Building an AI machine/Deep learning application

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  1. Part 1 –
    1. Following is the link of the Project and Dataset :

Quick Guide to Build a Recommendation Engine in Python & R

MovieLens 100K Dataset

  1. Run the code several times and show the intended output…you also need to EXPLAIN the output…
  2. You will also need to provide output for the following:
    1. Python file containing your code…
    2. Dimensions of the data…
    3. Sample of the data…
    4. Statistical summary of the data…
    5. Class distribution…
    6. One univariate and one multivariate diagram…
    7. Decision Tree…explain the best depth and why?…
    8. Results of training and new data, 80%-20% split…
      1. Accuracy report…what is it telling us?…
      2. Confusion matrix…what is it telling us?…
      3. Classification report…what is it telling us?…
    9. Results of training and new data, 50%-50% split…
      1. Accuracy report…what is it telling us?…
      2. Confusion matrix…what is it telling us?…
      3. Classification report…what is it telling us?…
  3. Part 2 – Updated Code…
    1. Now that you have a working base of code, let’s apply it to a “real world” scenario…
    2. Find an article or video that shows a potentially SIMILAR usage of the application you created in Part 1…
    3. Update the original application so that it “works” for the NEW application…
    4. In this “Movie Recommendation” project, you might find an article on “book recommendations” …you would then update the original program to handle the new scenario…
    5. YOU MUST UPDATE THE ORIGINAL CODE…do not provide entirely new code base.
    6. Run the code several times and show the intended output…you also need to EXPLAIN the output…
    7. You will also need to provide the same output for THIS application, as you did for the ORIGINAL application…specifically:
      1. Python file containing your code…
      2. Dimensions of the data…
      3. Sample of the data…
      4. Statistical summary of the data…
      5. Class distribution…
      6. One univariate and one multivariate diagram…
      7. Decision Tree…explain the best depth and why?…
      8. Results of training and new data, 80%-20% split…
        1. Accuracy report…what is it telling us?…
        2. Confusion matrix…what is it telling us?…
        3. Classification report…what is it telling us?…
      9. Results of training and new data, 50%-50% split…
        1. Accuracy report…what is it telling us?…
        2. Confusion matrix…what is it telling us?…
        3. Classification report…what is it telling us?…
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