Datasets:

Tomerd88
/

obesity-lifestyle-analysis

Age float64 14 61	Gender stringclasses 2 values	Height float64 1.45 1.98	Weight float64 39 173	CALC stringclasses 4 values	FAVC stringclasses 2 values	FCVC float64 1 3	NCP float64 1 4	SCC stringclasses 2 values	SMOKE stringclasses 2 values	CH2O float64 1 3	family_history_with_overweight stringclasses 2 values	FAF float64 0 3	TUE float64 0 2	CAEC stringclasses 4 values	MTRANS stringclasses 5 values	NObeyesdad stringclasses 7 values
21	Female	1.62	64	no	no	2	3	no	no	2	yes	0	1	Sometimes	Public_Transportation	Normal_Weight
21	Female	1.52	56	Sometimes	no	3	3	yes	yes	3	yes	3	0	Sometimes	Public_Transportation	Normal_Weight
23	Male	1.8	77	Frequently	no	2	3	no	no	2	yes	2	1	Sometimes	Public_Transportation	Normal_Weight
27	Male	1.8	87	Frequently	no	3	3	no	no	2	no	2	0	Sometimes	Walking	Overweight_Level_I
22	Male	1.78	89.8	Sometimes	no	2	1	no	no	2	no	0	0	Sometimes	Public_Transportation	Overweight_Level_II
29	Male	1.62	53	Sometimes	yes	2	3	no	no	2	no	0	0	Sometimes	Automobile	Normal_Weight
23	Female	1.5	55	Sometimes	yes	3	3	no	no	2	yes	1	0	Sometimes	Motorbike	Normal_Weight
22	Male	1.64	53	Sometimes	no	2	3	no	no	2	no	3	0	Sometimes	Public_Transportation	Normal_Weight
24	Male	1.78	64	Frequently	yes	3	3	no	no	2	yes	1	1	Sometimes	Public_Transportation	Normal_Weight
22	Male	1.72	68	no	yes	2	3	no	no	2	yes	1	1	Sometimes	Public_Transportation	Normal_Weight
26	Male	1.85	105	Sometimes	yes	3	3	no	no	3	yes	2	2	Frequently	Public_Transportation	Obesity_Type_I
21	Female	1.72	80	Sometimes	yes	2	3	yes	no	2	yes	2	1	Frequently	Public_Transportation	Overweight_Level_II
22	Male	1.65	56	Sometimes	no	3	3	no	no	3	no	2	0	Sometimes	Public_Transportation	Normal_Weight
41	Male	1.8	99	Frequently	yes	2	3	no	no	2	no	2	1	Sometimes	Automobile	Obesity_Type_I
23	Male	1.77	60	Sometimes	yes	3	1	no	no	1	yes	1	1	Sometimes	Public_Transportation	Normal_Weight
22	Female	1.7	66	Sometimes	no	3	3	yes	no	2	yes	2	1	Always	Public_Transportation	Normal_Weight
27	Male	1.93	102	Sometimes	yes	2	1	no	no	1	yes	1	0	Sometimes	Public_Transportation	Overweight_Level_II
29	Female	1.53	78	no	yes	2	1	no	no	2	no	0	0	Sometimes	Automobile	Obesity_Type_I
30	Female	1.71	82	no	yes	3	4	no	yes	1	yes	0	0	Frequently	Automobile	Overweight_Level_II
23	Female	1.65	70	Sometimes	no	2	1	no	no	2	yes	0	0	Sometimes	Public_Transportation	Overweight_Level_I
22	Male	1.65	80	no	no	2	3	no	no	2	yes	3	2	Sometimes	Walking	Overweight_Level_II
