10% OFF Diabetes Prediction Using Machine Learning Based Web App
Diabetes Prediction Using Machine Learning is a Python-based web application that estimates diabetes risk using clinical information such as glucose level, BMI, age, blood pressure, and insulin levels. The project uses Flask, Scikit-learn, and a trained machine learning model to display prediction results through a web interface.
Read more- Built with
- PythonMachine LearningBootstrapHTML/CSS
- You get
- Source codeDatabaseProject reportPPTSetup guide
- Delivery
- Instant download after payment
Project Overview
Diabetes Prediction Using Machine Learning is a web application developed using Python and Flask. It estimates diabetes risk from clinical information entered by the user, including glucose level, BMI, age, blood pressure, and insulin levels.
The project uses the PIMA Indians Diabetes Dataset. Its machine learning workflow includes data preprocessing, model training, performance comparison, and saving the selected trained model. The Flask application connects the input form to the machine learning model and displays the prediction result.
Available Features
- Patient Data Input: Enter clinical details such as glucose level, BMI, age, and insulin level.
- Diabetes Risk Prediction: Generate a prediction using the trained machine learning model.
- Real-Time Results: Display the prediction after the user submits the form.
- Web Interface: Use a simple, responsive interface built with HTML and CSS.
- Machine Learning Integration: Connect the trained model with the Flask application.
- Pre-Trained Model: Load the saved model using Joblib.
- Data Preprocessing: Clean the dataset and standardize relevant input features for model training.
- Model Comparison: Evaluate algorithms such as Logistic Regression, Decision Tree, and Random Forest during development.
Important: This application provides an experimental machine learning prediction, not a medical diagnosis. Results should not replace evaluation by a qualified healthcare professional.
Which Technologies Are Used in This Project?
- Python: Main programming language.
- Flask: Backend framework for routing and form handling.
- HTML: Structures the web interface.
- CSS: Styles the user interface.
- Pandas: Data loading and preprocessing.
- Scikit-learn: Machine learning model training and prediction.
- Joblib: Saving and loading the trained model.
- PIMA Indians Diabetes Dataset: Dataset used for model development.
More Details and Full Documentation
Visit the official UPDATEGADH project page for the complete description, features, and installation instructions.
Diabetes Prediction Using Machine Learning Based Web App – UPDATEGADH
Watch Project Demo
Watch the Car Price Prediction System Using Machine Learning demo on YouTube
- Diabetes Dataset Loading
- Data Cleaning and Preprocessing
- Feature Standardization
- Machine Learning Model Training
- Model Comparison and Selection
- Trained Model Saving and Loading
- Patient Data Input Form
- Diabetes Risk Prediction
- Prediction Result Display
- Flask Web Application
Step 1: Install Python 3 and Visual Studio Code.
Step 2: Extract the project files and open the project folder in VS Code.
Step 3: Open Terminal > New Terminal.
Step 4: Navigate to the Flask application directory
cd flask
Step 5: Create a virtual environment
python -m venv venv
Step 6: Activate the environment on Windows
venv\Scripts\activate
On macOS or Linux
source venv/bin/activate
Step 7: Install the required packages
pip install -r requirements.txt
Step 8: Check that the required model file and dataset are present. The article identifies diabetes.csv as the dataset and model.pkl as the saved model.
Step 9: Run the Flask application
python app.py
Step 10: Open the local URL displayed in the terminal.
Stuck while running it? Chat with us on WhatsApp for free installation help.
No reviews yet. Be the first to review this project.
