository
2. Install required libraries
3. Run the notebook or Python script

🌟 Result

A simple, clean ML model that accurately classifies Iris flower species.

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