Pharmaceutical Analysis Project

Overview This project utilizes data science and machine learning techniques to analyze pharmaceutical data, focusing on predicting outcomes based on prescription and fulfillment timelines. The goal is to optimize healthcare operations and improve patient care through data-driven insights.

Project Structure

Data Preprocessing: Raw data is cleaned and transformed to extract meaningful features. Time-based features are converted into actionable insights (e.g., converting days to hours for accurate prediction).

Machine Learning Model: A Decision Tree classifier and regressor were then implemented to predict categorical and continuous fulfillment statuses, respectively. This model is chosen for its ability to handle both categorical and continuous data effectively.

Evaluation: Model performance is evaluated using accuracy metrics. The accuracy score reflects how well the model predicts outcomes compared to actual data.

Tools and Technologies

Python: Programming language used for data analysis and machine learning. Pandas: Data manipulation and analysis library. Scikit-learn: Machine learning library for building and evaluating models. Jupyter Notebooks: Development environment used for interactive data exploration and model development. Installation To run the project locally, ensure you have Python installed along with the necessary libraries

Interpret Results: Review accuracy scores and predictions to understand how well the model performs in predicting fulfillment statuses based on prescription timelines. Contributing: Contributions are welcome! Please fork the repository, create a new branch, make your enhancements, and submit a pull request.