https://github.com/jibbs1703/Classic-ML-Models
This repository contains models that predict the obesity level of patients based on their eating/lifestyle habits and physical condition.
https://github.com/jibbs1703/Classic-ML-Models
data-preprocessing data-science decision-tree-classifier deep-learning feature-engineering machine-learning multiclass-classification obesity-prediction predictive-modeling pytorch xgboost-classifier
Last synced: 28 days ago
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This repository contains models that predict the obesity level of patients based on their eating/lifestyle habits and physical condition.
- Host: GitHub
- URL: https://github.com/jibbs1703/Classic-ML-Models
- Owner: jibbs1703
- Created: 2024-03-15T22:30:53.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-28T15:20:09.000Z (over 1 year ago)
- Last Synced: 2024-11-25T22:18:57.302Z (11 months ago)
- Topics: data-preprocessing, data-science, decision-tree-classifier, deep-learning, feature-engineering, machine-learning, multiclass-classification, obesity-prediction, predictive-modeling, pytorch, xgboost-classifier
- Language: Jupyter Notebook
- Homepage: https://github.com/jibbs1703/Predicting-Obesity-Levels
- Size: 2.36 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Machine Learning Model Development Using Python Frameworks
This repository contains scripts for developing, training and evaluating various machine learning models using python frameworks such as PySpark Mlib (the Machine Learning Framework provided in PySpark), Scikit-Learn, XGBoost and Neural Networks.
The model development process is tracked using MLflow, allowing for transformer and estimator parameters to be tracked, logged and eventually registered throughout the model development process.
## **Models Developed:**
**Multiclass Obesity:**
* Description: Predicts the obesity level of patients based on their eating/lifestyle habits and physical condition.
* Includes: Data preprocessing, model selection,hyperparameter tuning, model evaluation metrics (accuracy, precision, recall, F1-score).
**Adult Income:**
* Description: Classifies the earnings of individuals into two classes - above $50k or below $50k.
* Includes: Data preprocessing, model selection,hyperparameter tuning, model evaluation metrics (accuracy, precision, recall, F1-score).
```bash
docker build -t classic-ml-models-dev .
```
```bash
docker run -it --name classic-ml-models-container -v .:/workspace -p 8888:8888 classic-ml-models-dev
```