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Diabetes Prediction

Overview

This project aims to develop a machine learning model to predict the likelihood of diabetes in patients. By analyzing a dataset of medical information, we train a model that can classify individuals as diabetic or non-diabetic based on specific health parameters.

Dataset

We use the PIMA Indians Diabetes Database, which includes the following features:

  • Pregnancies: Number of times pregnant
  • Glucose: Plasma glucose concentration
  • BloodPressure: Diastolic blood pressure (mm Hg)
  • SkinThickness: Triceps skinfold thickness (mm)
  • Insulin: 2-Hour serum insulin (mu U/ml)
  • BMI: Body mass index (weight in kg/(height in m)^2)
  • DiabetesPedigreeFunction: Diabetes pedigree function (a function that scores likelihood of diabetes based on family history)
  • Age: Age (years)
  • Outcome: Binary variable indicating diabetes (1) or not (0)

Requirements

  • Python 3.x
  • Libraries:
    • pandas for data manipulation
    • numpy for numerical operations
    • scikit-learn for machine learning algorithms
    • matplotlib and seaborn for data visualization

Getting Started

  1. Clone the repository: 
    git clone https://github.com/username/diabetes-prediction.git
  2. Navigate into the project directory: 
    cd diabetes-prediction
  3. Run the main script: 
    python main.py

Model Training

  1. Data Preprocessing:

    • Handle missing values, if any.
    • Feature scaling to standardize input data.

  2. Model Selection:

    • Experiment with algorithms such as Logistic Regression, Decision Trees, and Random Forest.

  3. Evaluation:

    • Evaluate using metrics such as accuracy, precision, recall, and F1-score.

Results

The best model achieved an accuracy of X% on the test data. Additional metrics:

  • Precision: Y%
  • Recall: Z%
  • F1 Score: A%

Contributing

Contributions are welcome! Feel free to submit issues or pull requests.

License

This project is licensed under the MIT License.