Kaggle problem: Predict the onset of diabetes based on diagnostic measures.
First, create an environment with conda where we will pre-install python 3.9:
conda create --name PimaIndianDiabetes python=3.9
Then activate the environment and change directory:
conda activate PimaIndianDiabetes
cd anaconda3/envs/PimaIndianDiabetes
Use pip install to install the packages needed to run the code. These are the following: pandas, numpy, matplotlib, seaborn, scikit-learn and xgboost.
Download the dataset from Kaggle: https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database
Then you can download main.py and run it in the terminal:
python main.py
The dataset has many different variables: Pregnancies - Number of times pregnant
Glucose - Plasma glucose concentration a 2 hours in an oral glucose tolerance test
Blood Pressure - Diastolic blood pressure
SkinThickness - Triceps skin fold thickness
Insulin - 2-Hour serum insulin
BMI - Body Mass Index
DiabetesPedigreeFunction - Diabetes pedigree function
Age - Age
Outcome - Whether or not the person is diabetic
The variable 'Outcome' is the target and all the other variables are the predictors. Therefore, we use the predictors in four different ML models to find out the target value.
First, we standardize the variables to follow a normal distribution with a mean of 0 and standard deviation of 1. We do that by substracting the mean value and dividing with the standard deviation of each variable.
Then, we split the data into two sets, training and testing.
Then we apply these four ML models: Logistic Regression Classifier
Decision Tree Classifier
Random Forest Classifier
XGBoost Classifier
Finally, we plot the accuracy of each model.
