Diabetes-Prediction is a project aimed at developing a predictive model for diabetes using logistic regression. This repository contains the code and resources needed to create a user-friendly application that predicts the likelihood of an individual having diabetes based on various input features.
Diabetes is a prevalent and chronic disease that affects millions of people worldwide. Early detection and accurate prediction of diabetes can significantly improve patient outcomes and guide proactive healthcare interventions. In this project, we utilize logistic regression, a powerful machine learning algorithm, to develop a predictive model for diabetes.
Logistic regression is a binary classification technique used to predict one of two possible outcomes. For diabetes prediction, we train the model on a dataset containing patient attributes such as age, BMI, blood pressure, and glucose levels. These attributes serve as input features, and the model learns to differentiate between diabetic and non-diabetic individuals based on these characteristics.
In this phase, the focus is on preparing the data required for training and evaluating the logistic regression model for diabetes prediction. You can obtain the necessary data from a reliable source and ensure it is in a suitable format for training.
The objective of this phase is to train a logistic regression model using the prepared data. The key steps include:
- Data Splitting: Divide the dataset into training and validation sets.
- Model Training: Apply the logistic regression algorithm to the training data to estimate feature coefficients.
- Model Evaluation: Assess the trained model's performance using validation data.
In this phase, a user interface is developed to allow users to input their features, and the model predicts whether they are diabetic or not.
- main.py: The main Python script that contains the implementation of the logistic regression algorithm, model training, and user interface development.
- GUI.py/: A file containing codes and functions related to the user interface development.
- Data/: A directory containing sample datasets for diabetes prediction.
- Full Implementation of Logistic Regression: The logistic regression algorithm is implemented from scratch, providing a comprehensive understanding of its principles and control over the training process.
- User-Friendly Application: A user interface is created to make the prediction process simple and intuitive.
- Customizable Model Training: Users can train the model on their dataset by providing properly formatted CSV files with attributes and labels.
To get started with the Diabetes-Prediction project, follow these steps:
- Clone this repository to your local machine.
- Prepare the dataset for diabetes prediction.
- Train the logistic regression model using main.py and answer the user interface questions properly to see the result of your prediction.
- Customize the model training by providing your dataset if needed.
Detailed instructions can be found in the project code.
The Diabetes-Prediction project demonstrates the use of logistic regression for predicting the likelihood of diabetes. By implementing the logistic regression algorithm from scratch and creating a user-friendly interface, this project provides insights into both the underlying principles and practical application of machine learning in healthcare.
Feel free to contribute, customize, and utilize this project to enhance your understanding of diabetes prediction and logistic regression.