52	Female	1.69	87	no	yes	3	1	no	yes	2	yes	0	0	Sometimes	Automobile	Obesity_Type_I
22	Female	1.65	60	Sometimes	yes	3	3	no	no	2	yes	1	0	Sometimes	Automobile	Normal_Weight
22	Female	1.6	82	Sometimes	yes	1	1	no	no	2	yes	0	2	Sometimes	Public_Transportation	Obesity_Type_I
21	Male	1.85	68	Sometimes	yes	2	3	no	no	2	yes	0	1	Sometimes	Public_Transportation	Normal_Weight
20	Male	1.6	50	no	no	2	4	no	yes	2	yes	3	2	Frequently	Public_Transportation	Normal_Weight
21	Male	1.7	65	Always	yes	2	1	no	no	2	yes	1	2	Frequently	Walking	Normal_Weight
23	Female	1.6	52	Sometimes	yes	2	4	no	no	2	no	2	1	Frequently	Automobile	Normal_Weight
19	Male	1.75	76	Sometimes	yes	3	3	yes	no	2	yes	3	1	Sometimes	Public_Transportation	Normal_Weight
23	Male	1.68	70	Frequently	yes	2	3	no	no	2	no	2	2	Sometimes	Walking	Normal_Weight
29	Male	1.77	83	no	yes	1	4	no	no	3	no	0	1	Frequently	Motorbike	Overweight_Level_I
31	Female	1.58	68	Sometimes	no	2	1	no	no	1	yes	1	0	Sometimes	Public_Transportation	Overweight_Level_II
24	Female	1.77	76	Sometimes	no	2	3	no	no	3	no	1	1	Sometimes	Walking	Normal_Weight
39	Male	1.79	90	Sometimes	no	2	1	no	no	2	no	0	0	Sometimes	Public_Transportation	Overweight_Level_II
22	Male	1.65	62	Sometimes	yes	2	4	no	no	2	no	2	0	Frequently	Public_Transportation	Normal_Weight
21	Female	1.5	65	Sometimes	no	2	3	no	no	2	yes	2	2	Sometimes	Public_Transportation	Overweight_Level_II
22	Female	1.56	49	no	yes	2	3	yes	no	1	no	2	0	Sometimes	Walking	Normal_Weight
21	Female	1.6	48	Sometimes	yes	2	3	no	no	1	no	1	0	Sometimes	Public_Transportation	Normal_Weight
23	Male	1.65	67	Sometimes	yes	2	3	no	no	2	yes	1	1	Sometimes	Public_Transportation	Normal_Weight
21	Female	1.75	88	Sometimes	yes	2	3	no	no	3	yes	3	0	Sometimes	Public_Transportation	Overweight_Level_II
21	Female	1.67	75	Sometimes	yes	2	3	no	no	2	yes	1	0	Sometimes	Public_Transportation	Overweight_Level_I
23	Male	1.68	60	no	no	2	4	no	no	2	no	0	0	Frequently	Walking	Normal_Weight
21	Female	1.66	64	no	yes	1	3	no	no	1	yes	0	0	Sometimes	Public_Transportation	Normal_Weight
21	Male	1.66	62	Frequently	yes	2	3	no	yes	2	yes	1	1	Sometimes	Public_Transportation	Normal_Weight
21	Male	1.81	80	no	no	1	3	no	no	2	no	2	0	no	Public_Transportation	Normal_Weight
21	Female	1.53	65	no	no	2	3	no	no	1	yes	0	1	Sometimes	Public_Transportation	Overweight_Level_II
21	Male	1.82	72	Sometimes	yes	1	3	no	no	3	yes	2	0	Frequently	Public_Transportation	Normal_Weight
