metadata
license: mit
library_name: scikit-learn
tags:
- regression
- linear-regression
- obesity
datasets:
- ObesityDataSet_raw_and_data_sinthetic.csv
model-index:
- name: Obesity Weight Prediction Model
results:
- task:
type: regression
name: Weight prediction (kg)
dataset:
name: ObesityDataSet_raw_and_data_sinthetic.csv
type: tabular
metrics:
- type: mean_squared_error
value: 511.55
- type: r2
value: 0.2777
Obesity Weight Prediction Model — Linear Regression
Overview
This model predicts a person’s weight (kg) based on height (m) and age (years) using a Linear Regression model from scikit-learn.
Training
| Detail | Value |
|---|---|
| Algorithm | LinearRegression() |
| Features | Height, Age |
| Target | Weight |
| Train/Test Split | 75% / 25% |
| Random State | 42 |
| Dataset | ObesityDataSet_raw_and_data_sinhtetic.csv |
Performance
| Metric | Score |
|---|---|
| MSE (Mean Squared Error) | 511.55 |
| R^2 Score | 0.2777 |
These results indicate that height and age alone do not fully explain weight — important factors like diet, genetics, and exercise are missing.
Visualization
Below is a scatter plot showing predicted vs true weights:
The wide spread around the regression line shows prediction uncertainty for heavier individuals.
Limitations
- Only two features used → reduced explanatory power
- Synthetic dataset — not reflective of real population variation
- Performance not suitable for real-world medical decisions
This model is intended for educational use only.
Strengths
- Easy to interpret
- Fast and simple
- Good educational model
Weaknesses
- Low accuracy
- Missing key health variables
- Not production-ready
Citation
- "Estimation of Obesity Levels Based On Eating Habits and Physical Condition ." UCI Machine Learning Repository, 2019, https://doi.org/10.24432/C5H31Z.