Exploratory Data Analysis App

The Exploratory Data Analysis (EDA) App is a Streamlit-based web application that allows users to perform comprehensive exploratory data analysis on their datasets. It provides an intuitive and user-friendly interface for uploading CSV files, visualizing the input data, and generating an interactive profiling report.

About

This project simplifies the process of exploratory data analysis. Leveraging the power of Streamlit for interactive web interfaces and ydata_profiling for generating detailed profiling reports, the app enables quick insights into your data—all in one place.

Overview

EDA-App-Demo.mp4

Features

• CSV File Upload: Easily upload your dataset in CSV format.
• Data Preview: View your dataset in a neat table format.
• Interactive Profiling Report: Automatically generate a comprehensive EDA report with insights and visualizations.
• Example Dataset: If no CSV is uploaded, the app can generate and analyze a random example dataset.

Installation

To set up the project locally, follow these steps:

1. Clone the Repository

   git clone https://github.com/barkiayoub/Exploratory-Data-Analysis-Streamlit-Platform.git
   cd Exploratory-Data-Analysis-Streamlit-Platform

2. Install the Required Packages

   pip install -r requirements.txt

Usage

To run the EDA App:

1. Start the App with Streamlit

   streamlit run app.py

2. Interact with the Application

   • Use the sidebar to upload your CSV file.
   • The main page will display a preview of the dataset.
   • An interactive profiling report will be generated below the data preview.
   • If no file is uploaded, click on "Press to use Example Dataset" in the sidebar to analyze a randomly generated dataset.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any questions or feedback, please reach out via:

• GitHub: barkiayoub
• LinkedIn: barkiayoub

Happy Analyzing!