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.