Data
Campus-Recruitment

Campus-Recruitment

active ARFF CC0: Public Domain Visibility: public Uploaded 23-03-2022 by Dustin Carrion
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  • Computer Systems Machine Learning
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Hello My name is Ben Roshan D, doing MBA in Business Analytics at Jain University Bangalore . We have practical sessions in Python,R as subjects. Faculties provide us with such data sets to work on with it, So here is one of the data set which our class worked on What is in it This data set consists of Placement data of students in a XYZ campus. It includes secondary and higher secondary school percentage and specialization. It also includes degree specialization, type and Work experience and salary offers to the placed students Acknowledgement I would like to thank Dr. Dhimant Ganatara, Professor Jain University for helping the students by providing this data for us to train R programming Questions Which factor influenced a candidate in getting placed Does percentage matters for one to get placed Which degree specialization is much demanded by corporate Play with the data conducting all statistical tests.

15 features

sl_nonumeric215 unique values
0 missing
genderstring2 unique values
0 missing
ssc_pnumeric103 unique values
0 missing
ssc_bstring2 unique values
0 missing
hsc_pnumeric97 unique values
0 missing
hsc_bstring2 unique values
0 missing
hsc_sstring3 unique values
0 missing
degree_pnumeric89 unique values
0 missing
degree_tstring3 unique values
0 missing
workexstring2 unique values
0 missing
etest_pnumeric100 unique values
0 missing
specialisationstring2 unique values
0 missing
mba_pnumeric205 unique values
0 missing
statusstring2 unique values
0 missing
salarynumeric45 unique values
67 missing

19 properties

215
Number of instances (rows) of the dataset.
15
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
67
Number of missing values in the dataset.
67
Number of instances with at least one value missing.
7
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
31.16
Percentage of instances having missing values.
2.08
Percentage of missing values.
Average class difference between consecutive instances.
46.67
Percentage of numeric attributes.
0.07
Number of attributes divided by the number of instances.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

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