Predicting early hospital readmission rates for patients with Diabetes

Used UCI Machine Learning Repository’s Diabetes 130-Hospital Dataset to find the best fitting model for predicting early hospital admission rates in Diabetic patients
Performed feature engineering steps such as removing unimportant features, replacing and grouping feature values, one hot encoding categorical features and rescaling numerical features
Employed SMOTE to tackle class imbalance in the target feature
Performed Grid Search on the following models: Logistic Regression, Random Forest, and Neural Network to select the best model using Cross Validation

Name		Name	Last commit message	Last commit date
Latest commit History 24 Commits
.ipynb_checkpoints		.ipynb_checkpoints
dataset_diabetes		dataset_diabetes
images		images
mltools		mltools
.gitignore		.gitignore
781670.pdf		781670.pdf
Logistic_regression.ipynb		Logistic_regression.ipynb
Project Report.pdf		Project Report.pdf
README.md		README.md
ensemble_pipeline.ipynb		ensemble_pipeline.ipynb
feature_transformations.ipynb		feature_transformations.ipynb
ml_project.ipynb		ml_project.ipynb
neural_net_pipeline.ipynb		neural_net_pipeline.ipynb
preliminary_model_attempt.ipynb		preliminary_model_attempt.ipynb
rf_model.ipynb		rf_model.ipynb
train_test_split.ipynb		train_test_split.ipynb

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