
# Dataset Card for IMDb Movie Dataset: All Movies by Genre

## Dataset Description

- **Homepage:** https://www.kaggle.com/datasets/rajugc/imdb-movies-dataset-based-on-genre?select=history.csv
- **Repository:** 
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary

This dataset is an adapted version of **"IMDb Movie Dataset: All Movies by Genre"** found at: https://www.kaggle.com/datasets/rajugc/imdb-movies-dataset-based-on-genre?select=history.csv.
Within the dataset, the movie title and year columns were combined, the genre was extracted from the seperate csv files, the pre-existing genre column was renamed to expanded-genres, any movies missing a description (i.e. the description was written as "Add a plot / Plot unkown" or similar) were dropped from the original data, the rating column was left the same, and finally the rest of the remaining columns were dropped.
The columns in the data are: "movie title - year", "genre", "expanded-genres", "rating", and "description"

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

Language data is English.

## Dataset Structure

### Data Instances

**training** = 238256 entries
  - Total amount of disk used: 54.3 MB

**test** = 29756 entries
  - Total amount of disk used: 6.77 MB

**validation** = 29809 entries
  - Total amount of disk used: 6.78 MB
    
An example of one entry looks as follows:
```
{
  "movie title - year" : "Die Hard - 1988",
  "genre" : "Action",
  "expanded-genres" : "Action, Thriller",
  "rating" : 8.2,
  "description" : "A New York City police officer tries to save his estranged wife and several others taken hostage by terrorists during a Christmas party at the Nakatomi Plaza in Los Angeles."
}
```
### Data Fields

The data fields are the same among all splits.

Fields are as follows: 
  - "movie title - year": a string feature.
  - "genre": a string classification label, with the possible values: 'Adventure', 'Action', 'Thriller', 'Romance', 'Crime', 'Fantasy', 'Mystery', 'Horror', 'War', 'Family', 'Animation', 'Scifi', 'Sports', 'History', 'Biography', and 'Film-noir'.
  - "expanded-genres" : a string feature.
  - "rating" : a floating point value, ranging from 0.0 to 10.0.
  - "description" : a string feature.


### Source Data

**"IMDb Movie Dataset: All Movies by Genre"** found at: https://www.kaggle.com/datasets/rajugc/imdb-movies-dataset-based-on-genre?select=history.csv

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

[More Information Needed]

### Contributions

[More Information Needed]
language:en license:cc-by-nc-sa-4.0 region:us
{"language": ["en"], "license": "cc-by-nc-sa-4.0"}
TAGS
#language-English #license-cc-by-nc-sa-4.0 #region-us 


# Dataset Card for IMDb Movie Dataset: All Movies by Genre

## Dataset Description

- Homepage: URL
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: 

### Dataset Summary

This dataset is an adapted version of "IMDb Movie Dataset: All Movies by Genre" found at: URL
Within the dataset, the movie title and year columns were combined, the genre was extracted from the seperate csv files, the pre-existing genre column was renamed to expanded-genres, any movies missing a description (i.e. the description was written as "Add a plot / Plot unkown" or similar) were dropped from the original data, the rating column was left the same, and finally the rest of the remaining columns were dropped.
The columns in the data are: "movie title - year", "genre", "expanded-genres", "rating", and "description"

### Supported Tasks and Leaderboards



### Languages

Language data is English.

## Dataset Structure

### Data Instances

training = 238256 entries
  - Total amount of disk used: 54.3 MB

test = 29756 entries
  - Total amount of disk used: 6.77 MB

validation = 29809 entries
  - Total amount of disk used: 6.78 MB
    
An example of one entry looks as follows:

### Data Fields

The data fields are the same among all splits.

Fields are as follows: 
  - "movie title - year": a string feature.
  - "genre": a string classification label, with the possible values: 'Adventure', 'Action', 'Thriller', 'Romance', 'Crime', 'Fantasy', 'Mystery', 'Horror', 'War', 'Family', 'Animation', 'Scifi', 'Sports', 'History', 'Biography', and 'Film-noir'.
  - "expanded-genres" : a string feature.
  - "rating" : a floating point value, ranging from 0.0 to 10.0.
  - "description" : a string feature.


