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https://github.com/kanika300393/diabetes-prediction-model---web-application-deployment

This project demonstrates the complete pipeline of building, training, and deploying a machine learning model for diabetes prediction using the PIMA Diabetes dataset. The repository includes model building, saving the trained model, creating a predictive system, and deploying it as a Streamlit-based web application. Explore this end-to-end solution
https://github.com/kanika300393/diabetes-prediction-model---web-application-deployment

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This project demonstrates the complete pipeline of building, training, and deploying a machine learning model for diabetes prediction using the PIMA Diabetes dataset. The repository includes model building, saving the trained model, creating a predictive system, and deploying it as a Streamlit-based web application. Explore this end-to-end solution

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README 

        
# Diabetes-Prediction-Model---Web-Application-Deployment



This repository contains code for building a Diabetes Prediction Machine Learning Model using the Support Vector Machine (SVM) algorithm and deploying it as a web application using Streamlit.



## Process Flow

This project consists of the following key steps:



| **Step**                             | **Description**                                                                                                                                                                      |

|--------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|

| **Data Collection and Preprocessing** | - **Dataset**: PIMA Diabetes Dataset is used to predict the likelihood of diabetes based on various health parameters.
 - **Data Cleaning**: Features (X) and target variable (Y) are separated, where `Outcome` indicates diabetes status (1: Diabetic, 0: Non-Diabetic).
 - **Standardization**: Features are scaled to zero mean and unit variance to ensure uniform contribution to the model. |

| **Model Building (Training the SVM Classifier)** | - **Training**: Support Vector Machine (SVM) classifier is trained on X_train and Y_train.
 - **Model Evaluation**: Accuracy scores evaluate performance on training and testing datasets.
 - **Saving the Model**: Pickle is used to save the trained model for future predictions without retraining.                                                  |



### Data Visualisation



![Heat Map](https://github.com/user-attachments/assets/fa118b15-be22-4b66-b204-76a2c8a7142c)



![Pair Plot](https://github.com/user-attachments/assets/7d282208-2f57-469c-8159-c146b439fddf)



![Distribution of Glucose Level](https://github.com/user-attachments/assets/896f75de-40f6-44df-af80-00b0f83521f0)



![Distribution of BMI](https://github.com/user-attachments/assets/006891e7-f004-4625-b581-42628d83faee)



### 3. Creating a Predictive System



Once the model is trained and saved, we create a Python script (predictive_system.py) for making predictions with the trained model.



The script loads the saved model and accepts new input data to predict if a person is diabetic or not.



### 4. Building the Web Application (Streamlit)



A Streamlit web application (diabetes_predictive_web.py) is built to allow users to input their health parameters through a simple web interface and receive real-time predictions.



Users can enter details like the number of pregnancies, glucose level, BMI, and other features, and upon clicking the "Diabetes Test Result" button, the app displays whether the user is diabetic or not.



### 5. Deploying the Web Application



**Deployment**: The web application is ready to be deployed locally or on a cloud platform (like Heroku or AWS).



**Running Locally**: To run the application locally, you need to install the required libraries and run the Streamlit app using the following commands:



This will start the Streamlit app on your local server.



![Web_Application](https://github.com/user-attachments/assets/61c0c045-feb1-4352-abd5-1cd086f3f331)



## Conclusion



This project demonstrates the process of building and deploying a machine learning model to predict diabetes using SVM, and then integrating it into a web application for real-time predictions.