21	Male	1.75	72	Sometimes	yes	1	3	no	no	3	yes	2	0	Frequently	Public_Transportation	Normal_Weight
20	Female	1.66	60	Sometimes	no	3	3	no	no	2	yes	1	0	Sometimes	Walking	Normal_Weight
21	Female	1.55	50	Sometimes	yes	2	3	no	no	2	no	0	0	Sometimes	Public_Transportation	Normal_Weight
21	Female	1.61	54.5	Sometimes	yes	3	3	no	no	3	yes	0	1	Sometimes	Walking	Normal_Weight
20	Female	1.5	44	Sometimes	yes	2	3	no	no	1	no	0	0	Sometimes	Automobile	Normal_Weight
23	Female	1.64	52	no	yes	3	1	no	no	2	no	2	2	Sometimes	Public_Transportation	Normal_Weight
23	Female	1.63	55	no	no	3	3	yes	no	2	yes	2	1	no	Public_Transportation	Normal_Weight
22	Female	1.6	55	no	no	3	4	no	no	3	no	2	0	Always	Public_Transportation	Normal_Weight
23	Male	1.68	62	Sometimes	no	2	4	no	no	2	no	0	0	Frequently	Automobile	Normal_Weight
22	Male	1.7	70	Sometimes	yes	2	3	no	no	1	yes	0	1	Sometimes	Automobile	Normal_Weight
21	Male	1.64	65	no	no	2	3	no	no	1	yes	0	1	Sometimes	Public_Transportation	Normal_Weight
17	Female	1.65	67	no	yes	3	1	no	no	2	yes	1	1	Sometimes	Walking	Normal_Weight
20	Male	1.76	55	no	yes	2	4	no	no	3	yes	2	2	Sometimes	Public_Transportation	Insufficient_Weight
21	Female	1.55	49	Sometimes	yes	2	3	no	no	3	yes	3	1	Sometimes	Public_Transportation	Normal_Weight
20	Male	1.65	58	Sometimes	yes	2	3	no	no	2	no	3	1	Sometimes	Public_Transportation	Normal_Weight
22	Male	1.67	62	Sometimes	yes	2	1	no	no	2	no	0	0	no	Public_Transportation	Normal_Weight
22	Male	1.68	55	Sometimes	yes	2	3	no	no	2	yes	0	2	Sometimes	Automobile	Normal_Weight
21	Female	1.66	57	no	yes	2	3	no	no	1	yes	1	1	Frequently	Public_Transportation	Normal_Weight
21	Female	1.62	69	no	yes	1	3	no	no	2	yes	0	1	Frequently	Public_Transportation	Overweight_Level_I
23	Male	1.8	90	Frequently	yes	1	3	no	no	2	yes	0	2	Always	Public_Transportation	Overweight_Level_II
23	Male	1.65	95	Frequently	yes	2	3	no	no	2	yes	0	1	Always	Automobile	Obesity_Type_I
30	Male	1.76	112	Frequently	yes	1	3	yes	yes	2	yes	0	0	no	Automobile	Obesity_Type_II
23	Male	1.8	60	Sometimes	no	2	3	no	no	3	yes	0	1	no	Public_Transportation	Normal_Weight
23	Female	1.65	80	no	yes	2	3	no	no	2	yes	0	2	Always	Public_Transportation	Overweight_Level_II
22	Female	1.67	50	Sometimes	no	3	3	yes	no	3	yes	2	1	no	Public_Transportation	Insufficient_Weight
24	Female	1.65	60	no	no	2	3	yes	no	3	yes	1	0	Sometimes	Public_Transportation	Normal_Weight
19	Male	1.85	65	Sometimes	no	2	3	no	no	3	yes	2	1	Sometimes	Bike	Normal_Weight
