This documentation provides a comprehensive overview of the Iris Flowers Classification ML project. It includes information about the project's purpose, dataset, model architecture, training process, and evaluation metrics.
The purpose of this project is to develop a machine learning model that can accurately classify different species of Iris flowers based on their sepal length, sepal width, petal length, and petal width measurements. The model aims to classify the flowers into three species: Setosa, Versicolor, and Virginica.
The dataset used for this project is the Iris flower dataset, which is a well-known dataset in the machine learning community. It contains measurements of 150 Iris flowers, with 50 samples from each of the three species. Each sample has four features: sepal length, sepal width, petal length, and petal width. The dataset is commonly used for classification tasks.
Pair plots provide a comprehensive view of the relationships between all pairs of features in the dataset. Each scatter plot in the pair plot matrix represents the relationship between two features, and the plots can be differentiated by class labels using colors or markers.
For this project, a popular classification algorithm such as KNeigbors Classifier (KNN), Decision Tree Classifier, Support Vector Machines (SVM), Random Forest, can be chosen as the model architecture. The selection of the model should be based on its ability to handle multiclass classification tasks effectively.
- Python - Programming Language
- Jupyter Notebooks - Open-source web application that allows data scientists to create and share documents that integrate live code, equations, computational output, visualizations, and other multimedia resources.
- Anaconda - Local environment for practice
- GitHub - Repository for storing all files