Business Problem
Develop a machine learning model that can predict whether people have diabetes when their characteristics are specified
This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases.
The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.
Dataset Info
The datasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.
Number of Observation Units: 768
Variable Number: 9
| Feature | Definition |
|---|---|
| Pregnancies | Number of times pregnant |
| Glucose | Plasma glucose concentration a 2 hours in an oral glucose tolerance test |
| BloodPressure | Diastolic blood pressure (mm Hg) |
| SkinThickness | Triceps skin fold thickness (mm) |
| Insulin | 2-Hour serum insulin (mu U/ml) |
| BMI | Body mass index (weight in kg/(height in m)^2) |
| DiabetesPedigreeFunction | Diabetes pedigree function |
| Age | Age (years) |
| Outcome | Class variable ( 0 - 1) |
Usage
Script Modes:
--no-debug: Runs full script
--no-tuning: Runs script without tuning
--model-history: Shows models parameters and RMSE scores
Make Commands
To run script with make commands first install MakeFile application on your IDE then run like:
run: Runs full script with tuning
debug: Runs script with debug mode (Set number of row option on scripts/config.file)
notuning: Runs script without tuning
req: Creates requirements.txt
install: Installs requirements.txt
models: Shows models parameters and RMSE scores
Files
- diabetes-prediction.ipynb - Project Notebook
main.py - Main script
configs.py - Configuration Files (Grid & Project settings)
- model_history.py - Show model validation scores from model_info_data.json file
- preprocess.py - Data Preparation script
- train.py - Model Training with Debug option
utils.py - Helper functions
outputs - Output files, includes model pkl objects and model validation history
Requirements
joblib==1.1.0
lightgbm==3.1.1
matplotlib==3.5.2
numpy==1.22.3
pandas==1.4.4
scikit_learn==1.1.2
seaborn==0.11.2
xgboost==1.5.0
Author
- Oğuz Erdoğan - oguzerdo