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

monolish: MONOlithic Liner equation Solvers for Highly-parallel architecture

Time series forecasting with PyTorch

Code base of KU AIRS: SPARK Autonomous Vehicle Team

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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

monolish: MONOlithic Liner equation Solvers for Highly-parallel architecture

Time series forecasting with PyTorch

Code base of KU AIRS: SPARK Autonomous Vehicle Team

mlpack: a scalable C++ machine learning library --

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices

TIANCHI Purchase Redemption Forecast Challenge

Practical Time-Series Analysis, published by Packt

Datetimes for Humans™

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A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

ETNA – time series forecasting framework

Toolss - Automatic installer of hacking tools (ONLY FOR TERMUKS!)

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

Decision tree is the most powerful and popular tool for classification and prediction

Convoys is a simple library that fits a few statistical model useful for modeling time-lagged conversions.

﻿Greykite: A flexible, intuitive and fast forecasting library

CorrProxies - Optimizing Machine Learning Inference Queries with Correlative Proxy Models

BASTA: The BAyesian STellar Algorithm

A repository to work on Machine Learning course. Select an algorithm to classify writer's gender, of Hebrew texts.

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Greykite: A flexible, intuitive and fast forecasting library