Hospital Length of Stay (LOS) Prediction Project Overview: This project focuses on predicting the Length of Stay (LOS) for patients admitted with Cardiovascular Diseases (CAD). By leveraging clinical data, the model aims to assist hospital administrators in resource allocation and help clinicians optimize patient care plans.

Dataset Details: 1)The model is trained on clinical datasets including: 2)patients.csv: Demographic information. 3)diagnoses_icd.csv: Clinical diagnosis codes (ICD-9/10). 4)cad_raw_data.csv: Primary features related to cardiovascular health.

Tech Stack 1)Language: Python 2)Libraries: Pandas, NumPy, Scikit-Learn, Matplotlib/Seaborn 3)Environment: VS Code / Jupyter Notebook 4)Version Control: Git & GitHub

Features: 1)Data Preprocessing: Cleaning and merging relational clinical tables. 2)Feature Engineering: Extracting insights from medical codes and patient history. 3)Predictive Modeling: Utilizing Machine Learning algorithms to classify or regress the expected stay duration. 4)Visualization: Analysis of stay distribution across different age groups and clinical complexities.

Project Structure ├── data/ │ ├── raw/
│ └── processed/
├── notebooks/
├── src/
└── README.md How to Run Clone the repository:

Bash git clone https://github.com/V3dha6/LOS-Prediction.git Install dependencies:

Bash pip install -r requirements.txt Run the analysis:

Bash python main.py