Sales Analytics Dashboard

📊 Project Overview

This Sales Analytics Dashboard is a comprehensive data analysis and visualization project designed to provide deep insights into e-commerce sales data. The project demonstrates advanced skills in data science, business intelligence, and interactive dashboard creation.

🚀 Skills Demonstrated

• Data Processing: Pandas data manipulation
• Statistical Analysis: Advanced statistical techniques
• Data Visualization: Interactive dashboards with Streamlit and Plotly
• Machine Learning: Customer segmentation and predictive analytics
• Business Intelligence: Comprehensive sales insights

🔧 Technical Components

Data Sources

• E-commerce sales dataset
• Comprehensive sample retail sales data

Technologies Used

• Python 3.8+
• Pandas
• NumPy
• Streamlit
• Plotly
• Scikit-learn
• Seaborn
• Matplotlib

📁 Project Structure

sales-analytics-dashboard/
│
├── data/
│   ├── sample_ecommerce_data.csv
│   └── cleaned_sales_data.csv
│
├── notebooks/
│   ├── data_cleaning.ipynb
│   └── data_analysis.ipynb
│
├── sql/
│   └── sales_analysis_queries.sql
│
├── dashboard/
│   ├── dashboard.py
│   └── requirements.txt
│
└── README.md

🛠 Installation and Setup

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)
• Virtual environment (recommended)

Installation Steps

1. Clone the repository

   git clone https://github.com/yourusername/sales-analytics-dashboard.git
   cd sales-analytics-dashboard

2. Create a virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install required dependencies

   pip install -r dashboard/requirements.txt

🚀 Running the Project

Data Preprocessing

jupyter notebook notebooks/data_cleaning.ipynb
jupyter notebook notebooks/data_analysis.ipynb

Launch Dashboard

streamlit run dashboard/dashboard.py

📈 Key Features

1. Data Cleaning

• Handling missing values
• Date formatting
• Numerical validation
• Outlier removal

2. Analysis Capabilities

• Monthly sales trends
• Product performance metrics
• Customer segmentation
• Geographic sales analysis
• Predictive analytics preparation

3. Dashboard Highlights

• Interactive date range selection
• Product category filtering
• Dynamic visualizations
• Geographical sales insights

🔍 Advanced Analyses

Customer Segmentation

• RFM (Recency, Frequency, Monetary) Analysis
• K-means Clustering
• Customer value identification

Statistical Techniques

• Descriptive statistics
• Correlation analysis
• Hypothesis testing (ANOVA)
• Predictive modeling preparation

📊 Visualization Techniques

• Time series analysis
• Comparative bar charts
• Correlation heatmaps
• Geographical sales choropleth
• Customer segment scatter plots

🔬 Potential Enhancements

1. Implement machine learning predictive models
2. Add more advanced customer segmentation
3. Develop real-time dashboard updates
4. Integrate more complex statistical analyses
5. Create additional visualization techniques

📝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a pull request

📄 License

Distributed under the MIT License. See LICENSE for more information.

📧 Contact

Your Name - Pranav970

🙏 Acknowledgements

• Pandas
• Streamlit
• Plotly
• Scikit-learn
• Jupyter Notebook