Dataset The dataset used in this project contains information related to diabetes, including patient attributes such as BMI, HbA1c level, blood glucose level, and smoking history. The dataset has been preprocessed to handle missing values, encode categorical variables, and address outliers.

Key Features Exploratory Data Analysis (EDA): Explore and visualize the dataset to gain insights into the distribution of features, relationships, and potential patterns.

Data Preprocessing: Handle missing values, encode categorical variables, and address outliers to prepare the data for machine learning models.

Supervised Machine Learning Models:

Logistic Regression Decision Tree Classifier (Include other models you plan to implement) Model Evaluation: Assess model performance using metrics such as accuracy, precision, recall, and F1-score. Utilize techniques like cross-validation and hyperparameter tuning to optimize model performance.

GitHub Repository Structure:

data/: Contains the dataset used for training and testing. notebooks/: Jupyter notebooks detailing the step-by-step process of data exploration, preprocessing, and model implementation. models/: Saved models or model artifacts. results/: Evaluation metrics, visualizations, and summaries.