https://github.com/ryancodingg/rotten-tomatoes-classification-model
The motivation behind this project is to build a machine learning model that can predict the Rotten Tomatoes rating of movies based on critic reviews. By utilizing two large datasets, namely rotten_tomatoes_critic_reviews.csv and rotten_tomatoes_movies.csv obtained from Kaggle, I aim to develop a classification algorithm that effectively predicted
https://github.com/ryancodingg/rotten-tomatoes-classification-model
data-science python rotten-tomatoes xgboost
Last synced: 11 months ago
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The motivation behind this project is to build a machine learning model that can predict the Rotten Tomatoes rating of movies based on critic reviews. By utilizing two large datasets, namely rotten_tomatoes_critic_reviews.csv and rotten_tomatoes_movies.csv obtained from Kaggle, I aim to develop a classification algorithm that effectively predicted
- Host: GitHub
- URL: https://github.com/ryancodingg/rotten-tomatoes-classification-model
- Owner: ryancodingg
- Created: 2023-06-20T01:03:02.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-07-04T03:23:39.000Z (over 2 years ago)
- Last Synced: 2025-03-22T18:18:13.641Z (11 months ago)
- Topics: data-science, python, rotten-tomatoes, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 15.5 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Motivation
The motivation behind this project is to build a machine learning model that can predict the Rotten Tomatoes rating of movies based on critic reviews. By utilizing two large datasets, namely rotten_tomatoes_critic_reviews.csv and rotten_tomatoes_movies.csv obtained from Kaggle, I aim to develop a classification algorithm that can effectively predict the rating of movies.
Rotten Tomatoes is a popular online platform that provides movie reviews and ratings. The ratings provided on Rotten Tomatoes are determined by a combination of critics' reviews and audience ratings. Critics' reviews play a significant role in influencing the overall rating assigned to a movie. Therefore, by analyzing critic reviews, we can gain insights into the sentiment and opinions expressed by critics, which can subsequently be used to predict the movie's Rotten Tomatoes rating.
# Dataset Description
I use 2 dataset:
* `rotten_tomatoes_critic_reviews.csv`
- `rotten_tomatoes_link`: each link will associate with an unique ID of a movie
- `critic_name`: name of critic
- `top_critic`: tomatometer-approved critic: True or False
- `publisher_name`: name of Publisher
- `review_type`: Fresh, Rotten or Certified Fresh
- `review_score`: score for the movie
- `review_date`: date of review
- `review_content`: content of the review
* `rotten_tomatoes_movies.csv`
- `rotten_tomatoes_link`: each link will associate with an unique ID of a movie
- `movie_title`: name of the movie
- `movie_info`: brief introducrion of the movie
- `critic_consensus`: rotten Tomatoes's comment
- `content_rating`: rating of content - PG, R, NR, PG-13,G
- `genres`: type of movie
- `directors`: name of directors
- `authors`: name of authors
- `actors`: list of actors
- `original_release_date`: date which first made available to public
- `streaming_release_date`: for only streaming providers
- `runtime`: length of movie
- `production_company`: name of production house
- `tomatometer_status`: Fresh, Rotten or Certified Fresh
- `tomatometer_rating`: percentage of positive reviews
- `tomatometer_count`: critic ratings counted for the calculation of the tomatomer status
- `audience_status`: label assigned Spill or Upright
- `audience_rating`: percentage of positive rating users
- `audience_count`: total rating audience
- `tomatometer_top_critics_count`: number of rating by top critics
- `tomatometer_fresh_critics_count`: number of critic ratings labeled "Fresh"
- `tomatometer_rotten_critics_count`: number of critic ratings labeled "Rotten"