Data Visualization

Overview

This repository contains various data visualization projects and assignments, focusing on exploratory data analysis (EDA), statistical plots, interactive visualizations, and dashboard creation.

Course Details

• Course: Data Visualization
• Institution: Arizona State University (ASU)
• Topics Covered:
  • Exploratory Data Analysis (EDA)
  • Statistical Visualization
  • Geospatial Data Visualization
  • Interactive Dashboards
  • Time-series & Trend Analysis
  • Machine Learning Model Interpretability

Technologies Used

• Programming Languages: Python
• Libraries & Tools:
  • Matplotlib
  • Seaborn
  • Plotly
  • Bokeh
  • Dash
  • Tableau
  • Power BI

Repository Structure

data-visualization/
│── datasets/                # Sample datasets used for visualization
│── notebooks/               # Jupyter notebooks with visualization implementations
│── scripts/                 # Python scripts for generating visualizations
│── dashboards/              # Interactive dashboards using Dash/Tableau/Power BI
│── reports/                 # Analysis reports and presentations
│── README.md                # Documentation

Key Implementations

• Exploratory Data Analysis (EDA): Created statistical plots to analyze dataset distributions.
• Time-series Analysis: Visualized trends, seasonality, and forecasting using line charts and moving averages.
• Geospatial Visualization: Used Folium and Plotly to create maps for location-based data analysis.
• Interactive Dashboards: Developed dashboards in Dash, Tableau, and Power BI for dynamic data representation.
• Machine Learning Model Insights: Used SHAP, Feature Importance plots to explain ML models.

Setup Instructions

Prerequisites

• Python 3.x
• Required Libraries:

  pip install matplotlib seaborn plotly bokeh dash pandas numpy folium

Running the Code

1. Clone the repository:

   git clone https://github.com/AjayKannan97/data-visualization.git
   cd data-visualization

2. Open Jupyter Notebook:

   jupyter notebook

3. Navigate to the notebooks/ folder and run the visualization scripts.

Results & Observations

• Enhanced Data Understanding: Visualizations helped identify key patterns, anomalies, and trends.
• Interactivity: Dashboards provided an interactive way to explore data dynamically.
• Geospatial Insights: Effective mapping revealed regional variations in datasets.

Contributors

• Ajay Kannan
• [Add any collaborators if applicable]

License

This project is intended for educational purposes. If used, please give appropriate credit.

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For any questions, contact Ajay Kannan at ajaykannan@gmail.com.