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Irisflower_project

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.

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