https://github.com/tanishq-ctrl/walmart-analysis
This repository contains a Jupyter Notebook (Walmart Analysis.ipynb) that analyzes Walmart's sales data, focusing on identifying trends, patterns, and actionable insights. The analysis aims to understand sales performance and improve business strategies.
https://github.com/tanishq-ctrl/walmart-analysis
eda exploratory-data-analysis jupyter-notebook matplotlib numpy pandas python seaborn walmart
Last synced: 11 months ago
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This repository contains a Jupyter Notebook (Walmart Analysis.ipynb) that analyzes Walmart's sales data, focusing on identifying trends, patterns, and actionable insights. The analysis aims to understand sales performance and improve business strategies.
- Host: GitHub
- URL: https://github.com/tanishq-ctrl/walmart-analysis
- Owner: tanishq-ctrl
- License: mit
- Created: 2024-12-13T11:44:40.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-13T15:33:30.000Z (about 1 year ago)
- Last Synced: 2025-01-27T06:35:07.279Z (about 1 year ago)
- Topics: eda, exploratory-data-analysis, jupyter-notebook, matplotlib, numpy, pandas, python, seaborn, walmart
- Language: Jupyter Notebook
- Homepage: https://github.com/tanishq-ctrl/Walmart-Analysis
- Size: 1.92 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Walmart Analysis
This repository contains a Jupyter Notebook (`Walmart Analysis.ipynb`) that analyzes Walmart's sales data, focusing on identifying trends, patterns, and actionable insights.
The analysis aims to understand sales performance and improve business strategies.
## Features
- **Data Analysis:**
- Exploratory Data Analysis (EDA) on Walmart's dataset to uncover trends and patterns.
- Sales performance analysis across different categories, regions, and timeframes.
- **Visualizations:**
- Interactive and static charts for better understanding of sales trends.
- Graphs to highlight key insights such as seasonality, best-performing stores, and categories.
- **Insights:**
- Recommendations based on data-driven insights.
- Identification of high-performing products and regions.
## Requirements
To run the analysis, ensure you have the following installed:
- Python 3.7+
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Seaborn
## Installation
1. Clone the repository:
```bash
git clone https://github.com/tanishq-ctrl/Walmart-Analysis.git
```
2. Navigate to the repository folder:
```bash
cd Walmart-Analysis
```
3. Install the required Python packages:
```bash
pip install -r requirements.txt
```
4. Launch the Jupyter Notebook:
```bash
jupyter notebook
```
5. Open `Walmart Analysis.ipynb` and run the cells sequentially.
## Dataset
The dataset used for this analysis is sourced from [Walmart's sales data]. Please ensure you have the dataset in the appropriate directory before running the notebook. If required, update the file path in the notebook.
## Usage
1. Load the dataset into the notebook.
2. Follow the EDA steps to understand data distribution and relationships.
3. Use visualizations to interpret trends and draw insights.
4. Utilize the insights to make data-driven business decisions.
## Output
The notebook generates:
- Graphical representations of sales data.
- Summaries and recommendations based on analysis.
- Insights that can guide Walmart's sales strategies.
## Contributions
Contributions are welcome! If you'd like to enhance the analysis or add new features, please:
1. Fork the repository.
2. Create a new branch for your changes.
3. Submit a pull request with a detailed description of your changes.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## Contact
For questions or feedback, please contact:
- **Author:** Tanishq Prabhu
- **Email:** [tanishqprabhu20@gmail.com]
---
Thank you for exploring Walmart Analysis! We hope this analysis provides valuable insights to improve business strategies.