This project implements machine learning models to classify the famous Iris flower dataset. It utilizes Logistic Regression and Decision Tree classifiers to predict the species of Iris flowers based on sepal and petal measurements.
- 🌸 Iris Flower Classification – Predicts Iris species using machine learning.
- 📊 Multiple Models – Implements Logistic Regression and Decision Tree classifiers.
- 🔍 Feature-Based Prediction – Uses petal and sepal measurements as input features.
- 📈 Performance Evaluation – Evaluates model accuracy and performance metrics.
- Programming Language: Python
- Libraries: Pandas, NumPy, Scikit-learn, Matplotlib
- Dataset: Iris dataset from
sklearn.datasets
- Load the dataset and preprocess the data.
- Train the models using Logistic Regression and Decision Tree classifiers.
- Evaluate the models and visualize results using Matplotlib.