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Gender-Classification-Dataset

Gender-Classification-Dataset

active ARFF CC0: Public Domain Visibility: public Uploaded 23-03-2022 by Lowe
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Context While I was practicing machine learning, I wanted to create a simple dataset that is closely aligned to the real world scenario and gives better results to whet my appetite on this domain. If you are a beginner who wants to try solving classification problems in machine learning and if you prefer achieving better results, try using this dataset in your projects which will be a great place to start. Content This dataset contains 7 features and a label column. longhair - This column contains 0's and 1's where 1 is "long hair" and 0 is "not long hair". foreheadwidthcm - This column is in CM's. This is the width of the forehead. foreheadheightcm - This is the height of the forehead and it's in Cm's. nosewide - This column contains 0's and 1's where 1 is "wide nose" and 0 is "not wide nose". noselong - This column contains 0's and 1's where 1 is "Long nose" and 0 is "not long nose". lipsthin - This column contains 0's and 1's where 1 represents the "thin lips" while 0 is "Not thin lips". distancenosetoliplong - This column contains 0's and 1's where 1 represents the "long distance between nose and lips" while 0 is "short distance between nose and lips". gender - This is either "Male" or "Female". Acknowledgements Nothing to acknowledge as this is just a made up data. Inspiration It's painful to see bad results at the beginning. Don't begin with complicated datasets if you are a beginner. I'm sure that this dataset will encourage you to proceed further in the domain. Good luck.

8 features

long_hairnumeric2 unique values
0 missing
forehead_width_cmnumeric42 unique values
0 missing
forehead_height_cmnumeric21 unique values
0 missing
nose_widenumeric2 unique values
0 missing
nose_longnumeric2 unique values
0 missing
lips_thinnumeric2 unique values
0 missing
distance_nose_to_lip_longnumeric2 unique values
0 missing
genderstring2 unique values
0 missing

19 properties

5001
Number of instances (rows) of the dataset.
8
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
7
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of nominal attributes.
Average class difference between consecutive instances.
87.5
Percentage of numeric attributes.
0
Percentage of missing values.
0
Percentage of instances having missing values.
0
Percentage of binary attributes.
0
Number of binary attributes.
Number of instances belonging to the least frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the most frequent class.
0
Number of attributes divided by the number of instances.

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