https://github.com/codehub001/python_diwali_sales_analysis
Diwali Sales Analysis is a data analysis project that explores sales trends during the Diwali festival. Using Python and Pandas, this project cleans, processes, and visualizes sales data to extract meaningful insights.
https://github.com/codehub001/python_diwali_sales_analysis
jupyter pandas-dataframe python
Last synced: 3 months ago
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Diwali Sales Analysis is a data analysis project that explores sales trends during the Diwali festival. Using Python and Pandas, this project cleans, processes, and visualizes sales data to extract meaningful insights.
- Host: GitHub
- URL: https://github.com/codehub001/python_diwali_sales_analysis
- Owner: codehub001
- License: mit
- Created: 2024-09-01T11:37:50.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-02-22T05:04:49.000Z (3 months ago)
- Last Synced: 2025-02-22T06:18:43.584Z (3 months ago)
- Topics: jupyter, pandas-dataframe, python
- Language: Jupyter Notebook
- Homepage:
- Size: 550 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Diwali Sales Analysis
## 📌 Project Overview
Diwali Sales Analysis is a data analysis project that explores sales trends during the Diwali festival. Using Python and Pandas, this project cleans, processes, and visualizes sales data to extract meaningful insights.
## 🗂 Dataset
The dataset contains information on:
- Customer demographics (Gender, Age Group, State)
- Product categories
- Purchase amounts
- Payment methods
## 🛠 Technologies Used
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Jupyter Notebook
## 📊 Analysis Performed
- Data Cleaning and Preprocessing
- Exploratory Data Analysis (EDA)
- Visualization of sales trends
- Insights into customer purchasing behavior
## 🚀 How to Run the Project
1. Clone the repository:
```sh
git clone https://github.com/your-username/diwali-sales-analysis.git
```
2. Navigate to the project directory:
```sh
cd diwali-sales-analysis
```
3. Install dependencies:
```sh
pip install -r requirements.txt
```
4. Open the Jupyter Notebook:
```sh
jupyter notebook
```
5. Run the analysis notebook to see the insights.
## 📈 Key Findings
- Highest sales occurred in metropolitan cities.
- Male customers contributed more to total sales.
- Electronics and clothing were the most purchased categories.
## 📌 Future Enhancements
- Implement predictive modeling for sales forecasting.
- Integrate interactive dashboards using Power BI or Tableau.
## 🤝 Contributing
Feel free to contribute by opening issues or submitting pull requests.
## 📜 License
This project is licensed under the MIT License.
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📩 For any queries, feel free to reach out!