Welcome to the Diabetic Prediction System by Waleed Ahmed! This system is designed to predict the likelihood of an individual having diabetes based on various features such as glucose levels, blood pressure, BMI, and more. Below, you'll find a comprehensive guide on how to use this system effectively.
The Diabetic Prediction System is a web-based application built using Streamlit, a popular Python library for creating interactive web applications. It leverages machine learning algorithms to analyze and predict the likelihood of diabetes in individuals based on their health data.
To install the necessary dependencies, follow these steps:
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Clone this repository to your local machine:
git clone https://github.com/Waleed-Ahmed-Khan/diabetic-prediction.git
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Navigate to the project directory:
cd diabetic-prediction -
Install the required Python packages:
pip install -r requirements.txt
To run the Diabetic Prediction System, execute the following command:
streamlit run app.pyThis will start the Streamlit server, and you can access the application in your web browser at the provided URL.
In the Uploads section, you can upload your own CSV file containing the relevant data for diabetic prediction. If you don't have a suitable dataset, you can download one from Kaggle. Once you upload your file, it will be processed and displayed for further analysis.
The EDA section allows you to explore the uploaded data in detail. It provides various insights such as summary statistics, data distribution, correlations, and more. You can visualize the data using interactive charts and graphs to gain a better understanding of the dataset.
In the ML section, the uploaded data is used to train and evaluate different machine learning models for diabetic prediction. Models such as Logistic Regression, Decision Trees, Random Forests, SVM, and Neural Networks are trained and compared based on their performance metrics. Confusion matrices and classification reports are provided to assess the effectiveness of each model.
The Prediction section allows you to make predictions for new data based on the trained machine learning models. You can input the relevant features for an individual, and the system will predict whether they are likely to have diabetes or not.
Note: It's recommended to upload your data and perform EDA before proceeding to the ML and Prediction sections for better insights and understanding.
To use the Diabetic Prediction System effectively, follow the steps outlined in the Getting Started section. Once the application is up and running, navigate through the different sections (Uploads, EDA, ML, Prediction) using the sidebar menu. Upload your data, explore insights, train models, and make predictions as needed.
Contributions to the Diabetic Prediction System are welcome! If you have any suggestions, feature requests, or bug reports, please open an issue or submit a pull request on the GitHub repository.
This project is licensed under the MIT License.
if you need any help regarding code and want to make progress in it you are welcome to contact me at my email [email protected] . for more understnading here is the link of the application i am hosting from streamlit application host, click on the following link. Diabetic Prediction System by Waleed Ahmed. Thanks me Later.