Description
The following has to be done using Bayesian learning (Naïve Bayes classifier):
1) Randomly divide the data into 80% for training and 20% for testing. Apply the following:
a) Handle the missing values in both train and test set. [5]
b) Encode categorical variables using appropriate encoding method (in-built function
allowed). [5]
c) After completing step (a) and (b), compute 5-fold cross validation on the training set
(normalisation of data is allowed, if required). Print the final test accuracy. [10]
2) Apply PCA (select number of components by preserving 95% of total variance) on the
processed data from step (1).
a) Plot the graph for PCA (in-built function allowed for PCA and visualisation). [20]
b) Use the features extracted from PCA to train your model. Compute 5-fold cross
validation on the training set (normalisation of data is allowed, if required). Print the
final test accuracy. [10]
3) Using the processed data from step (1), apply the following:
a) A feature value is considered as an outlier if its value is greater than mean + 3 x
standard deviation. A sample having maximum such outlier features must be
dropped. [5]
b) Using the sequential backward selection method, remove features. [15]
c) Print the final set of features formed. [5]
d) Compute 5-fold cross validation on the training set (normalisation of data is allowed
if required). Print the final test accuracy. [5]
4) Report and results. [20]
Dataset Description:
Use Train_B.csv as data for this assignment. The “Stay” column will be used as labels.
Submission Guidelines:
Implementation has to be done in Python. No function for Naïve Bayes classifier should be
used. Provide a report on your study with proper description. Keep your codes and report
along with your results in a single folder. Submit the compressed .zip file following
groupno_asgn2.zip naming convention. For example, 5_asgn2.zip for Group no 5.
