Automatic Data Visualization and Exploratory Data Analysis using Python's AutoViz library

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📑 Table of Contents

• Features
• Prerequisites
• Installation
• Usage
• Project Structure
• Data Description
• Documentation

✨ Features

• 📊 Generate comprehensive visualizations with minimal code.
• 📈 Perform automated EDA on your datasets.
• 📉 Identify patterns and trends in your data.
• 📑 Create detailed visualiation reports for easy sharing.

🔧 Prerequisites

• Python 3.11.0
• AutoViz library
• Web browser for viewing HTML reports

📥 Installation

1. Clone the repository:

git clone https://github.com/PreProd-Internship/DIY-Python-AutoViz

2. Create and activate a virtual environment (recommended). If using Conda:

conda create -n env_name python==3.11.0 -y
conda activate env_name

3. Install dependencies:

pip install -r requirements.txt

🚀 Usage

1. Start the Streamlit application:

streamlit run source/app.py

2. Access the web interface at http://localhost:8501

3. Provide your CSV file path, configure the parameters, and generate visualizations and EDA reports!

📁 Project Structure

DIY-Python-AutoViz/
    ├── data/
    │   └── urban_public_transporation.csv
    ├── plots/
    │   └── .gitignore
    ├── source/
    │   ├── app.py
    │   └── eda.py
    ├── .gitignore
    ├── LearnWithPrompts.md
    ├── README.md
    └── requirements.txt

🗃️ Data Description

urban_public_transporation.csv

A mock dataset generated using Mockaroo for learning purposes. The urban_public_transporation.csv dataset focuses on analyzing the performance of a city's public transportation system, identifying patterns in ridership, and optimizing routes and schedules to improve efficiency and user satisfaction. Each row represents a single bus trip with columns like

• trip_id,
• time_of_day,
• bus_id,
• weather,
• delay_minutes, and so on.

Note: delay_minutes is the dependent variable (dep_var) in this dataset. If you'd like, other fields like number_of_passengers or fuel_consumption could also be explored as alternative target variables for different perspectives.

Use Cases in AutoViz:

• 🚍 Visualizing trip performance based on time of day and traffic conditions.
• ⛽ Analyzing fuel efficiency in relation to traffic and weather.
• 🚌 Identifying underperforming routes based on passenger count and delays.
• 🚦 Comparing bus and driver performance to optimize staffing and assignments.
• ⏱️ Analyzing the correlation between delays and trip duration under different conditions.

This dataset and business case provide a rich, real-world scenario that highlights the capabilities of AutoViz.

📚 Documentation

For detailed information about the project, please refer to:

• AutoViz - AutoViz library's documentation
• LearnWithPrompts.md - Use your favourite LLM to learn more about AutoViz