Technical_Report

Technical Report

Abstract
This technical report documents the methodology, tools, and processes employed in the development of an Exploratory Data Analysis (EDA) 
project using Plotly Dash. The project aims to visually explore and analyze the relationships between various data points
through a series of data cleaning steps and interactive visualizations.
Introduction
The EDA project was conceptualized as a part of the final semester project requirements.
It focuses on deploying a web-based application to perform and present data analysis. 
The core objective was to clean, analyze, and visualize data to uncover patterns, correlations, and insights.
Methodology
	•	Data Cleaning: The data was processed through four major cleaning steps to prepare it for analysis.
   This included handling missing values, data type conversions, outlier detection, and normalization.
	•	Visualization: Utilizing Plotly Dash, over 20 different types of visualizations were created to represent 
    the data's correlations, frequencies, and relationships. Each visualization was designed with a clear title 
    and legible labels to ensure comprehensibility.
	•	Queries: Beyond visualizations, specific SQL-like queries were implemented to further dissect the data
    and extract actionable insights without the need for additional charts.
	•	Deployment: The final dashboard was deployed using Google App Engine, making the insights accessible 
  through an external-facing website.
Results
The project successfully demonstrates the application of EDA techniques in a web environment.
The interactive dashboard allows users to explore various aspects of the data through intuitive
visualizations and queries, providing a comprehensive understanding of the analyzed dataset.
Conclusion
The Exploratory Data Analysis project illustrates the power of visual data exploration 
in uncovering hidden patterns and insights. Through a systematic approach to data cleaning and visualization,
the project offers a template for future analyses aiming to make data more accessible and understandable.
Future Work
Further development could include the integration of machine learning models to predict 
trends and the addition of more complex data sources for a richer analysis experience.