https://github.com/chdl17/marvelvsdc
This GitHub repository contains Python and Tableau for analyzing Marvel Vs DC superheros.
https://github.com/chdl17/marvelvsdc
excel jupyter-notebook numpy pandas python tableau-public
Last synced: 4 months ago
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This GitHub repository contains Python and Tableau for analyzing Marvel Vs DC superheros.
- Host: GitHub
- URL: https://github.com/chdl17/marvelvsdc
- Owner: chdl17
- Created: 2023-06-21T15:30:37.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-06-21T15:44:33.000Z (over 2 years ago)
- Last Synced: 2025-10-09T02:09:25.610Z (4 months ago)
- Topics: excel, jupyter-notebook, numpy, pandas, python, tableau-public
- Homepage: https://public.tableau.com/app/profile/maruthi.sai.phani.teja.chadalapaka/viz/SuperHeros_16869435284630/Dashboard1
- Size: 768 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# MarvelVsDC
## About:
This repository contains a Tableau dashboard comparing the characteristics and attributes of Marvel and DC superheroes. The dashboard is built using the 'heros' dataset and offers an interactive visualization of various hero statistics, including strength, speed, intelligence, and more. Explore the data, compare heroes from both universes, and gain insights into the similarities and differences between Marvel and DC characters.
Feel free to provide additional details about the dataset, any specific features or functionalities of the dashboard, or any unique insights it provides. The goal is to give potential users a clear understanding of what the repository contains and what they can expect from the dashboard.
## Approach:
Here's how I approached the missing data issue: 💪
**1️⃣ Data Exploration:** I began by examining the dataset to identify any missing values or incomplete records. Using Python's powerful libraries, such as Pandas, NumPy, and Matplotlib, I gained insights into the structure and content of the data.
**2️⃣ Missing Data Detection:** To identify missing data, I utilized Python's DataFrame functions to check for null values, NaNs, or placeholders. These indicators helped me pinpoint the areas where data was absent.
**3️⃣ Data Imputation:** To fill in the gaps, I employed various techniques. For numerical data, I used methods like mean, median, or regression to estimate missing values based on the available information. Categorical data, on the other hand, underwent imputation through techniques like mode or interpolation.
**4️⃣ Data Validation:** After imputing the missing values, it was crucial to validate the integrity and reliability of the data. I cross-referenced the imputed values with the original dataset, ensuring accuracy and consistency.
**5️⃣ Tableau Dashboard Creation:** With the missing data handled, I dove into the exciting realm of Tableau. Leveraging its user-friendly interface and powerful visualization capabilities, I crafted an engaging and visually appealing Marvel vs. D.C. Stats dashboard. It encapsulates the essence of the superhero rivalry, presenting an array of captivating charts, graphs, and visualizations.
**Result:** [Link to the Dashboard](https://public.tableau.com/app/profile/maruthi.sai.phani.teja.chadalapaka/viz/SuperHeros_16869435284630/Dashboard1)