The Iris Classification project aims to use machine learning algorithms to classify different species of iris flowers. The iris dataset, which consists of measurements of sepal length, sepal width, petal length, and petal width, will be used to train the machine learning models. The three species of iris flowers in the dataset are Iris setosa, Iris versicolor, and Iris virginica.
Several machine learning algorithms will be explored, including Logistic Regression or Logit Model (LR), Linear Discriminant Analysis (LDA), k-Nearest Neighbors Method (KNN), Classification and Regression Tree (CART), Naive Bayes Classifier (NB), Support Vector Machine Method (SVM)
The project will also involve data visualization techniques to gain insights into the dataset and to help select the best machine learning algorithm for the classification task. The final output of the project will be a trained machine learning model that can accurately classify iris species based on their measurements.
Overall, the Iris Classification project will demonstrate the power of machine learning in solving classification problems and showcase the practical application of these techniques in the field of botany.