• Welcome to the Diabetes Prediction System, the second project of my internship at MeriSKILL.

• As a data analyst, I've developed this Python-based application to predict the likelihood of an individual having diabetes using machine learning techniques.

• The predictive model, built with scikit-learn's logistic regression, boasts an accuracy score of approximately 0.81. To make the system accessible and user-friendly, I've deployed it using Django, creating a local server for seamless interaction.

• Video Presentation: Click here

• Linkedin Post: Click here

• Machine Learning Model: Utilizes logistic regression for diabetes prediction, achieving an accuracy score of approximately 0.81.

• Web Application: The model is deployed using Django, providing a user-friendly interface for input and displaying the prediction results.

• NumPy: Fundamental package for scientific computing with Python.

• Pandas: Data manipulation and analysis library.

• Seaborn: Statistical data visualization.

• scikit-learn: Machine learning library for classification, regression, and clustering.

• joblib: Library for lightweight pipelining in Python.

1. Clone the repository:

   git clone https://github.com/MohdAkif919/Diabetes-Prediction-System.git
   

2. Navigate to the project directory:

   cd Diabetes-Prediction-System
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

1. Run the Django development server:

   python manage.py runserver
   

2. Access the application:

   • Open your web browser and navigate to http://127.0.0.1:8000/.

3. Input relevant features:

   • Fill out the form with the required information. This typically includes input fields for features such as age, BMI, blood pressure, etc.

4. Get the prediction:

   • Click the "Submit" button to send the input data to the machine learning model.
   • The system will then provide a prediction for the likelihood of diabetes based on the input values.

1. Home Page

2. Non-Diabetes Patient

3. Diabetes Patient

This project serves as the second project in my internship at MeriSKILL, where I hold the position of a data analyst. I am excited to contribute to the organization's goals and further enhance my skills in data analysis.