End-to-End-Sales-Prediction-Model

Comparative Analysis of Sales Prediction with Deployment

End-to-End-Sales-Prediction-Model is a Flask-based web app that allows users to predict sales and visualize sales forecasts using a pre-trained machine learning model. This application provides an intuitive user interface for inputting store, item, year, month, and day of year information to obtain sales predictions.

Description

The Store Sales Prediction Web Application is designed to provide users with quick and accurate sales predictions based on input parameters such as store number, item number, year, month, and day of year. It employs a pre-trained machine learning model to make predictions and showcases the forecasted sales using data visualization tools.

Getting Started

Usage

1. Install the required Python packages using pip and the requirements.txt file:

   pip install -r requirements.txt

2. Run the Flask app by executing the following command in the project directory:

   python app.py

3. Open a web browser and navigate to http://localhost:5000/.

4. On the home page, you can:

   • Select a store, item, year, month, and day of year for which you want to predict sales.
   • Click the "Submit" button to obtain a predicted sales value.

5. The prediction will be displayed on the prediction page along with the input parameters.

   Prediction

Forecasting

Please note that the deployed web application focuses solely on providing predictions using a pre-trained machine-learning model. For more comprehensive forecasting and in-depth analysis, you are encouraged to refer to the accompanying Jupyter Notebook in the repository.