24	Male	1.7	85	Frequently	yes	2	3	no	no	3	yes	0	1	Frequently	Public_Transportation	Overweight_Level_II
23	Female	1.63	45	no	no	3	3	yes	no	3	yes	2	0	Sometimes	Public_Transportation	Insufficient_Weight
24	Female	1.6	45	no	no	2	3	no	no	2	yes	1	0	no	Public_Transportation	Insufficient_Weight
24	Female	1.7	80	no	yes	2	3	no	no	3	yes	0	0	Sometimes	Public_Transportation	Overweight_Level_II
23	Female	1.65	90	no	yes	2	3	no	no	3	yes	0	1	Sometimes	Public_Transportation	Obesity_Type_I
23	Male	1.65	60	Sometimes	no	2	3	no	no	2	yes	0	0	Sometimes	Public_Transportation	Normal_Weight
19	Female	1.63	58	no	no	3	3	yes	no	2	no	0	0	Sometimes	Public_Transportation	Normal_Weight
30	Male	1.8	91	Sometimes	yes	2	3	no	no	2	yes	0	0	Frequently	Public_Transportation	Overweight_Level_II
23	Male	1.67	85.5	no	yes	2	3	no	no	2	yes	0	1	Always	Public_Transportation	Obesity_Type_I
19	Female	1.6	45	no	no	3	3	yes	no	3	no	2	0	no	Walking	Insufficient_Weight
25	Male	1.7	83	Sometimes	yes	2	3	no	no	2	yes	0	1	Frequently	Public_Transportation	Overweight_Level_II
23	Male	1.65	58.5	no	no	2	3	no	no	2	yes	0	0	Sometimes	Public_Transportation	Normal_Weight
21	Male	1.85	83	no	yes	2	1	no	no	3	yes	1	0	Frequently	Public_Transportation	Normal_Weight
19	Male	1.82	87	no	yes	2	3	no	no	2	yes	0	0	Sometimes	Public_Transportation	Overweight_Level_I
22	Female	1.65	65	no	yes	2	3	yes	no	2	yes	1	0	Frequently	Automobile	Normal_Weight
29	Female	1.7	78	Frequently	yes	3	3	no	no	1	yes	2	1	Sometimes	Automobile	Overweight_Level_II
25	Female	1.63	93	no	no	3	4	no	no	1	no	2	0	Always	Public_Transportation	Obesity_Type_II
20	Female	1.61	64	Frequently	no	3	3	yes	no	2	yes	0	1	Always	Public_Transportation	Normal_Weight
55	Male	1.78	84	Frequently	no	3	4	yes	no	3	yes	3	0	Frequently	Walking	Overweight_Level_I
20	Female	1.6	57	no	no	3	3	no	no	2	no	1	0	Always	Walking	Normal_Weight
24	Female	1.6	48	no	yes	3	3	no	no	2	no	2	0	Sometimes	Public_Transportation	Normal_Weight
26	Male	1.7	70	Frequently	no	3	1	no	no	2	yes	2	0	Frequently	Public_Transportation	Normal_Weight
23	Female	1.66	60	Sometimes	no	2	3	no	no	2	yes	3	0	Sometimes	Public_Transportation	Normal_Weight
21	Female	1.52	42	Sometimes	no	3	1	no	no	1	no	0	0	Frequently	Public_Transportation	Insufficient_Weight
21	Female	1.52	42	Sometimes	no	3	1	no	no	1	no	0	0	Frequently	Public_Transportation	Insufficient_Weight
23	Male	1.72	70	Frequently	no	2	3	no	no	2	no	3	1	Sometimes	Public_Transportation	Normal_Weight

