Diagnosing Diabetes

Overview

The Diagnosing Diabetes project leverages machine learning to predict diabetes outcomes using logistic regression. The model classifies whether individuals are likely to have diabetes based on their health metrics.

Features

• Data Exploration: Visualizes correlations between features using heatmaps.
• Data Preparation: Splits data into training and testing sets for model evaluation.
• Model Training: Trains a logistic regression model on the training data.
• Evaluation: Assesses model accuracy and provides prediction results.

Getting Started

To run this project locally, follow these steps:

1. Clone the Repository:

   git clone https://github.com/TahaBakhtari/Diagnosing-diabetes.git

2. Navigate to the Project Directory::

   cd Diagnosing-diabetes

3. Install Dependencies::

   pip install numpy pandas seaborn scikit-learn matplotlib

4. Run the Jupyter Notebook::

   jupyter notebook diagnosing_diabetes.ipynb
   

Dataset

The project uses diabetes.csv, containing health metrics such as glucose levels, BMI, and age for diabetes prediction. 0 : no diabetes 1 : diabetes