Jewelry Enterprise Corporation Sales Forecasting

Overview

This project aims to apply time series analysis and sales forecasting techniques to the sales data of Jewelry Enterprise Corporation. The objective is to enhance the company's ability to predict future sales trends and make informed business decisions.

Dataset

The dataset consists of daily sales transactions from 2016 to the end of 2023. The key attributes of the dataset are as follows:

• Invoice_Date: Date of the transaction.
• StoreID: Unique identifier for each store.
• Actual_Value: Total sales value.
• Tranx: Number of transactions.
• Ticket_Size: Average transaction size.

Repository Structure

• data/: Contains the dataset files.
• notebooks/: Jupyter notebooks for exploratory data analysis and model development.
• scripts/: Python scripts for data preprocessing and model training.
• results/: Output files such as charts, graphs, and forecast results.
• README.md: Project documentation.

Getting Started

Prerequisites

To run the notebooks and scripts, you need the following Python packages:

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn
• statsmodels

You can install these packages using pip:

pip install pandas numpy matplotlib seaborn scikit-learn statsmodels
....
To be defined later!