Writing Question

1. Refer back to assignment 01.04 (Titanic Demo with Orange).
2. Make sure that the original random forest modeling, prediction and results are completed correctly (refer to lecture).
3. Add two (2) more ML algorithms (listed below–kNN, neural net) and compare how well the models they generate predict the target variable for the test data set after being trained on the same training dataset.
   1. ML algos to add:
      1. kNN
      2. neural network
   2. Example screenshot of final work surface:
      • 
4. You will submit a SINGLE Word document with the following contents:
   1. brief discussion of the outcomes, comparing how well each model performed (minimum 150 words)
      1. discuss accuracy, false positives and false negatives, and any other metrics or topics we’ve covered so far
   2. Define each of the three (3) ML algorithm in your own words–use the slides, course materials or any other external references (e.g., Google).
      1. Minimum 50 words for each algorithm (Random Forest, kNN, Neural Network). Total minimum of 150 words.
   3. a single screenshot (pasted into doc) that shows your work surface in Orange after completing #3 above
   4. at least one more screenshot (pasted into doc) showing the confusion matrix outcome(s) for the three model predictions

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