Backgroud:

Obesity has become a global epidemic that has doubled since 1980, with serious consequences for health in children, teenagers, and adults. Obesity levels in individuals may relate to their eating habits and physical condition. In this assessment, you will be analysing and creating ML models based on a given dataset that contains attributes of individuals with relation to obesity levels.

Dataset filename: obesity_levels.csv

Dataset description: This dataset include data for the estimation of obesity levels in individuals based on their

eating habits and physical condition. The data contains 17 attributes and 2111 records.

Features and labels: The attribute names are listed below. The description of the attributes can be found in this article (web-link).

1. Gender
2. Age
3. Height
4. Weight
5. family_history_with_overweight (family history of overweight)
6. FAVC (frequent high caloric food)
7. FCVC (vegetables per meal)
8. NCP (number of main meals per day)
9. CAEC (any food between meals)
10. SMOKE (smoking)
11. CH2O (daily water intake)
12. SCC (daily consumed calories)
13. FAF (frequency of physical activity)
14. TUE (technology usage)
15. CALC (consumption of alcohol)
16. MTRANS (means of transport)
17. NObeyesdad (obesity levels, i.e. Insufficient Weight, Normal Weight, Overweight Level I, Overweight Level II, Obesity Type I, Obesity Type II and Obesity Type III)

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Questions

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1. Create a machine learning (ML) model for predicting "weight" using all features except "NObeyesdad" and report observed performance. Explain your results based on following criteria:

• What model have you selected for solving this problem and why?
• Have you made any assumption for the target variable? If so, then why?
• What have you done with text variables? Explain.
• Have you optimised any model parameters? What is the benefit of this action?
• Have you applied any step for handling overfitting or underfitting issue? What is that?

2.Create a ML model for classifying subjects into two classes applying following constraints on above dataset. 12 marks

• Use "NObeyesdad" as target variable and rest of them as predictor variables.
• drop samples with value "Insufficient Weight" for "NObeyesdad"
• Group Normal Weight, Overweight Level I, and Overweight Level II into a class, and the other three labels (Obesity Type I, II, III) as the other class.

1. Report classification performance scores. Select scores that you think best for describing the model performance with appropriate justification.
2. Have you taken any step to check generalisability of the model? What is that and how it ensures generalisability.
3. Can you design and develop any other model for solving this problem? If so, then why have you used the reported one? Give your justification.

3.Suppose that a company has a number (>=500) of resorts around the globe. 8 marks

• Identify a list of features (>=5) that can be used to describe these resorts.
• Create a dataset (rows>=500) and explain all variables. You can generate data either synthetically
• or collecting from similar datasets. Submit your created dataset. In addition, please provide links
• in case you have collected the dataset.
• Build a ML model that can help a customer to select appropriate set of resorts based on the
• season of travel. Present and describe the performance of your model.
• Why do we need a ML model for this problem?

data: https://github.com/lifestim/files

Using AgglomerativeClustering unsupervised ML algorithms only

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obesity_levels Gender Age Height Weight family_history_with_overweight FAVC FCVC NCP CAEC SMOKE CH20 SCC FAF TUE CALC MTRANS NObeyesdad Female 2 1.62 64 yes no 2 3 Sometimes no 2 no 0 1 no Public_Transportation | Normal_Weight Female 21 1.52 56 yes no 3 3 Sometimes yes 3 yes 3 0 Sometimes Public_Transportation|Normal_Weight Male 23 18 77 yes no 2 3 Sometimes no 2 no 2 1 Frequently Public_Transportation Normal_Weight Male 27 87 no no 3 3 Sometimes no 2 | no 2 0 Frequently Walking Overweight_Level_ Male 22 1.78 89.8 no no 2 1 Sometimes no 2 no 0 0 Sometimes Public_Transportation | Overweight_Level_II Male 29 .62 53 no yes 2 3 Sometimes no 2 no 0 0 Sometimes Automobile Normal_Weight Female 23 55 yes yes 3 3 Sometimes | no 2 no 1 0 Sometimes Motorbike Normal_Weight Male 22 1.64 53 no no 2 3 Sometimes no 2 no 3 0 Sometimes Public_Transportation Normal_Weight Male 24 1.78 64 yes yes 3 3 Sometimes no 2 no 1 1 Frequently Public_Transportation | Normal_Weight Male 22 172 yes yes 2 3 Sometimes no 2 no 1 1 no Public_Transportation | Normal_Weight Male 26 1.85 105 yes 3 Frequently no 3 no 2 2 Sometimes Public_Transportation Obesity_Type_I Female 21 1.72 80 yes yes 3 Frequently no 2 yes 2 1 Sometimes Public_Transportation| Overweight_Level_II Male 22 .65 56 no no 3 3 Sometimes no 3 no 2 0 Sometimes Public_Transportation Normal_Weight Male 41 1.8 99 no yes 2 3 Sometimes 2 no 2 1 Frequently Automobile Obesity_Type_ Male 1 23 177 yes yes 3 1 Sometimes | no 1 no 1 Sometimes Public_Transportation Normal_Weight Female 1 .7 36 yes no 3 3 Always no 2 yes 2 1 Sometimes Public_Transportation Normal_Weight Male 27 1.93 102 yes yes 1 Sometimes |no 1 no 1 0 Sometimes Public_Transportation | Overweight_Level_II Female 29 1.53 78 no yes 2 1 Sometimes no 2 no 0 0 no Automobile Obesity_Type_I Female 30 1.71 82 yes yes 3 4 Frequently yes 1 no 0 0 no Automobile Overweight_Level_II Female 23 1.65 70 yes no 2 1 Sometimes no 2 no 0 0 Sometimes Public_Transportation|Overweight_Level_I Male 22 1.65 80 yes no 2 3 Sometimes no 2 no 3 2 no Walking Overweight_Level_II Female 52 1.69 87 yes yes Sometimes yes 2 no 0 0 no Automobile Obesity_Type_I 1 Female 22 .65 60 yes yes 3 3 Sometimes | no 2 no 0 Sometimes Automobile Normal_Weight Female 22 1.6 82 yes yes |Sometimes | no 2 no 0 Sometimes Public_Transportation |Obesity_Type_ Male 21 .85 yes yes 2 3 Sometimes | no 2 no 0 1 Sometimes Public_Transportation |Normal_Weight Male 20 LE 50 yes no 2 Frequently yes 2 no 3 2 nc Public_Transportation| Normal_Weight Male 21 1.7 65 yes yes 1 Frequently no 2 no 1 2 Always Walking Normal_Weight Female 23 LE 52 no yes 2 4 Frequently no 2 no 2 1 Sometimes Automobile Normal_Weight Male 1.75 76 yes yes 3 3 Sometimes |no 2 yes 3 1 Sometimes Public_Transportation |Normal_Weight Male 1.68 70 no yes 2 3 Sometimes |no 2 no 2 Frequently Walking Normal_Weight Male 29 1.77 83 no yes 1 4 Frequently no 3 no 0 1 no Motorbike Overweight_Level_I Female 31 1.58 8 yes no 2 1 Sometimes no 1 no 1 0 Sometimes Public_Transportation|Overweight_Level_II Female 24 1.77 76 no no 1 IN 3 Sometimes no 3 no 1 Sometimes Walking Normal_Weight Male 39 1.79 90 no no 2 1 Sometimes no 2 no 0 0 Sometimes Public_Transportation |Overweight_Level_II Male 22 1.65 62 no yes 4 Frequently no 2 no 0 Sometimes Public_Transportation| Normal_Weight