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https://github.com/tushar2704/rossman-sales-forecasting
Building a sales forecasting model that predicts future sales for each Rossmann store. By leveraging historical sales data, store-specific information, and external factors
https://github.com/tushar2704/rossman-sales-forecasting
algorithms artificial-intelligence data-science forecasting python regression sales-analysis time-series tushar2704
Last synced: 19 days ago
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Building a sales forecasting model that predicts future sales for each Rossmann store. By leveraging historical sales data, store-specific information, and external factors
- Host: GitHub
- URL: https://github.com/tushar2704/rossman-sales-forecasting
- Owner: tushar2704
- License: apache-2.0
- Created: 2023-08-12T06:36:57.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-08-12T16:21:56.000Z (over 1 year ago)
- Last Synced: 2024-05-11T05:53:46.969Z (8 months ago)
- Topics: algorithms, artificial-intelligence, data-science, forecasting, python, regression, sales-analysis, time-series, tushar2704
- Language: Jupyter Notebook
- Homepage: https://tushar-aggarwal.com
- Size: 14.5 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Rossman Sales Forecasting
The objective of this project is to build a sales forecasting model that predicts future sales for each Rossmann store. By leveraging historical sales data, store-specific information, and external factors like holidays, the model aims to provide reliable sales predictions for upcoming periods. This will help Rossmann allocate resources efficiently, optimize store operations, and maintain customer satisfaction by ensuring the availability of products.
## Project Overview
Rossmann is a well-known drugstore chain with thousands of stores across different regions. The company aims to improve its sales and inventory management by accurately forecasting future sales. Accurate sales forecasting enables the company to optimize inventory, plan promotions effectively, and make informed business decisions.
## Dataset Information:
The dataset for this project consists of two main CSV files:
- 1. data.csv: This file contains historical sales data for various Rossmann stores. It includes information such as the store ID, date, sales, customers, whether the store was open or closed, whether a promotion was running, and various other store-related attributes.
- 2. store.csv: This file provides additional information about each Rossmann store. It includes details like the store ID, store type (a, b, c, d), assortment type (a, b, c), competition distance, whether there is a competitor nearby, whether the store is under promotion (Promo2), and related details.
## Project Scope
- Rossmann aims to gain a competitive edge by enhancing sales forecasting accuracy. Accurate predictions help Rossmann to optimize inventory levels, ensure sufficient stock availability during high-demand periods, and reduce excess inventory costs. Furthermore, the accurate forecast enables the company to plan promotions strategically, resulting in increased footfall and higher sales.
- To achieve this, the project will explore the historical sales data of various Rossmann stores, identify patterns, trends, and seasonality, and leverage this information to build a reliable sales forecasting model. Additionally, external factors such as holidays and competitor activity will be considered to account for their impact on sales.
- By implementing an accurate sales forecasting model, Rossmann can make data-driven decisions, improve resource allocation, and optimize store performance to achieve higher profitability and customer satisfaction.
## Getting Started
Follow these steps to get started with the project:
1. **Clone the Repository:** Clone this repository to your local machine using the following command:
```
git clone https://github.com/tushar2704/Rossman-Sales-Forecasting
```
2. **Install Dependencies:** Install the required dependencies by running:
```
pip install -r requirements.txt
```
3. **Fetching Data:** Use the provided Jupyter notebook to fetch historical Netflix stock price data using the `yfinance` library.
4. **Data Analysis:** Analyze the stock price data using various techniques like plotting time series graphs, calculating moving averages, and identifying trends.
5. **Visualization:** Create visualizations to showcase stock price trends, volume changes, and any interesting patterns that emerge from the data.
6. **Insights:** Interpret the visualized data to derive insights into historical stock price behavior and potential investment opportunities.
## Project Structure
The project repository is organized as follows:
```
├── LICENSE
├── README.md <- README .
├── notebooks <- Folder containing the final reports/results of this project.
│ │
│ └── rossman_sales_forecasting.py <- Final notebook for the project.
├── reports <- Folder containing the final reports/results of this project.
│ │
│ └── Report.pdf <- Final analysis report in PDF.
│
├── src <- Source for this project.
│ │
│ └── data <- Datasets used and collected for this project.
| └── model <- Model.
```
## License
This project is licensed under the [MIT License](LICENSE).
## Author
- ©2023 Tushar Aggarwal. All rights reserved
- [LinkedIn](https://www.linkedin.com/in/tusharaggarwalinseec/)
- [Medium](https://medium.com/@tushar_aggarwal)
- [Tushar-Aggarwal.com](https://www.tushar-aggarwal.com/)
- [New Kaggle](https://www.kaggle.com/tagg27)
## Contact me!
If you have any questions, suggestions, or just want to say hello, you can reach out to us at [Tushar Aggarwal](mailto:[email protected]). We would love to hear from you!