MediPrice AI - Pharmaceutical Price Prediction

This project is a data science application that predicts pharmaceutical prices using machine learning. It utilizes a Random Forest model to provide accurate price predictions based on various features of pharmaceutical products.

Project Structure

• app.py: The main application file containing the Flask web application
• Indian_Pharmaceuticals.ipynb: Jupyter notebook containing the data analysis and model development process
• model_features.pkl: Pickle file containing the preprocessed features for the model
• rf_model.pkl: Pickle file containing the trained Random Forest model
• requirements.txt: List of Python dependencies required for the project

Setup Instructions

1. Clone the repository:

git clone https://github.com/maitrisavaliya/MediPrice-AI.git

2. Create a virtual environment (recommended):

python -m venv venv
.\venv\Scripts\activate

3. Install the required dependencies:

pip install -r requirements.txt

Usage

1. Run the Flask application:

python app.py

2. Open your web browser and navigate to the local server address shown in the terminal (typically http://localhost:5000)

3. Enter the required pharmaceutical product details in the form to get price predictions

Model Information

The project uses a Random Forest model trained on Indian pharmaceutical data. The model takes into account various features of pharmaceutical products to predict their prices accurately.

Large Files (.pkl)

This project contains large pickle files that are handled using Git Large File Storage (Git LFS). To work with these files:

1. Install Git LFS on your system
2. Pull the repository with LFS support

For more information about handling large files, see the Git LFS section below.

Contributing

If you'd like to contribute to this project, please fork the repository and create a pull request with your proposed changes.