Diabetes Prediction Using Logistic Regression

This project involves the development of a machine learning model to predict whether a person has diabetes based on certain medical features. The model is built using Logistic Regression, a popular algorithm for binary classification tasks.

Steps Involved:

1. Data Preparation:

   • Collected and preprocessed a dataset containing medical attributes related to diabetes (e.g., glucose levels, BMI, age, etc.) from Kaggle.
   • Split the dataset into training and testing sets to evaluate the model's performance.

2. Model Development:

   • Implemented a Logistic Regression model using scikit-learn.
   • Trained the model on the training data to learn the relationship between the features and the target variable (whether the person has diabetes or not).

3. Model Serialization:

   • After training, the model was saved (serialized) using Python's pickle module. This allows the trained model to be easily reused without retraining.

4. Streamlit Application:

   • Developed an interactive web application using Streamlit.
   • The app allows users to input medical details (such as glucose levels, BMI, etc.) and, upon pressing a button, predicts whether the individual is likely to have diabetes.
   • The prediction is made by loading the pickled Logistic Regression model and applying it to the input data.

5. How to Run the Application:

   • To run the Streamlit application, clone this repository and ensure all dependencies are installed .
   • Use the command streamlit run main.py to start the application. This will open the app in your web browser, where you can interact with the model and get predictions.