  Exponential growth of the web increased the importance of web document
classification and data mining. To get the exact information, in the form of
knowing what classes a web document belongs to, is expensive. Automatic
classification of web document is of great use to search engines which provides
this information at a low cost. In this paper, we propose an approach for
classifying the web document using the frequent item word sets generated by the
Frequent Pattern (FP) Growth which is an association analysis technique of data
mining. These set of associated words act as feature set. The final
classification obtained after Na\"ive Bayes classifier used on the feature set.
For the experimental work, we use Gensim package, as it is simple and robust.
Results show that our approach can be effectively classifying the web document.
