README.md

nhanesDM https://nhanesdm-5a06a4078888.herokuapp.com/

The dataset, sourced from National Health and Nutrition Examination Survey (NHANES) for educational purposes, is used to develop a website that facilitates machine learning predictions for diabetes and features an interactive dashboard.

Key Components:

Flask Application (app.py):

• Manages routes for rendering templates and handling predictions.
• Provides routes for toggling dark mode and serving the Dash app.

Dash Application (dash_app.py):

• Implements interactive data visualizations with filters for diabetes data.
• Uses Bootstrap for theming and styling.
• Contains a clientside callback to handle theme switching.

Model Utilities (model_utils.py):

• Handles loading of the trained model, scaler, and encoder.
• Provides preprocessing and prediction functions for the model.

Data Preprocessing (dataCleaning.ipynb)

• Data Loading:

  • Load the dataset using pd.read_csv().
  • Display the first few rows to understand the structure of the data.

• Data Preprocessing:

  • Handle missing values by filling them with the mean of each column.
  • Separate features and target variable.
  • Encode categorical features using OneHotEncoder.
  • Scale numerical features using StandardScaler.

• Model Training:

  • Split the data into training and testing sets.
  • Train a Random Forest model and neural network using Keras.

• Model Evaluation:

  • Evaluate the performance of each model using classification reports.

• Model Saving:

  • Save the trained models, scaler, and encoder using joblib.
  • Save the neural network model using Keras' model.save() function.