Update pages/Introduction.py

pages/Introduction.py

@@ -70,10 +70,8 @@ st.write("**Document Frequency:** This tests the meaning of the text, which is v
 st.write("df(t) = occurrence of t in documents")
 
 st.write("Inverse Document Frequency: Mainly, it tests how relevant the word is. The key aim of the search is to locate the appropriate records that fit the demand. Since tf considers all terms equally significant, it is therefore not only possible to use the term frequencies to measure the weight of the term in the paper. First, find the document frequency of a term t by counting the number of documents containing the term:")
-st.write("df(t) = N(t)")
-st.write("where")
-st.write("df(t) = Document frequency of a term t")
-st.write("N(t) = Number of documents containing the term t")
+st.write("df(t) = N(t)\nwhere:\n- df(t) = Document frequency of a term t\n- N(t) = Number of documents containing the term t")
+
 
 
 st.write("Term frequency is the number of instances of a term in a single document only; although the frequency of the document is the number of separate documents in which the term appears, it depends on the entire corpus. Now let’s look at the definition of the frequency of the inverse paper. The IDF of the word is the number of documents in the corpus separated by the frequency of the text.")