Flower Species Classification

Description

The Flower Species Classification Model is a machine learning-based classification tool that identifies different flower species using sepal and petal dimensions. Built using Logistic Regression, this model classifies Setosa, Versicolor, and Virginica species from the famous Iris dataset.

🔹 Who Can Use This?
✅ Machine Learning Beginners – Learn classification models, feature scaling, and evaluation techniques.
✅ Data Scientists & AI Enthusiasts – Explore data visualization and model tuning.
✅ Educators & Researchers – Use this model to teach machine learning concepts in academia.
✅ Botanists & Biologists – Understand floral classification with ML techniques.

Key Features:

✔ Iris Dataset Classification – Predicts species based on sepal and petal measurements.
✔ Logistic Regression Model – A simple yet powerful classification technique.
✔ Data Preprocessing & Feature Scaling – Uses StandardScaler to normalize data.
✔ Seaborn & Matplotlib Visualizations – Includes scatter plots, boxplots, and heatmaps.
✔ Performance Evaluation – Provides accuracy scores, classification reports, and confusion matrices.
✔ Beginner-Friendly & Expandable – Ideal for ML projects, research, and learning purposes.