Adding new model retail sales forecasting

[gos24abhik]

Description

This PR introduces a new machine learning model for retail sales forecasting, designed to predict future sales based on historical data. The model utilizes various features, including date, weekly sales, and holiday indicators, to provide accurate sales predictions.

Issue Resolved

This PR resolves #102 by adding new model retail sales forecaster.

Changes Made

• Added a new model for retail sales forecasting located in the models/retail_sales_forecasting directory.
• Implemented model.py and predict.py to handle model training and predictions.
• Integrated the model into the Streamlit app by adding a new section in app.py to allow users to access the retail sales forecasting feature.

Screenshots or Videos

![Retail Sales Forecaster]

Additional Details

• The model uses the following features for prediction:
  • date: The date of the sales transaction.
  • weekly_sales: The total sales amount for the week.
  • is_holiday: A binary indicator specifying whether the week includes a holiday (1 for holiday, 0 for non-holiday).
• The model aims to help businesses optimize inventory management and enhance decision-making based on predicted demand.

Checklist

• [x] My code follows the current project structure
• [x] I have thoroughly reviewed and updated the requirements.txt file to include any new packages
• [x] The predict.py file includes a properly implemented model_details() function, or the notebook contains this function to print a detailed model report. The model will not be accepted without this function, as it is essential for generating the necessary model details.
• [ ] I have added relevant tests (if necessary).
• [x] I have added comments in the code where needed.
• [x] This PR is submitted under Hacktoberfest.
• [x] This PR is submitted under GirlScript Summer of Code (GSSoC-Extd).