Use python or C/C++

data into classes based on the Background: Pattern recognition is concerned with data's properties. In a real problem, as many as 100 or more properties may be considered. Each property of the data may be regarded as dimension and so, the dimension of a real world problem may be 100 or more. For this assignemnt, we consider a data set with only two attributes. supervised pattern recognition scheme because a well known sample data set is available. Problem statement: It is desired to classify whether a movie is a HIT (1) or a FLOP (0). the scheme that is employed in this problem is known as movie has two attributes: 1. Hit percentage of director (xl) and 2. Hit percentage of cast (x2. Your recognition scheme should use the following training or pre-classified data: uc it (1) o (You should store this data in disk file called trsamp.dat) An incoming pattern is classifed as a Hit or Flop using the nearest neighbor (NN) method. This method works as follows: te the Euclidean distance between the unclassified film [incoming pattern] and all the films in the training data set 2. Find the film in the training sample set nearest to the film to be classified 3. Assign the unknown film the class of the nearest film. Suppose that the attributes of an incoming pattern be (xl,X2). Then the Euclidean distance between this pattern and the jth member of the training set can be calculated as