Data Analyst Dashboard is an interactive tool built to help data analysts explore, analyze, and visualize datasets. Using Dash and Plotly, this project provides an intuitive interface for dynamic data exploration, focusing on e-commerce data analysis.
-
🔍 Interactive Data Exploration
Filter and visualize data dynamically using various chart types (e.g., bar charts, line charts, pie charts). -
⚙️ Data Filtering & Manipulation
Easily manipulate and filter datasets to focus on specific insights, with functionality for handling missing data and performing transformations. -
📊 Customizable Dashboards
Build custom dashboards by selecting relevant metrics and visualizing them in real-time. -
💼 E-Commerce Analysis
Analyze e-commerce sales data based on product categories and visualize sales trends.
The project starts with data preprocessing in a Jupyter Notebook, where we:
- Load datasets, inspect the data, and perform initial exploration.
- Clean the data by removing duplicates and handling missing values.
- Generate summary statistics and visualize trends in the data using Plotly.
The dashboard allows users to interact with the data:
- E-commerce Data: Users can select product categories from a dropdown menu to filter the dataset.
- Visualizations: The selected category is used to display a bar chart showing sales per product in that category.
- 🎯 Dropdown Menu: Allows users to select a category and filter data.
- 📈 Real-time Graphs: Displays dynamic visualizations based on user selection.
- 🐍 Python: Core programming language.
- ⚙️ Streamlit: Framework for building interactive web applications.
- 📊 Plotly: Library for creating interactive charts.
- 🔢 Pandas: Used for data manipulation and cleaning.
git clone https://github.com/Arfazrll/Data-Analyst-Dashboard.git
cd Data-Analyst-DashboardIf a requirements.txt file is provided:
pip install -r requirements.txtstreamlit run Dashboard/EcomersDashboard.py.pyOpen your browser and navigate to http://127.0.0.1:8050 to access the interactive dashboard.
The dashboard uses example e-commerce datasets, including product categories and sales data, to provide insights on sales performance and trends.
This project is designed to be flexible and easy to extend. Whether you're working with e-commerce data or other datasets, it offers a powerful and interactive solution for data analysis and visualization. It's a great tool for data analysts looking to gain insights from their data in a dynamic and visual way.
Feel free to open an issue or submit a pull request if you have suggestions or improvements for this project!