Diabetes Prediction Model

Description:

This project builds a machine learning model to predict whether a patient has diabetes based on medical attributes. The process includes data loading, preprocessing (such as handling missing values and encoding), training a logistic regression model, evaluating its accuracy, and saving the trained model for future use.

Main Steps:

Loaded and prepared the dataset from diabetes.csv

Handled missing values using mean imputation

Applied one-hot encoding with get_dummies

Split data into training and testing sets

Trained a Logistic Regression model

Evaluated performance using accuracy score

Saved the model using Joblib for future deployment

Tools & Libraries Used:

Python

Pandas

Scikit-learn (LogisticRegression, train_test_split, accuracy_score)

Joblib (for model persistence)