End of preview. Expand in Data Studio

Obesity Levels & Lifestyle Dynamics: A Predictive Research Study

Student: Tomer Dariel Academic Institution: Reichman University (IDC Herzliya) Course: Introduction to Data Science

1. Project Overview

This research explores the critical relationship between lifestyle habits, physical attributes, and genetic predispositions in determining obesity levels. By analyzing multimodal data, the study aims to identify which factors—biological or behavioral—should be prioritized in public health assessments. The research moves beyond the common assumption that weight is purely a result of exercise, seeking to uncover the hidden "anchors" of body mass.

2. Research Questions & Dataset Selection

Source: The dataset is sourced from Kaggle (Estimation of Obesity Levels based on eating habits and physical condition), containing pre-processed multimodal data for health classification.

Size: The dataset consists of 2,111 rows and 17 features, providing a substantial foundation for non-basic statistical analysis.

Features:

Biological and Genetic (3 features): Age, Height, and Family History of Overweight.
Behavioral Habits (7 features): Physical Activity Frequency (FAF), Water Consumption (CH2O), Time using Technology (TUE), and Vegetable Consumption (FCVC).

Target Variable: NObeyesdad, which classifies each sample into 7 categories: Insufficient Weight, Normal Weight, Overweight I/II, and Obesity I/II/III.

Primary Research Question: Which factor serves as the strongest predictor of clinical obesity: physical stature (Height), behavioral habits (Activity), or genetic background (Family History)?

3. Data Cleaning & Preprocessing

Integrity Check: The dataset was found to be complete with no missing values (0 nulls). Duplicate rows were identified and removed to ensure statistical purity and prevent over-representation of specific profiles.

Label Mapping: I manually mapped the categorical target variable (NObeyesdad) into an ordinal scale ranging from 0 to 6. This allowed the analysis to treat obesity as a progression rather than independent labels.

Feature Encoding: Categorical variables such as Family History and Gender were converted into binary numerical values (0 and 1). This critical step enabled these features to be processed in the Advanced Correlation Matrix, measuring the "signal" of genetics against numeric behavioral habits.

4. Key Research Decision: Outlier Handling

Identification: Outliers were detected using Box Plots analyzing the distribution of Weight across Obesity Levels.

Handling and Justification: While standard practice often suggests removing outliers, I made the strategic decision to keep all extreme values.

Reasoning: In obesity research, outliers represent the most clinically significant cases—individuals at the extreme ends of the weight spectrum. Removing them would create a "sterile" dataset incapable of detecting the very conditions (Obesity Type III) that this research aims to predict. These outliers are not errors; they are the core of the study.

5. Height vs. Weight: The Physical Anchor

Visualization:

Analysis: The scatter plot revealed a positive correlation of 0.46.

Insight: While height provides the physical frame for weight, the 0.46 correlation indicates that it only explains less than half of the variance. This finding led to the next stage of the study: investigating what fills the gap between stature and actual body mass.

6. Physical Activity vs. Weight: Challenging the Intuition

Visualization:

Analysis: I investigated the frequency of physical activity (FAF) as a predictor.

Finding: Surprisingly, the correlation was significantly lower than height or genetics.

Insight: This challenges the common intuition that exercise is the primary driver of weight. In this specific population, activity frequency is a supportive factor but lacks the predictive power of biological anchors, suggesting that lifestyle choices are often secondary to a genetic baseline.

7. Cross-Analysis: Family History & Gender

Visualization:

Methodology: I utilized a Pivot Table to calculate the mean weight across gender and genetic lines.

Key Finding: Individuals with a family history of overweight have a drastically higher weight floor.

Insight: The data reveals that males with a family history reached the highest average weights in the sample. This identifies a specific high-risk demographic where genetic predisposition and gender-specific biology intersect.

8. Advanced Correlation Map

Visualization:

Analysis: By including the newly encoded Family History variable in the correlation matrix, the true hierarchy of predictors emerged.

Finding: A remarkable 0.50 correlation was found between Family History and the Obesity Level.

Insight: This was the strongest link in the entire study. It proves that a patient's genetic and environmental background is a more reliable primary differentiator for obesity than their physical height or their self-reported exercise habits.

9. Final Conclusions

Genetics as a Proxy for Risk: The study identifies Family History (0.50) as the most powerful predictor. Predictive models must prioritize genetic background to accurately identify at-risk individuals before they reach extreme obesity levels.

Physical-Habit Dissonance: The gap between the 0.46 (Height) and 0.50 (Genetics) correlations indicates that while we are anchored to our stature, our genetic environment sets our biological limit.

Final Summary: The research concludes that obesity is a multi-modal challenge. The findings suggest that public health interventions should be tailored to individuals with specific genetic risk factors, as behavioral habits alone show a lower direct correlation with clinical weight outcomes in this dataset.

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