This program applies basic machine learning (classification) concepts on Fisher's Iris Data to predict the species of a new sample of Iris flower. Software and Libraries

• Python 3.7.10
• scikit-learn 0.18.1 Introduction
  The dataset for this project originates from the Kggle platform The Iris flower data set or Fisher's Iris data set is a multivariate data set. The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis.
• The data set consists Arround 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor).
• Four features were measured from each sample (in centimetres):
  • Length of the sepals
  • Width of the sepals
  • Length of the petals
  • Width of the petals

Working of the iris_decision_tree_classifier

• The program takes data from the load_iris() function available in sklearn module.
• The program then creates a decision tree based on the dataset for classification.
• The user is then asked to enter the four parameters of his sample and prediction about the species of the flower is printed to the user.

Working of the iris_selfmade_KNN

• The program takes data from the load_iris() function available in sklearn module.
• The program then divides the dataset into training and testing samples in 80:20 ratio randomly using train_test_learn() function available in sklearn module.
• The training sample space is used to train the program and predictions are made on the testing sample space.
• Accuracy score is then calculated by comparing with the correct results of the training dataset.