Data Pre-Processing and Understanding - The Process of Data Science - IT Assessment Answer

IT Assessment Task

1. Introduction.

a. Explain the dataset with your own words. Focus on the attributes description.

2. Data pre-processing and understanding.

a. Load the data.

b. Explore the data. Plot the distribution of the attributes.

What trends can you find in your data? Are there attributes that are useless at this point?

c. Are there restaurant duplicates in the data? Detect and if there is, clean it.

d. What is the neighborhood with the highest average rating? What are the major characteristics of this neighborhood (e.g., type of restaurant, type of food they offer, etc).

3. Build the best model you can that forecasts the approximate cost of a meal for two people using the attributes location, rating, restaurant type, and cuisine.

a. Explain what is the task you’re solving (e.g., supervised x unsupervised, classification x regression x clustering or similarity matching x etc). (5 marks) b. What models will you choose? Why?

c. Which metrics will you use to evaluate your model?

d. How do you make sure not to overfit?

e. Build your model and verify how it performs (using the metrics you have chosen in Section 3(c)) in your training data. Justify which evaluation approach you are using?(Out of sample validation or Cross-validation). Use a plot to justify your findings. How good is your model?

f. Test your model in your testing set and evaluate its performance. Use a plot to justify your findings. How is it performing compared to your training data?

g. Can you tune your model to perform better? Explain the technique you’re using and justify why it is improving your results.

h. (Bonus) Use relief feature selection to improve your model.

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