https://github.com/singhxtushar/diabetes-prediction-classification
This model predicts the Diabetes by using the Logistic Regression or SVC with using the Flask app
https://github.com/singhxtushar/diabetes-prediction-classification
decision-tree-classifier flask-application logistic-regression machine-learning-algorithms svc-svm
Last synced: about 2 months ago
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This model predicts the Diabetes by using the Logistic Regression or SVC with using the Flask app
- Host: GitHub
- URL: https://github.com/singhxtushar/diabetes-prediction-classification
- Owner: SINGHxTUSHAR
- License: mit
- Created: 2023-10-01T11:18:42.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-10-11T07:44:15.000Z (about 2 years ago)
- Last Synced: 2025-05-19T21:44:47.019Z (5 months ago)
- Topics: decision-tree-classifier, flask-application, logistic-regression, machine-learning-algorithms, svc-svm
- Language: Jupyter Notebook
- Homepage:
- Size: 135 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Diabetes-Prediction-LogisticRegression
This project predicts Diabetes for a person using the Logistic Regression Algorithm of Supervised Machine Learning.
### Steps Involved:
- Install Dependencies and Setup :
Install using the requirements file provided -
```
pip install -r requirements.txt
```
- Performing the EDA and Feature Engineering on the Data set.
- Segregate the dependent and independent variables.
- Separate dataset into train and test.
- In this step, I have done the Standard Scaling- Standardization and using pickling for the raw unseen data points.
- Train the Model using the Logistic Regression Algorithm.
- Perform the Hyperparameter Tuning using the GridSearch CV.
- Finally, obtain the Accuracy, Precision and Recall for the Trained data.
---
### Important Features Used :
{
"Pregnancies",
"Glucose",
"BloodPressure",
"SkinThickness",
"Insulin",
"BMI",
"DiabetesPedigreeFunction",
"Age"
}
### To run the flask application
```
python app.py
```
---
##### This project is Deployed on the AWS by using the services like Elastic BeanStalk and CodePipeLine.
----