iris-flower-classification

I trained a Logistic Regression model on the Iris flower dataset. Now the model can predict the species of a flower based on its input features such as sepal length, sepal width, petal length, and petal width.

Tools & Libraries

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

Dataset

The dataset consists of 150 records of iris flowers, with features:

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

Target classes:

• Setosa
• Versicolor
• Virginica

ScreenShots:

How it works

1. Load and preprocess the dataset
2. Visualize with pair plots
3. Train a Logistic Regression model
4. Evaluate with accuracy & classification report
5. Accept custom input from the user and predict species in real-time

Sample Output

Enter sepal length: 5.1 Enter sepal width: 3.5 Enter petal length: 1.4 Enter petal width: 0.2 Predicted Iris Class is => setosa