Diabetes Prediction Using Logistic Regression and K-Nearest Neighbors (KNN)

This project aims to predict whether a person has diabetes using two popular machine learning classification algorithms: Logistic Regression and K-Nearest Neighbors (KNN).

Key aspects of the project include:

Data preprocessing and cleaning to handle missing values and outliers

Exploratory Data Analysis (EDA) to understand feature distributions and relationships

Feature scaling to prepare data for KNN algorithm

Building and training classification models using Logistic Regression and KNN

Evaluating model performance using accuracy, precision, recall, and F1-score metrics

Comparing the results of both models to select the best predictor