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Predicting the hiring possibility for candidate

Predicting the hiring possibility for candidate

Price : 10000

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Course Duration
Approx 10

Course Price
₹ 10000

Course Level
High

Course Content

ABSTRACT

 

Placements are considered to be very important for each and every college. This project predicts the probability of a student getting placed in a company by applying classification algorithm such as K-nearest neighbor and logistic regression. The main objective of this model is to predict whether the student gets placed or not in campus recruitment. For this the data considered is the academic history of student like overall percentage, backlogs, and credits. The algorithms are applied on the previous year’s data of the students .A high placement rate is a key entity for any educational institution. Hence such a system has a significant place in educational system of any learning institutions.

 

1.1           INTRODUCTION

It is aimed to develop automation in placement prediction at college level which predicts the chances of undergraduate students getting placed in an IT company and helps in profiling a candidate before the recruitment process starts. It involves the use of machine learning model of k-nearest neighbor algorithm as base model to classify students or users into appropriate clusters and the result would help them in improving their skills and other mindset. The results of the same are also compared with the results obtained from other models like logistic regression, random forest and SVM for optimal solution. With various data mining and machine learning techniques, this proposition would help both students as well as recruiters during placements and other recruitment activities.

 

BLOCK DIAGRAM

BLOCK DIAGRAM

SYSTEM REQUIREMENTS

Hardware Requirement:-

      System: Pentium IV 2.4 GHz. 

      Hard Disk: 500 GB.

      Ram: 4 GB.

      Any desktop / Laptop system with above configuration or higher level .

 

Software Requirements:-

      Operating system : Windows XP / 7

      Coding Language :Python

      Interpreter    :Python 3.6

      IDE             : Jupyter notebook

      ML APIS           :Sklearn, numpy, pandas, matplotlib

Functional Requirements:-

      Predict the possibility of candidate hiring.

      Predict the result based on the academic results, interview score, specialization etc…

Non Functional Requirements:-

They basically deal with issues like:

      Security

      Maintainability

      Reliability

      Scalability

      Performance

CONCLUSION

Ø The system helps in improving the placement rate of an institution thereby can act as a key element in improving the reputation of the institution.

 

Ø  From the analysis, it is clear that the methodology used in system implementation is efficient enough to considerably improve the state of the art of classification technique that is employed so far in placement prediction field.

 

 

Ø  We proposed a system that predicts the probability of a student getting placed in a company by applying classification algorithm such as K-nearest neighbor, logistic regression, SVM and Decision Tree.

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