🧪 Exploratory Data Analysis on Emergency Department Dataset 📊 Project Overview This repository presents a comprehensive Exploratory Data Analysis (EDA) pipeline conducted on an emergency department dataset. The analysis emphasizes data quality, distribution patterns, statistical relationships, and key operational metrics such as ED visits, diagnoses, and hospital attributes. Visualizations are used extensively to uncover trends and anomalies that can aid strategic decision-making in healthcare settings.

📁 Project Structure plaintext Copy Edit . ├── project.py # Main script for performing EDA ├── eda_summary.txt # Auto-generated summary report of analysis ├── eda_plots/ # Directory containing all generated plots └── README.md # Project documentation ⚙️ Features Robust missing value handling and imputation

Automated outlier detection and treatment using IQR method

Data type analysis and categorical exploration

Distribution visualization via histograms, boxplots, and heatmaps

Correlation analysis with Pearson matrix and significance highlights

Feature engineering using binning techniques

Advanced visual storytelling with:

Count plots

Stacked bar plots

Regression plots

Interactive Plotly scatter plots

Summary report (eda_summary.txt) automatically generated

🏁 How to Run

Prerequisites Ensure the following libraries are installed:

bash Copy Edit pip install pandas numpy matplotlib seaborn plotly scipy 2. Clone the Repository bash Copy Edit git clone https://github.com/your-username/emergency-department-eda.git cd emergency-department-eda 3. Update File Path Update the dataset path in project.py:

python Copy Edit file_path = "C:/Users/hp/Desktop/python_dataset.csv" Replace with your actual CSV file path or place the CSV in the project directory and update the relative path accordingly.

Run the Script bash Copy Edit python project.py All plots will be saved to the eda_plots/ directory and a summary will be logged in eda_summary.txt.

📈 Sample Outputs Heatmap of missing values

Boxplots post outlier treatment

Correlation matrix

Histograms of numerical features

Interactive scatter plot using Plotly

Regression analysis with slope, intercept, R² and p-value

🧠 Use Cases Healthcare operations planning

Data-driven resource allocation

Early anomaly detection in ED visit patterns

Foundational layer for machine learning modeling

📌 Notes Script includes automatic directory creation for plot storage.

Outliers are capped (not removed) to preserve dataset size.

Categorical variables are explored using count plots and cross-tabulations.

🤝 Contributions Contributions are welcome! Please fork the repository and submit a pull request with enhancements or bug fixes.

Pythonproject

Install / Use

README