https://github.com/abdealijaroli/diabetes-classification-algorithms
Basic implementation of different classifiers for predicting whether a patient has diabetes or not.
https://github.com/abdealijaroli/diabetes-classification-algorithms
diabetes gradient-boost logistic-regression random-forest svm
Last synced: 3 months ago
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Basic implementation of different classifiers for predicting whether a patient has diabetes or not.
- Host: GitHub
- URL: https://github.com/abdealijaroli/diabetes-classification-algorithms
- Owner: abdealijaroli
- License: mit
- Created: 2021-05-17T16:35:28.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2021-05-17T16:55:41.000Z (about 4 years ago)
- Last Synced: 2025-01-12T00:15:23.017Z (5 months ago)
- Topics: diabetes, gradient-boost, logistic-regression, random-forest, svm
- Language: Python
- Homepage:
- Size: 21.5 KB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Diabetes-Classification
Basic implementation of different classifiers for predicting whether a patient has diabetes or not. This is simple binary classification based on the `pima-indian-diabetes-dataset` found on Kaggle. You can find the dataset [here](https://www.kaggle.com/uciml/pima-indians-diabetes-database).
### Classifiers used
Experimented with the following classifiers:
- SVM
- Random Forest
- Logistic Regression
- Naive Bayes
- AdaBoost
- Gradient Boosting
Also played around cleaning and standardizing the dataset.
### Prerequisites
- python-2.7.11
- scikit-learn
- numpy
- pandas
### How to Run
- Clone the repository
- Run the file corresponding to the classifier you want.
- For SVM: `python svm.py`
- For Ada Boost: `python ada_boost.py`
- For Gradient Boost: `python grad_boost.py`
- For Naive Bayes: `python naive_bayes.py`
- For Random Forest: `python random_forest.py`
- For Logistic Regression: `python log_reg.py`