Description
Problem Statement:
Write a program to learn a naïve Bayes classifier and use it to predict class labels of test data. Laplacian
smoothing should be used. The learned classifier should be tested on test instances and the accuracy of
prediction for the test instances should be printed as output. A single program should train the classifier on
the training set as well as test it on the test set.
Data Set Description:
The task is to predict whether a citizen is happy to live in a city based on certain parameters of the city as
rated by the citizens in a scale of 1-5 during a survey.
Attribute Information:
D = decision/class attribute (D) with values 0 (unhappy) and 1 (happy) (Column 1 of file)
X1 = the availability of information about the city services (Column 2 of file)
X2 = the cost of housing
X3 = the overall quality of public schools
X4 = your trust in the local police
X5 = the maintenance of streets and sidewalks
X6 = the availability of social community events
Attributes X1 to X6 have values 1 to 5.
Training Data Filename: data2_19.csv, Test Data Filename: test2_19.csv
Submission Guidelines:
You may use one of the following languages: c/C++/Java/Python. You should name your file as
special purpose library. Numpy/pandas may be used. You should submit the program file only and not the
output/input file. The submitted single program file should have the following header comments:
# Roll # Name # Assignment number # Specific compilation/execution flags (if required)
Please submit the program in moodle by September 4, 2019 midnight (hard deadline). Copying from
friends/web will lead to strict penalties.
