https://github.com/chandadiya2004/diabetes-prediction
The Diabetes Prediction project utilizes machine learning techniques to determine the probability of an individual having diabetes based on various health metrics like age, BMI, and blood pressure. The prediction model is developed using the Support Vector Machine (SVM) algorithm, which classifies individuals based on these parameters.
https://github.com/chandadiya2004/diabetes-prediction
column-transformer machine-learning pipeline python streamlit svm-model
Last synced: about 1 month ago
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The Diabetes Prediction project utilizes machine learning techniques to determine the probability of an individual having diabetes based on various health metrics like age, BMI, and blood pressure. The prediction model is developed using the Support Vector Machine (SVM) algorithm, which classifies individuals based on these parameters.
- Host: GitHub
- URL: https://github.com/chandadiya2004/diabetes-prediction
- Owner: chandadiya2004
- License: apache-2.0
- Created: 2024-12-27T11:00:58.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-21T13:23:27.000Z (over 1 year ago)
- Last Synced: 2025-10-26T16:41:59.712Z (8 months ago)
- Topics: column-transformer, machine-learning, pipeline, python, streamlit, svm-model
- Language: Jupyter Notebook
- Homepage: https://diabetes-prediction-diya.onrender.com
- Size: 336 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Diabetes Prediction using Machine Learning
This project focuses on predicting whether an individual has diabetes using machine learning techniques based on various health parameters. The dataset includes features like age, BMI, blood pressure, and other health-related factors. The Support Vector Machine (SVM) algorithm is utilized to build an efficient classification model.
# Features:
1. Data Preprocessing: Missing values are handled using SimpleImputer.
Categorical variables are encoded using OneHotEncoder.
2. Modeling: The SVM classifier is implemented for accurate diabetes prediction.
3. Pipeline: A streamlined Pipeline integrates preprocessing and model training, ensuring scalability and simplicity.
4. Evaluation: The model's performance is assessed through metrics such as accuracy and classification reports.
5. Deployment: The project incorporates Streamlit for user-friendly deployment, enabling users to input health details and instantly receive predictions.
# Technologies Used:
Python for development
scikit-learn for machine learning tasks
Streamlit for building an interactive web application
Pickle for saving and reusing the trained model