Personalized Healthcare Dashboard

Project Overview

A machine-learning-based healthcare dashboard designed to present patient information and personalized insights in a clear and accessible format.

The project combines data preprocessing, machine-learning classification, and dashboard visualization to transform healthcare-related data into information that can be easier to understand and analyze.

Problem

Healthcare datasets often contain a mixture of categorical and numerical information. Before applying machine-learning models, this data needs to be properly prepared and standardized.

The project therefore focuses on building a preprocessing workflow that prepares the healthcare data for classification while taking potential class imbalance into consideration.

My Work

Prepared the healthcare dataset for machine-learning processing.

Converted categorical features into numerical representations using LabelEncoder.

Applied StandardScaler to scale numerical features.

Considered class imbalance through the use of class weights.

Built the classification workflow around the prepared data.

Connected the resulting insights to a healthcare-oriented dashboard.

Designed the dashboard to present patient-focused information in a clear way.

Technical Approach

Data → Preprocessing → Feature Transformation → Classification → Insights → Dashboard

The preprocessing stage includes:

Categorical Data

→ Label Encoding

Numerical Data

→ Standard Scaling

Imbalanced Classes

→ Class Weights

Processed Data

→ Machine Learning Classification

Model Insights

→ Dashboard Presentation

Technologies

Python

Machine Learning

Scikit-learn

LabelEncoder

StandardScaler

Data Preprocessing

Data Analytics

Key Features

Patient-focused dashboard

Healthcare data visualization

Machine-learning classification workflow

Categorical data encoding

Numerical feature scaling

Class imbalance handling

Personalized insights