This project is a Pima Diabetes Predictor, a machine learning model that uses data from the Pima Indian Diabetes dataset to predict whether a patient has diabetes or not. The project is implemented in Python using the scikit-learn library.
The Pima Indian Diabetes dataset contains information about female patients of Pima Indian heritage, including features such as age, BMI, blood pressure, and glucose level, as well as a binary label indicating whether the patient has diabetes or not.
The project involves data preprocessing, exploratory data analysis, feature engineering, and model training and evaluation. The goal is to build a predictive model that can accurately classify patients as diabetic or non-diabetic based on their features.
The final model is evaluated on a held-out test set and achieves an accuracy of over 80%, demonstrating its effectiveness in predicting diabetes in this population.
The Python notebook for the Pima Diabetes Predictor project can be found here. The notebook contains step-by-step instructions for reproducing the project, including data preprocessing, exploratory data analysis, feature engineering, model training, and evaluation.
The notebook then trains and evaluates one main machine learning models, Support Vector Machines to preduct the outcome of a given set of data.
Overall, the Pima Diabetes Predictor project provides a comprehensive example of a machine learning pipeline for classification tasks, from data preprocessing to model evaluation, and can be adapted as a basic template to other datasets and applications.