The Simpsons and Machine Learning: What makes an Episode Great?

Last update: Nov 02, 2021

Overview

The Simpsons and Machine Learning: What makes an Episode Great?

Check out my Medium article on this!

PROBLEM:

The Simpsons has had a decline in quality over the years
A quantitative solution is required to discover what makes a quality epsiode, and how these kinds of episodes can be replicated

SOLUTION:

Utilized sklearn, statsmodels, matplotlib, seaborn, pandas, and numpy packages
Built a linear regression model to predict IMDB rating based off word count per character and location

ATTACHED FILES:

simpsons_characters.csv
simpsons_episodes.csv
simpsons_locations.csv

Owner

Quantitative Insight

GitHub Repository

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The Simpsons and Machine Learning: What makes an Episode Great?

Related tags

Overview

The Simpsons and Machine Learning: What makes an Episode Great?

PROBLEM:

SOLUTION:

ATTACHED FILES:

Owner

Distributed deep learning on Hadoop and Spark clusters.

Machine Learning e Data Science com Python

Learn how to responsibly deliver value with ML.

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MachineLearningStocks is designed to be an intuitive and highly extensible template project applying machine learning to making stock predictions.

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

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Mesh TensorFlow: Model Parallelism Made Easier

Evidently helps analyze machine learning models during validation or production monitoring

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