Visualytics is a web application for data analysis and visualization.
- Dashboard: Summarization, Dataset Preview, Shape, Data Types, Null Values, Summary Statistics, JSON View.
- Analytics: Analysis of the Dataset, Goal-based visualization.
- Code: Query your Data to Generate Code.
- Graph: Query your Data to Generate Graphs.
- Custom: Select Visualization technique (Bar, Pie, Pairplot, Heatmap, Linechart, Treemap).
- Python 3.x
Python is a high-level programming language widely used for various purposes, including data analysis, web development, and automation. Your project requires Python 3.x, preferably the latest stable version available.
- Streamlit
Streamlit: Streamlit is an open-source Python library that allows you to create interactive web applications for data science and machine learning projects. It simplifies the process of building and sharing data applications by providing a user-friendly interface and seamless integration with Python libraries.
- pandas
pandas: pandas is a powerful data manipulation and analysis library for Python. It provides data structures like DataFrames and Series, along with a wide range of functions for data cleaning, transformation, and exploration. pandas is essential for handling and processing tabular data in your project.
- plotly
plotly: plotly is a graphing library for creating interactive, publication-quality plots and charts in Python. It offers various types of charts, including scatter plots, bar charts, line charts, and more. plotly is useful for visualizing data and presenting insights in an interactive and visually appealing manner.
- seaborn
-seaborn: seaborn is a statistical data visualization library built on top of matplotlib. It provides a high-level interface for creating attractive and informative statistical graphics. seaborn simplifies the process of generating complex visualizations such as heatmaps, pair plots, and categorical plots.
- openai
openai: OpenAI is an artificial intelligence research laboratory that develops and promotes AI technologies. In your project, you're using OpenAI's API for tasks such as text generation and natural language processing. Make sure to sign up for an API key from OpenAI and follow their guidelines for usage.
1. Clone the repository.
2. Install the required dependencies: `pip install -r requirements.txt`.
3. Run the application: `streamlit run app.py`.