IRIS-Flower-classification and iris-flower-classification-ml
These are direct competitors—both are standalone machine learning projects that independently solve the same iris flower classification problem using similar supervised learning approaches, making them interchangeable alternatives rather than tools designed to work together.
About IRIS-Flower-classification
amberkakkar01/IRIS-Flower-classification
This project is for the Identification of Iris flower species is presented
Implements multiple supervised learning algorithms (Decision Tree, K-Nearest Neighbors, SVM, and Logistic Regression) using scikit-learn to classify iris specimens. The project applies train-test-split methodology and evaluation metrics to benchmark classifier performance on the standard Iris dataset. Designed as an educational resource for machine learning beginners to understand algorithmic approaches and accuracy measurement.
About iris-flower-classification-ml
RafiShaik-AI/iris-flower-classification-ml
Machine learning project for classifying Iris flower species using algorithms like Logistic Regression and Decision Tree. Includes data preprocessing, model training, accuracy comparison, and visualization using Python and scikit-learn.
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