Diabetes Detection

Description

This project aims to detect diabetes in patients using machine learning algorithms. The dataset used contains various medical predictor variables and one target variable indicating whether or not a patient has diabetes. The goal is to build a model that can accurately predict the presence of diabetes based on the input features.

Features

• Data preprocessing and cleaning.
• Exploratory data analysis (EDA).
• Implementation of various machine learning algorithms.
• Model evaluation and comparison.
• Hyperparameter tuning for the best model.

Requirements

• Python 3.x
• Jupyter Notebook
• Common Python libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn

Installation

1. Clone the repository:

   git clone https://github.com/Ali-Ch-001/Diabetes-Detection.git

2. Navigate to the project directory:

   cd Diabetes-Detection

3. (Optional) Create a virtual environment and activate it:

   python3 -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

4. Install the required packages:

   pip install -r requirements.txt

Dataset

The dataset used for this project can be found in the data directory. It includes various medical predictor variables such as glucose level, blood pressure, insulin level, BMI, age, etc., and a target variable indicating the presence of diabetes.

Usage

1. Navigate to the project directory:

   cd Diabetes-Detection

2. Open the Jupyter Notebook:

   jupyter notebook

3. Open and run the diabetes_detection.ipynb notebook to see the data analysis, model training, and evaluation process.

Steps

• Data Preprocessing: Clean and prepare the data for analysis.
• Exploratory Data Analysis (EDA): Understand the data distribution and relationships.
• Model Implementation: Train various machine learning models.
• Model Evaluation: Compare the performance of different models.
• Hyperparameter Tuning: Optimize the best-performing model.

Contributing

Contributions are welcome! If you have any suggestions, bug reports, or improvements, please create an issue or submit a pull request.

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-branch

3. Commit your changes:

   git commit -m "Description of your changes"

4. Push to the branch:

   git push origin feature-branch

5. Create a new pull request.

Contact

For any questions or feedback, please contact:

• GitHub: Ali-Ch-001