This project focuses on the analysis of simulated real-world substance prescription data to uncover trends in drug usage, identify potential side effects, and explore adherence patterns. The synthetic dataset mimics real-world scenarios, incorporating various drug classes, health conditions, and patient adherence levels.
Understanding patterns in drug prescription data is crucial for healthcare professionals and policymakers. This project aims to provide insights into prescription trends, medication adherence, and potential side effects, utilizing a simulated dataset with realistic characteristics.
The dataset used in this analysis is a synthetic prescription dataset generated to simulate real-world scenarios. It includes information about patients, drug classes, specific medications, associated health conditions, adherence levels, and potential side effects.
To run the analysis, you can use Google Colab, a Jupyter notebook environment in the cloud.
- Load Data: Load the simulated prescription data into a Pandas DataFrame.
- Explore Data: Understand the structure of the dataset and check for missing values.
- Drug Usage Trends: Analyze prescription data to identify commonly prescribed drugs.
- Correlate Medications: Investigate correlations between specific medications and health conditions.
- Side Effects Analysis: Explore potential side effects associated with medications.
- Adherence Patterns: Analyze patterns and factors influencing medication adherence.
The analysis reveals insights into drug usage patterns, correlations between medications and health conditions, potential side effects, and adherence patterns. Visualizations help interpret the findings.
Follow the provided code snippets in the analysis notebook to load your prescription data and perform similar analyses. Customize the code to match your specific dataset and research questions.
Contributions are welcome! Feel free to open issues or pull requests with improvements, bug fixes, or additional features.
This project is licensed under the MIT License.