Data-Visualization-App

Visualization Project for Data Analysis

How to have access to the Dashboard

You can now view your Streamlit app in your browser.

Render Address: https://data-visualization-app-we3i.onrender.com

Network URL: http://10.214.238.184:8501

External URL: http://44.226.122.3:8501

Project Description

This Visualization Project is a Data Analysis Dashboard meant to help the user visualize the input data as clearly as possible. It aims to uncover patterns, anomalies, correlations, and trends in the data, thereby providing valuable insights. While the core of the project revolves around data visualization and analysis, it also incorporates elements of simulation to model potential market behaviors and predict outcomes under different scenarios. This simulation aspect is designed to provide a dynamic understanding of how random events or changes in market conditions might affect vehicle pricing, demand, and supply dynamics.

Methods and Libraries Used

The project leverages Python, a powerful programming language known for its simplicity and robust ecosystem for data analysis and visualization. Key Python libraries utilized in this project include:

Pandas:

For data manipulation and cleaning, allowing for efficient handling of the dataset.

plotly.express:

For creating a wide range of static, interactive, and animated visualizations to explore the dataset from various angles.

streamlit:

Supports the development of simple web apps.

altair:

Specifically designed for creating statistical visualizations. These tools together provide a comprehensive framework for conducting exploratory data analysis, enabling the project to extract meaningful insights from complex datasets.

Launching the Project Locally

1. Ensure you have Python installed: The project requires Python 3.x. You can download it from python.org.

2. Clone or download the project repository: If the project is hosted on a version control system like GitHub, clone it to your local machine. If provided as a downloadable archive, extract it to a local directory.

3. Install required libraries: Navigate to the project directory in your terminal or command prompt and install the necessary libraries by running pip install -r requirements.txt. This command assumes a requirements.txt file exists with all the needed packages listed.

4. Launch Jupyter Notebook: Install Jupyter Notebook via pip if you haven't already (pip install notebook). Then, run jupyter notebook in your terminal within the project directory. This command will open Jupyter in your web browser.

5. Open the EDA Notebook: In the Jupyter interface, navigate to the EDA.ipynb file and open it to view the analysis and simulations.

By following these steps, anyone interested can set up the environment to run and explore the EDA and simulation tools provided in this project.