# Machine Learning Ball Case Study

I have solved this case study in supervised machine learning using Decision Tree

• Below application uses Decision Tree algorithm to classify the type of ball.
• In this application training set contains two types of balls.
• Features of our training set is weight and type of surface of ball.
• We are using two labels as Tennis and Cricket.
• We train our data set using Decision tree algorithm.

Consider below characteristics of Machine Learning Application :

• Classifier : Decision Tree
• Data Set : Balls Dataset
• Features : Weight & Surface type
• Labels : Tennis and Cricket
• Training Dataset : 15 Entries
• Testing Dataset : 1 Entry
`from sklearn import tree#moduledef MyDataSet(weight,surface):    BallFeatures=[[35,1],[47,1],[90,0],[48,1],[90,0],[35,1],[92,0],                 [35,1],[96,0],[43,1],[110,0],[35,1],[95,0]]#two features as weight and surface     Names=[1,1,2,1,2,1,2,1,1,1,2,1,2]#labels in other words answers to train our model    obj=tree.DecisionTreeClassifier()#object of Decision Tree Class    obj=obj.fit(BallFeatures, Names)#fit method to train our model    result=obj.predict([[weight,surface]])#predict method to test our model    if result==1:        print("It Looks like Tennis Ball\n")    if result==2:        print("It Looks Like Cricket Ball\n")        def main():    print("Demonstration of Ball Data Set by Hrishikesh Deshmukh")    weight=int(input("Enter the Weight\n"))    surface=input("Enter the Surface as Smooth or Rough\n")    if surface.lower()=="smooth":        surface=0    elif surface.lower()=="rough":        surface=1    else:        print("Please enter valid input\n")        exit()    MyDataSet(weight,surface)        if __name__=="__main__":    main()#starter`

Explanation:-

• We have imported Sklearn module in our code
• I have created object of Decision Tree Classifier class which is present in tree module
• In Decision Tree Classifier class there is one instance method:- fit()
• fit():- This is an instance method in Decision Tree Class which accepts two parameters as Features and Label. And this method is used for Training Purpose.
• Once we completed training we have to train our model so for that we accessed predict() method for training purpose.
• predict():- This is also an instance method in Decision Tree class, we use this method for testing purpose. This method accepts 1 parameter as Features. and return result.

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