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  3. Building Recommendation Engines in Python

Exercise

Creating the TF-IDF DataFrame

Now that you have generated our TF-IDF features, you will need to get them in a format that you can use to make recommendations. You will once again leverage pandas for this and wrap the array in a DataFrame. As you will be using the movie titles to do your filtering of the data, you can assign the titles to the DataFrame's index.

The df_plots DataFrame has once again been loaded for you. It contains movies' names in the Title column and their plots in the Plot column.

Instructions

100 XP
  • Create a TfidfVectorizer and fit and transform it as you did in the previous exercise.
  • Wrap the generated vectorized_data in a DataFrame. Use the names of the features generated during the fit and transform phase as its column names and assign your new DataFrame to tfidf_df.
  • Assign the original movie titles to the index of the newly created tfidf_df DataFrame.