1. Download the dataset file that is provided on the Blackboard and browse its content

2.Read the data into a dataframe

3. Apply the cosine similarity using sklearn . Use the normal cosine similarity model that is computed using the below formula. This is different from the first part in which we used the adjusted cosine similarity (the one centered around the mean). For more information about how to use the cosine_similarity function

4. Based on the top 2 neighbor items, predict the rating for item5 using the cosine similarity that you computed in the previous step and the prediction formula that we used in the class (it is the same prediction formula that is implemented in the first part of this lab).

\begin{tabular}{|l|r|r|r|r|r|r|r|} \hline & \multicolumn{1}{|c|}{ A } & \multicolumn{1}{c|}{ B } & C & D & E & \multicolumn{1}{|c|}{ U } \\ \hline 1 & & Alice & User1 & User2 & User3 & User4 \\ \hline 2 & item1 & & 5 & 3 & 4 & 3 & 1 \\ \hline 3 & item2 & & 3 & 1 & 3 & 3 & 5 \\ \hline 4 & item3 & & 4 & 2 & 4 & 1 & 5 \\ \hline 5 & item4 & & 4 & 3 & 3 & 5 & 2 \\ \hline 6 & item5 & & 3 & 5 & 4 & 1 \\ \hline 7 & & & & & & & \end{tabular} 1. Download the dataset file that is provided on the Blackboard and browse its content 2. Read the data into a Apply the cosine similarity using Use the normal model that is computed using the below formula. This is different from the first part in which we used the (the one centered around the mean). For more information about how to use the function, check this link for example ABAB=i=1nAi2i=1nBi2i=1nAiBi Based on the top 2 neighbor items, predict the rating for item 5 using the cosine similarity that you computed in the previous step and the prediction formula that we used in the class (it is the same prediction formula that is implemented in the first part of this lab)