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.
Experimented with the following classifiers:
- SVM
- Random Forest
- Logistic Regression
- Naive Bayes
- AdaBoost
- Gradient Boosting
Also played around cleaning and standardizing the dataset.
- python-2.7.11
- scikit-learn
- numpy
- pandas
- 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
- For SVM: