Diabetes Predictor App is used to predict whether a person has diabetes or not based on the given input. Created using python's scikit-learn, Fastapi, numpy and joblib packages.
The dataset consist of several predictor (independent) variables and one target (dependent) variable, Outcome. Independent variables include the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.
The data contains the following columns:
| Feature Name | Feature Description |
|---|---|
| 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) |
| Diabetes pedigree function | Diabetes pedigree function (a function which scores likelihood of diabetes based on family history) |
| Age | Age (years) |
| Outcome | Class variable (0 or 1) 268 of 768 are 1, the others are 0 |
Open Anaconda prompt and create new environment
conda create -n your_env_name python = (any_version_number > 3.10.4)
Then Activate the newly created environment
conda activate your_env_name
Clone the repository using git
git clone gh repo clone Baktho-SN/Diabetes-Predictor-web-app
Change to the cloned directory
cd <directory_name>
To install all requirement packages for the app
pip install -r requirements.txt
Then, Run the app
uvicorn main:app --reload
