/Diabetes-Prediction-Dataset-Machine-Learning

Diabetes Prediction Dataset

Primary LanguageJupyter Notebook

Diabetes Prediction using Machine Learning

This project focuses on predicting diabetes using machine learning algorithms, particularly with sklearn and pandas. The dataset used for this project is sourced from Kaggle. Below is an overview of the tools and libraries used, as well as the model-building process.

Dataset

The dataset for this project can be found on Kaggle: Diabetes Prediction Dataset.

You can view the notebook walkthrough here: Diabetes Prediction Dataset Notebook.

Project Workflow

  1. Data Preprocessing:

    • Using pandas to load and manipulate the data.
    • Cleaning and handling missing values in the dataset.

  2. Feature Selection:

    • Using sklearn to select features that contribute the most to the prediction outcome.

  3. Model Building:

    • Several models are built using sklearn, including decision trees and other classifiers.
    • Performance evaluation is conducted using metrics such as accuracy, precision, recall, and F1 score.

Dependencies

Make sure to install the following libraries:

pip install pandas
pip install scikit-learn