SAURABH SINGH

PROJECTS

Data Visualization – Interactive Dashboard

Patient Flow & Specialist Analysis

Overview

Developed an interactive data visualization dashboard in Python to analyze patient flow and specialist interactions. The dashboard enables dynamic filtering by time, specialist, and patient type, providing actionable insights into operational trends. Implemented advanced visualizations including polar charts, heatmaps, and correlation matrices to reveal patterns in patient distribution, specialist workload, and interdependencies between metrics.

Technical Features

Data Engineering

Processed and cleaned patient and specialist datasets, handling timestamps, categorical variables, and missing values. Aggregated metrics such as patient wait time, consultation frequency, and specialist load using NumPy and pandas.

Interactive Visualizations

Polar charts to visualize patient distribution across specialists and time periods.
Heatmaps for hourly and daily patient flow patterns across departments.
Correlation matrices to identify relationships between patient volume, specialist interactions, and operational efficiency.
Interactivity: Enabled dynamic filtering and drill-down capabilities using Matplotlib widgets and programmatic controls for selecting time ranges, specialists, and patient categories.
Performance & Optimization: Efficiently handled large datasets by vectorizing computations with NumPy and optimizing Matplotlib plotting routines for responsive interaction.
Reproducibility: Delivered as a Jupyter notebook with modular functions for data processing, visualization rendering, and dashboard export (PNG/PDF).