### Source Data

"IMDb Movie Dataset: All Movies by Genre" found at: URL

## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases



### Other Known Limitations



## Additional Information

### Dataset Curators



### Licensing Information







### Contributions


# Dataset Card for IMDb Movie Dataset: All Movies by Genre ## Dataset Description

- Homepage: URL
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: ### Dataset Summary

This dataset is an adapted version of "IMDb Movie Dataset: All Movies by Genre" found at: URL
Within the dataset, the movie title and year columns were combined, the genre was extracted from the seperate csv files, the pre-existing genre column was renamed to expanded-genres, any movies missing a description (i.e. the description was written as "Add a plot / Plot unkown" or similar) were dropped from the original data, the rating column was left the same, and finally the rest of the remaining columns were dropped.
The columns in the data are: "movie title - year", "genre", "expanded-genres", "rating", and "description" ### Supported Tasks and Leaderboards ### Languages

Language data is English. ## Dataset Structure ### Data Instances

training = 238256 entries
  - Total amount of disk used: 54.3 MB

test = 29756 entries
  - Total amount of disk used: 6.77 MB

validation = 29809 entries
  - Total amount of disk used: 6.78 MB
    
An example of one entry looks as follows: ### Data Fields

The data fields are the same among all splits.

Fields are as follows: 
  - "movie title - year": a string feature.
  - "genre": a string classification label, with the possible values: 'Adventure', 'Action', 'Thriller', 'Romance', 'Crime', 'Fantasy', 'Mystery', 'Horror', 'War', 'Family', 'Animation', 'Scifi', 'Sports', 'History', 'Biography', and 'Film-noir'.
  - "expanded-genres" : a string feature.
  - "rating" : a floating point value, ranging from 0.0 to 10.0.
  - "description" : a string feature. ### Source Data

"IMDb Movie Dataset: All Movies by Genre" found at: URL ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions
TAGS
#language-English #license-cc-by-nc-sa-4.0 #region-us 
 # Dataset Card for IMDb Movie Dataset: All Movies by Genre ## Dataset Description

- Homepage: URL
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: ### Dataset Summary

This dataset is an adapted version of "IMDb Movie Dataset: All Movies by Genre" found at: URL
Within the dataset, the movie title and year columns were combined, the genre was extracted from the seperate csv files, the pre-existing genre column was renamed to expanded-genres, any movies missing a description (i.e. the description was written as "Add a plot / Plot unkown" or similar) were dropped from the original data, the rating column was left the same, and finally the rest of the remaining columns were dropped.
The columns in the data are: "movie title - year", "genre", "expanded-genres", "rating", and "description" ### Supported Tasks and Leaderboards ### Languages

Language data is English. ## Dataset Structure ### Data Instances

training = 238256 entries
  - Total amount of disk used: 54.3 MB

test = 29756 entries
  - Total amount of disk used: 6.77 MB

validation = 29809 entries
  - Total amount of disk used: 6.78 MB
    
An example of one entry looks as follows: ### Data Fields

The data fields are the same among all splits.

Fields are as follows: 
  - "movie title - year": a string feature.
  - "genre": a string classification label, with the possible values: 'Adventure', 'Action', 'Thriller', 'Romance', 'Crime', 'Fantasy', 'Mystery', 'Horror', 'War', 'Family', 'Animation', 'Scifi', 'Sports', 'History', 'Biography', and 'Film-noir'.
  - "expanded-genres" : a string feature.
  - "rating" : a floating point value, ranging from 0.0 to 10.0.
  - "description" : a string feature. ### Source Data

"IMDb Movie Dataset: All Movies by Genre" found at: URL ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions
passage: TAGS
#language-English #license-cc-by-nc-sa-4.0 #region-us 
# Dataset Card for IMDb Movie Dataset: All Movies by Genre## Dataset Description

- Homepage: URL
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact:### Dataset Summary

This dataset is an adapted version of "IMDb Movie Dataset: All Movies by Genre" found at: URL
Within the dataset, the movie title and year columns were combined, the genre was extracted from the seperate csv files, the pre-existing genre column was renamed to expanded-genres, any movies missing a description (i.e. the description was written as "Add a plot / Plot unkown" or similar) were dropped from the original data, the rating column was left the same, and finally the rest of the remaining columns were dropped.
The columns in the data are: "movie title - year", "genre", "expanded-genres", "rating", and "description"### Supported Tasks and Leaderboards### Languages

Language data is English.## Dataset Structure### Data Instances

training = 238256 entries
  - Total amount of disk used: 54.3 MB

test = 29756 entries
  - Total amount of disk used: 6.77 MB

validation = 29809 entries
  - Total amount of disk used: 6.78 MB
    
An example of one entry looks as follows: