Demand Forecaster is a project aimed at predicting sales demand using RandomForestRegressor based on historical sales data for various products.
- Clone the repository
git clone <repository-url>
cd <repository-directory>- To install the required dependencies, run the following command:
pip install -r requirements.txt- Create a virtual environment and activate it:
python -m venv venv
source venv/bin/activate # On Windows, use `venv\Scripts\activate`The project utilizes RandomForestRegressor for predicting sales demand based on historical data.
The model is trained on a dataset of sales data, incorporating features such as lagged sales, differences, and rolling averages.
To run the application and predict sales demand:
Open Streamlit Application:
streamlit run main.pyUpload your sales data in CSV format through the interface.
Explore the predicted sales for each product for the upcoming weeks. Find the forecasted results here https://sales-demand-forecaster.onrender.com/ for the dataset i've used.