def METHOD_NAME():
    uniqueClassifxnFeat = []  # vector of unique classifying feature
    maxProbilty = 0  # maximum probability
    # insert each value from classifying feature into vector
    for k, v in dataRecords.items():
        uniqueClassifxnFeat.append(v[numFeatures - 1])
    # delete duplicates from uniqeClassifxnFeat list
    uniqueClassifxnFeat = list(set(uniqueClassifxnFeat))
    # get no. of classifications
    numClassifxn = len(uniqueClassifxnFeat)
    # find probabilities of each classification in classifying feature
    for i in uniqueClassifxnFeat:
        # calculate probability of ith classification
        problilty = countClassification(i) / numData
        # insert this probilty into the probilty dictionary
        classifxnProbilty[i] = problilty
    # finding conditial probabilities of each value in test data w.r.t each classification
    for i in range(numClassifxn):
        # set probilty 0 for each iteration
        problilty = 0
        # get count of matching classifications
        for j in range(len(testData)):
            # set count 0 for each iteration
            count = 0
            for k, v in dataRecords.items():
                # if jth data value matches test data value (sunny, rainy, ...)
                if v[j] == testData[j]:
                    # jth data classifixn matches ith classifxn value (yes, no, ...)
                    if v[numFeatures - 1] == uniqueClassifxnFeat[i]:
                        count += 1  # increment count
            # calculate conditional probability of ith classifxn value
            problilty += count / classifxnCount[uniqueClassifxnFeat[i]]
        # insert the result into result map after multiplying with ith classifxn value's probability
        testDataRes[uniqueClassifxnFeat[i]] = problilty * \
            classifxnProbilty[uniqueClassifxnFeat[i]]
    # find classifxn value with maximum probability
    for k, v in testDataRes.items():
        if v >= maxProbilty:
            maxProbilty = v
            classifiedRes = k
    # display final classification results
    print("\n\nCLASSIFIED RESULT :", classifiedRes)