Doctor Visit Insights is a data analysis project aimed at extracting meaningful insights from healthcare data related to doctor visits. This project leverages machine learning, data visualization, and statistical analysis to analyze patterns in patient visits, predict healthcare outcomes, and optimize healthcare management. The project includes data on patient demographics, diagnosis, treatment plans, and visit history.
- Data Exploration: Explore and analyze doctor visit data to identify key patterns, trends, and anomalies.
- Predictive Modeling: Build machine learning models to predict outcomes such as the likelihood of a follow-up visit or the prediction of healthcare conditions based on demographics and visit history.
- Diagnosis Frequency: Identify the most common diagnoses and trends across various patient groups.
- Data Visualization: Visualize trends, patterns, and correlations in the doctor visit data using graphs, charts, and heatmaps.
- Healthcare Insights: Provide actionable insights to improve patient care, hospital management, and operational efficiency.
To run the Doctor Visit Insights project locally, follow these steps to set up the project.
Ensure you have the following installed:
- Python 3.x: A Python environment to run the project.
- Git: To clone the repository.
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Clone the repository:
First, clone the repository to your local machine:
git clone https://github.com/prashantkumar7541/Doctor-Visit-Insights.git