Anime Analytics

This project involves the collection, preprocessing, exploration, and analysis of anime data using various machine learning techniques. The goal is to predict the mean ratings of anime shows and classify their success based on various features.

Project Overview

• Data Collection: The dataset was collected from the MyAnimeList (MAL) API to gather data on anime shows.
• Data Preprocessing: The raw data was cleaned, transformed, and prepared for analysis using standard data preprocessing techniques.
• Exploratory Data Analysis (EDA): A deep dive into the data to understand distributions, relationships, and important features.
• Regression Analysis: A model was developed to predict the mean ratings of anime shows based on features such as genres, studios, and seasonality.
• Classification: A classification model was built to predict the success of an anime based on its features, where success is defined by achieving a certain rating threshold.

Technologies Used

• Programming Language: Python
• Libraries: pandas, numpy, matplotlib, seaborn, requests, sklearn