Jewelry Store Data Analysis

Overview

This repository contains data analysis and visualization tools tailored for jewelry stores. By leveraging various data sources and analytical techniques, the tools offer insights into store operations, product performance, consumer profiles, and more.

Contents

1. Jewelry Store Analysis Notebook - A Jupyter Notebook providing in-depth analysis and visualizations. Link to the notebook
2. Streamlit Dashboard - A series of Streamlit pages providing interactive visualizations and insights.
   • Home - The main landing page of the dashboard.
   • Store Analysis - Insights into store operations and performance.
   • Product Analysis - Analysis of product performance and trends.
   • Profile Consumer - Insights into consumer behaviors and preferences.
   • Analysis of External Factors - Understanding external factors affecting the store.
   • Social Media - Analysis of the store's social media presence and performance.

Setup and Installation

Prerequisites:

• Python 3.9

Steps to setup:

1. Clone the repository:

   git clone <repository_url>
   cd <repository_name>
   

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows use: venv\Scripts�ctivate
   

3. Install the required packages:

   pip install -r requirements.txt
   

Main Dependencies:

• altair==5.1.1
• streamlit==1.26.0
• pandas==2.1.0
• plotly==5.16.1
• matplotlib==3.7.2
• seaborn==0.12.2
• scikit-learn==1.3.0
• numpy==1.25.2

Contributing

I welcome contributions to enhance Jewelry App! Whether you're a developer, designer, or simply passionate about Jewelry, you can contribute in several ways:

• Open Issues: If you encounter bugs or have ideas for improvements, please open an issue to discuss them.
• Suggest Enhancements: Feel free to suggest new features or enhancements that would make this app even more valuable.
• Pull Requests: If you'd like to contribute code, please submit a pull request. I'll review it and work together to incorporate your changes.

Future Improvements

• Integration with Other Data Sources: Enhance the analysis by incorporating additional data sources.
• Advanced Customization: Allow users to customize charts and visualizations to their liking.
• Forecasting Model: Develop a forecasting model for gold and silver prices to adjust product pricing.
• Real-time Alerts: Implement alerts to notify administrators about significant drops in sales or underperforming products.
• Sales by Seller: Propose training based on evaluating sales by each seller.
• User Feedback: Add a dedicated section for users to provide feedback on the application.
• Authentication and Security: Introduce an authentication layer to ensure only authorized users can access the dashboard.