PYTHON 2.7 PLEASE!

Hyperlink for data: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data

Introduction Mochine learning Is an area of computer science whose alm is to create programs which improve thelr performance with experience. There are many applications for this, including: face recognition, recommendation systens, defect detection, robot navigation, and game playing. For this assignment, you will implement a simple machine learning algorithm called Neorest Neighbor which learns by remembering training examples, It then classifies test examples by choosing the class of the "closest" training example. The netion of "closeness" differs depending on applications. You will need to use the Nearest Neighbor algorithm to learn and classify types of Iris plants based on their sepal and petal length and width. There are three Iris types you wwill need to classify: Iris Setasa Iris Versicclour Iris Virginica The learning will be done by remembering training examples stored in a comma-separated file. The training examples Include different measurements which collectively are called feotures or attributes, and a class label for difterent instances. These are: 1. sepal length in cm 2. sepal width in cm 3. petal length in cm 4, petal width In cm 5. class: Iris Setosa -Iris Versicolour Iris Virginica To see how well the program "learned", you will then load a file containing testing examples, which will include the same type of Information, but for different Instances. For each test Instance, you will apply the Nearest Nelghbor algorithm to classify the instance. This alporithm works by choosing a class label of the "closest training example where "closest means shortest distance. The distance is comiputed using the fallowing formula: where x,y are two instances (i.e. a training or a testing examplel. sl,sly ae their sepal lengths, sWSWy are their sepal widths, p'x ply are their petal lengths, and pw wy are their petal widths. After yau finish classifying each testing instance, you will then need to cornpare it to the e label that is specified instances divided by the number of total testing instances. for each example and compute the accuracy. A uracy is mrasured as the e nurnber of carrectly classified ot cc Introduction Mochine learning Is an area of computer science whose alm is to create programs which improve thelr performance with experience. There are many applications for this, including: face recognition, recommendation systens, defect detection, robot navigation, and game playing. For this assignment, you will implement a simple machine learning algorithm called Neorest Neighbor which learns by remembering training examples, It then classifies test examples by choosing the class of the "closest" training example. The netion of "closeness" differs depending on applications. You will need to use the Nearest Neighbor algorithm to learn and classify types of Iris plants based on their sepal and petal length and width. There are three Iris types you wwill need to classify: Iris Setasa Iris Versicclour Iris Virginica The learning will be done by remembering training examples stored in a comma-separated file. The training examples Include different measurements which collectively are called feotures or attributes, and a class label for difterent instances. These are: 1. sepal length in cm 2. sepal width in cm 3. petal length in cm 4, petal width In cm 5. class: Iris Setosa -Iris Versicolour Iris Virginica To see how well the program "learned", you will then load a file containing testing examples, which will include the same type of Information, but for different Instances. For each test Instance, you will apply the Nearest Nelghbor algorithm to classify the instance. This alporithm works by choosing a class label of the "closest training example where "closest means shortest distance. The distance is comiputed using the fallowing formula: where x,y are two instances (i.e. a training or a testing examplel. sl,sly ae their sepal lengths, sWSWy are their sepal widths, p'x ply are their petal lengths, and pw wy are their petal widths. After yau finish classifying each testing instance, you will then need to cornpare it to the e label that is specified instances divided by the number of total testing instances. for each example and compute the accuracy. A uracy is mrasured as the e nurnber of carrectly classified ot cc