Pokémon Analysis

A comprehensive exploration of Pokémon data to uncover trends and insights.

Project Overview

This project focuses on analyzing Pokémon data to uncover trends in attributes such as type distributions, stats, and correlations. By leveraging Python libraries like Pandas, Matplotlib, and Seaborn, we visualized and derived key insights from the Pokémon universe. The analysis utilized cleaned data for precision.

Data Cleaning

The raw Pokémon dataset was cleaned to handle missing values, standardize data types, and address anomalies. Unused columns were dropped, and data integrity was ensured to facilitate accurate analysis.

Removed 386 entries with missing secondary types.
Standardized numerical data for consistent analysis.
Prepared cleaned dataset with 800 Pokémon entries.

Key Visualizations

Stunning charts and graphs were generated to uncover patterns:

Type distributions showing dominance of Water and Normal types.
Correlations between Attack, Defense, and Speed stats.
Comparative analysis of Legendary versus non-Legendary Pokémon.

Statistical Insights

Key insights derived from the data:

Average total stats of Legendary Pokémon are significantly higher than regular Pokémon (670 vs. 435).
Highest HP belongs to Chansey with 255 HP.
Most common primary type: Water (112 entries).

Technologies Used

Python Pandas Matplotlib Seaborn

Explore More

Dive into the full code and visualizations to see how insights were derived.

View on GitHub