🏥 Patient Satisfaction Analysis
Patient Satisfaction Analysis is a data-driven project aimed at understanding the key factors that influence patient happiness and healthcare quality. By combining data science, machine learning, and visual storytelling, this project transforms raw survey data into actionable insights that help hospitals enhance service delivery and patient experience.
🚀 Overview
In this project, I analyzed patient satisfaction survey data to uncover what truly impacts how patients perceive their care — from wait times and staff behavior to communication and cleanliness.
Through a combination of EDA, predictive modeling, and data visualization, the project highlights which areas healthcare providers should focus on to improve satisfaction and retention.
🧩 Key Features
Comprehensive Data Cleaning & Preprocessing — Handled missing values, encoded categorical data, and ensured dataset consistency for accurate analysis.
Exploratory Data Analysis (EDA) — Visualized key trends and relationships between patient satisfaction and service metrics.
Machine Learning Models — Built classification models (e.g., Logistic Regression, Random Forest) to predict satisfaction levels and identify top impact features.
Interactive Visualizations — Designed charts and dashboards using Matplotlib, Seaborn, and Tableau to communicate insights effectively.
Insightful Reporting — Summarized data-backed recommendations to improve patient satisfaction outcomes.
⚙️ Tech Stack
Layer | Tools / Technologies |
|---|---|
Language | Python |
Libraries | Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn |
Visualization | Tableau |
Environment | Jupyter Notebook / Google Colab |
Data Source | Public Healthcare Survey Dataset- Medicare/MedicAid (Kaggle) |
📊 Project Workflow
Data Collection: Imported patient feedback and survey datasets.
Data Preprocessing: Cleaned, normalized, and encoded data for analysis.
Exploratory Analysis: Explored satisfaction drivers using statistical analysis and visualization.
Model Building: Applied supervised learning models to predict satisfaction ratings.
Evaluation & Insights: Interpreted model outputs and visualized key predictors in dashboards.
💡 Key Insights
Wait time and staff courtesy emerged as the top drivers of overall satisfaction.
Cleanliness and doctor communication were strongly correlated with positive reviews.
Predictive models achieved ~88% accuracy, providing a reliable way to forecast satisfaction trends.
🧠 Lessons Learned
Strong data preprocessing dramatically improves model accuracy and interpretability.
Combining EDA, ML, and visualization creates a full-circle data storytelling workflow.
Visual dashboards are essential for bridging technical insights with business understanding.
📈 Future Enhancements
Integrate NLP-based sentiment analysis for open-ended patient feedback.
Deploy a Streamlit dashboard for real-time satisfaction monitoring.
Extend dataset to multi-hospital systems for comparative analysis.
Incorporate automated reporting for healthcare management teams.
