Visuallite - A Data Visualization Tool

Visuallite is a web-based data visualization tool built with Streamlit. It allows users to upload CSV files, explore summary statistics, analyze correlations, detect outliers, and visualize data using various chart types.

Features

• Upload CSV File: Users can upload their own CSV files to analyze and visualize their data.
• Summary Statistics: Explore summary statistics including mean, median, standard deviation, minimum, maximum, etc.
• Correlation Analysis: Analyze correlations between different variables in the dataset.
• Outlier Detection: Detect outliers in the data using the interquartile range (IQR) method and visualize them on scatter plots.
• Interactive Chart Selection: Choose from various chart types including line plots, scatter plots, histograms, bar plots, box plots, and violin plots.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/visuallite.git

2. Navigate to the project directory:

   cd visuallite

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the Streamlit app:

   streamlit run main.py

Usage

1. Upload CSV file: Use the sidebar file uploader to upload your CSV file.
2. Explore Summary Statistics and Correlation Analysis: Select the respective checkboxes in the sidebar to explore summary statistics and analyze correlations.
3. Outlier Analysis: Enable the outlier analysis checkbox in the sidebar to detect outliers in the data and visualize them on scatter plots.
4. Visualize Data: Choose from various chart types available in the sidebar to visualize your data interactively.