About the dataset

The dataset contains 33 attributes. The target variable refers to the salary of an Engineering Graduate in India.

To load the training data in your jupyter notebook, use the below command:

import pandas as pd

eng_grad_data = pd.read_csv("https://raw.githubusercontent.com/dphi-official/Datasets/master/eng_grad_emp_salary/training_set_label.csv" )

Data Description

ID: A unique ID to identify a candidate
Salary: Annual CTC offered to the candidate (in INR)
Gender: Candidate's gender
DOB: Date of birth of the candidate
10percentage: Overall marks obtained in grade 10 examinations
10board: The school board whose curriculum the candidate followed in grade 10
12graduation: Year of graduation - senior year high school
12percentage: Overall marks obtained in grade 12 examinations
12board: The school board whose curriculum the candidate followed
CollegeID: Unique ID identifying the university/college which the candidate attended for her/his undergraduate
CollegeTier: Each college has been annotated as 1 or 2. The annotations have been computed from the average AMCAT scores obtained by the students in the college/university. Colleges with an average score above a threshold are tagged as 1 and others as 2.
Degree: Degree obtained/pursued by the candidate
Specialization: Specialization pursued by the candidate
CollegeGPA: Aggregate GPA at graduation
CollegeCityID: A unique ID to identify the city in which the college is located in.
CollegeCityTier: The tier of the city in which the college is located in. This is annotated based on the population of the cities.
CollegeState: Name of the state in which the college is located
GraduationYear: Year of graduation (Bachelor's degree)
English: Scores in AMCAT English section
Logical: Score in AMCAT Logical ability section
Quant: Score in AMCAT's Quantitative ability section
Domain: Scores in AMCAT's domain module
ComputerProgramming: Score in AMCAT's Computer programming section
ElectronicsAndSemicon: Score in AMCAT's Electronics & Semiconductor Engineering section
ComputerScience: Score in AMCAT's Computer Science section
MechanicalEngg: Score in AMCAT's Mechanical Engineering section
ElectricalEngg: Score in AMCAT's Electrical Engineering section
TelecomEngg: Score in AMCAT's Telecommunication Engineering section
CivilEngg: Score in AMCAT's Civil Engineering section
conscientiousness: Scores in one of the sections of AMCAT's personality test
agreeableness: Scores in one of the sections of AMCAT's personality test
extraversion: Scores in one of the sections of AMCAT's personality test
nueroticism: Scores in one of the sections of AMCAT's personality test
openess_to_experience: Scores in one of the sections of AMCAT's personality test

Note: To give you more context AMCAT is a job portal.

Test Dataset

Load the test data (name it as test_data). You can load the data using the below command.

test_data = pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/eng_grad_emp_salary/testing_set_label.csv')

Here the target column is deliberately not there as you need to predict it.

Acknowledgement

We would like to thank ‘Aspiring Minds Research’ for making this dataset available publicly.

Data Sprint #2 - Engineering Graduates Employment Outcomes

Challenge Starts

Registration Ends

Challenge Ends