Hello, this is for Datascience Application. Below is a question from the textbook fundamentals of machine learning for predictive data analytics, chapter 5, ISBN: 978-0-262-02944-5.

Thank you very much for your help!

Below is a picture of the book and below that is the exercise 3 from chapter 5.

JOHN D. KELLEHER BRIAN MAC NAMEE AOIFE D'ARCY FUNDAMENTALS OF MACHINE LEARNING FOR PREDICTIVE DATA ANALYTIC ALGORITHMS, WORKED EXAMPLES, AND CASE STUDIE 3. The predictive task in this question is to predict the level of corruption in a country based on a range of macro-economic and social features. The table below lists some countries described by the following descriptive fea- tures: .LIFE EXP., the mean life expectancy at birth . TOP-10 INCOME, the percentage of the annual income of the country that goes to the top 10% of earners INFANT MORT., the number of infant deaths per 1,000 births MIL. SPEND, the percentage of GDP spent on the military SCHOOL YEARS, the mean number years spent in school by adult females The target feature is the Corruption Perception Index (CPI and ranges from 0 (highly corrupt) to 100 (very clean).34 COUNTRY CPI measures the perceived levels of corruption in the public sector of c MIL. SCHOOL LIFEMORT.940 TOP-10INFANT LIFE EXR INCOME MORT, SPEND 4.44 0.09 1.07 1.86 0.76 1.95 1.43 6.77 4.72 0.60 2.59 1.31 23.21 47.67 38.23 26.58 74.30 Afghanistan Haiti 59.61 45.00 82.60 19.60 13.30 13.70 14.50 3.60 6.30 3.50 4.40 3.50 4.90 3.40 1799 4.10 2449 5.30 2.8622 10.10 29961 640 36356 7.20 3.774 12.50 5.8069 13.70 7.1357 11.50 7.5360 13.00 7.7751 12.00 8.0461 14.20 8.6725 11.50 8.8442 12.80 9.2985 12.30 9.4627 Nigeria 70.48 75.77 74.87 73.12 81.30 78.51 80.15 80.09 80.24 80.99 82.09 tina 29.98 42.93 28.80 29.85 27.23 28.49 22.07 24.79 25.40 22.18 27.81 China Brazil Israel U.S.A Ireland U.K. Australia Sweden 1.86 1.27 2.40 4.90 New Zealand 80.67 We will ne Russi as ur query country for this question. The tablc beo lists the descriptive features for Russia. COUNTRY ID Russia LIFE ToP-10 INFANT EXR INCOME MORT, SPEND YEARS CPI MIL. SCHOOL 31.68 10.00 12.90 a. What value would a 3-nearest neighbor prediction model using Euclidean distance return for the CPI of Russia? b. What value would a weighted k-NN prediction model return for the CP of Russia? Use k-16 (i.e., the full dataset) and a weighting scheme the reciprocal of the squared Euclidean distance between the neighbor and the query. c. The descriptive features in this dataset are of different types. For exam- ple, some are percentages, others are measured in years, and others are measured in counts per 1,000. We should always consider normalizing our data, but it is particularly important to do this when the descrip- tive features are measured in different units. What value would a 3- nearest neighbor prediction model using Euclidean distance return for the CPI of Russia when the descriptive features have been normalized using range normalization? d. What value would a weighted k-NN prediction model-with k-16 (i.e., the full dataset) and using a weighting scheme of the reciprocal of the squared Euclidean distance between the neighbor and the query- return for the CPI of Russia when it is applied to the range-normalized data? e. The actual 2011 CPI for Russia was 2.4488. Which of the predictions made was the most accurate? Why do you think this was? mmonder system for a large