Team-Go

• Extracted some tables on the sql database
• loaded, copied and merged both post and user data
• Due to high amount of missing data, columns with missing data was removed
• Got the TF-IDF score or frequency of a word occurring in a document, i.e the title
• defining a function that takes in a post title as an input and outputs a list of the 10 most similar post.
• Construct a reverse map of indices and movie titles
• Got the index of the post given its title.
• Got the list of cosine similarity scores for that particular post with all posts. Convert it into a list of tuples where the first element is its position and the second is the similarity score.
• Sorted the aforementioned list of tuples based on the similarity scores; that is, the second element.
• Got the top 10 elements of this list. Ignore the first element as it refers to self (the post most similar to a particular post is the post itself).
• Returned the titles corresponding to the indices of the top elements.

Article_prediction

• open on colab
• call the get_recommendation function and pass in any post title in the post data to bring up similar post title recommendation

Recommender System KNN testing

• Has models for posts and users recommendations
• Open with Jupyter
• add all files to the same directory as the notebook
• Run testing_model function and follow intructions within

Data Used

• Data used were extracted in csv format from the sql database provided using Xampp
• Data were then loaded and read using pandas
• Below is the link to the extracted data
• https://www.dropbox.com/sh/0zmpvv3d7f525i5/AAAz96dXMfu2jr2Nfd_ia4uma?dl=0