OtakuAnalytics: Filtering the Masterpieces
Comprehensive exploratory data analysis on a dataset of over 12,000 anime entries. Applied statistical methods to identify outliers and surface critically acclaimed titles. Built a feature engineering pipeline for filtering and ranking anime based on multiple quality metrics.
1. Overview
An anime data analysis Python project focusing on exploratory data analysis (EDA) of over 12,000 anime entries. This Pandas anime analysis pipeline filters statistical outliers to surface truly acclaimed titles.
2. Problem Statement
With thousands of anime released, navigating massive anime databases to surface statistically significant and critically acclaimed titles hidden in the noise is challenging.
3. Dataset
A comprehensive dataset containing over 12,000 anime entries with multiple quality metrics, genres, community ratings, and episode counts.
4. Methodology & Data Preprocessing
Applied Python data analysis techniques to filter statistical outliers. Built a robust feature engineering pipeline for filtering and ranking anime based on aggregated metrics.
5. Analysis & Model
Executed anime EDA using Pandas to examine community ratings versus popularity, identifying true masterpieces versus artificially hyped shows.
6. Results & Key Findings
Successfully surfaced critically acclaimed titles and visualized complex anime statistics through a data visualization project pipeline using Matplotlib.
8. What I Learned
Mastered advanced Pandas data analysis techniques for handling missing data, applying statistical filtering, and generating compelling data visualizations.