COMPSCI 762 2022 S1 Week 5 Questions – Regression & Preprocessing
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COMPSCI 762 2022 S1 Week 5 Questions – Regression & Preprocessing
2022
Question 1 – Regression
Consider a data set as the following:
|
X Y |
8 4 |
3 12 |
2 1 |
10 12 |
11 9 |
3 4 |
6 9 |
5 6 |
1. Use the least square method to determine the equation of line of best fit for the data. Then plot the line.
2. Given a new data point X = 9, what is the predicted value of Y?
Question 2 – Data Cleaning
You sent a survey to collect data about customers who buy lunch in the university cafe. Given the following data for the attribute – Age:
13, 15, 16, 16, 19, 20, 20, 21, 22, 22, 25, 25, 25, 25, 30, 33, 33, 35, 35, 35, 35, 36, 40, 45, 46, 52, 70
1. Use smoothing by bin means to smooth these data with 3 bins. Illustrate your steps. Comment on the effect of this technique for the given data.
2. How do you determine outliers in the data?
3. What other methods are there for data smoothing?
Question 3 – Preprocessing on Real-World Data
Your team decides to participate a Kaggle competition on predicting car price. Here is the URL for the Kaggle page: https://www.kaggle.com/ersany/car-price-prediction?resource=download&select= car_price.csv.
1. What preprocessing techniques would you apply in order to train a regression model?
2. There is no missing value in the dataset. What if 20% of body type are missing? What imputation technique should you use?
3. What if 20% of body color are missing? What imputation technique should you use? Can you use the same imputation technique for both attributes?
Note: The documentation of scikit-learn preprocessing is a good starting point. The link can be found here: https://scikit-learn.org/stable/modules/preprocessing.html#preprocessing. Writ- ing code for Question 3 can certainly help you to determine the optimal solution. However, it is not required. You must explain why a particular technique has been selected.
2022-03-26