adityab24840/eda-and-datavisualization

This app allows you to select a dataset, view its contents, explore data statistics, and generate various types of plots. It's built with Streamlit, a Python library for creating web applications with ease.

ML DataSet Explorer & Data Visualization with Streamlit

Link : https://edavisual.streamlit.app/

Table of Contents

• ML DataSet Explorer & Data Visualization with Streamlit
• Features
• Usage
• Author
• GitHub Repository
• Built With

Explore and visualize your datasets interactively with this Streamlit application. This app allows you to select a dataset, view its contents, explore data statistics, and generate various types of plots. It's built with Streamlit, a Python library for creating web applications with ease.

Features

• Dataset Selection: Choose a dataset file from a directory.
• Data Exploration:
  • View the first few rows of the dataset.
  • Display the dataset's shape (number of rows and columns).
  • Select specific columns to display.
  • Check the value counts of the target/class variable.
  • Check for missing (null) values in the dataset.
  • View the data types of each column.
  • Explore summary statistics of the dataset.
• Data Visualization:
  • Generate a correlation heatmap (Seaborn).
  • Create a pie chart to visualize target variable distribution.
  • Generate bar plots based on selected columns.
  • Customize and generate various types of plots (area, bar, line, etc.).
• Fun Interaction: Click the "Thanks" button to display balloons.

Usage

1. Clone Repository: Clone this repository to your local machine or download the code.

   git clone https://github.com/adityab24840/EDA-and-DataVisualization

2. Install Dependencies: Install the required Python packages listed in the requirements.txt file:

   pip install -r requirements.txt

3. Run the App: Start the Streamlit app using the following command:

   streamlit run app.py

4. Interact with the App: The app will open in your default web browser, allowing you to explore and visualize your datasets interactively.

Author

• Author: Aditya N Bhatt
• Student ID: 231057017
• Program: AI & ML, MSIS

GitHub Repository

You can find the source code for this project on GitHub.

Built With

• Streamlit: The app is built with Streamlit, a Python library for creating web applications.