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