International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024
p-ISSN: 2395-0072
www.irjet.net
SMART PREP: Smart Preparation Web Application for Placements Using Machine Learning Prasanna Kandekar1, Manjiri Raut2, Shravani Kadam3, Atharv Aundhkar4, Ajinkya Divekar5,Pushpak Ithule6 1,2 Assistant Professor, Dept of Computer Engineering, Keystone School of Engineering,
Maharashtra, India
3,4,5,6 BE student, Dept of Computer Engineering, Keystone School of Engineering, Maharashtra,
India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract-Smart Prep is an innovative project poised to 1.2 Motivation revolutionize the recruitment landscape by leveraging machine learning and interview assessments to forecast an individual's likelihood of securing a position within a specific company. Building upon the success of "Smart Hire," which utilizes image recognition technology in interviews, Smart Prep combines traditional interview metrics with advanced data-driven techniques to provide invaluable insights into candidate suitability. By meticulously analyzing factors such as communication skills, problem-solving abilities, and cultural compatibility, Smart Prep generates a predictive model that accurately forecasts a candidate's chances of success. Moreover, by integrating image recognition technology, Smart Prep offers an additional layer of insight by analyzing non-verbal cues and expressions. In a rapidly evolving job market, where making informed hiring decisions is crucial, Smart Prep empowers companies with a refined, data-backed approach to recruitment, thereby minimizing mismatches and streamlining the hiring process.
The motivation behind the creation of SMART PREP stems from the recognition of the challenges faced by candidates in navigating the complex landscape of placement examinations and interviews. By leveraging the power of AI/ML algorithms, SMART PREP aims to provide candidates with tailored study materials, personalized feedback, and predictive insights into their placement prospects. This not only empowers candidates to make informed decisions about their preparation strategies but also enhances their overall confidence and readiness for the placement process.
1.3 Need of Research The need for research in this domain arises from the desire to address the shortcomings of existing online placement preparation tools and techniques. While several platforms offer study materials and practice tests, few integrate advanced AI/ML capabilities to provide personalized guidance and predictive analytics. By conducting research on the development and evaluation of SMART PREP, this paper seeks to contribute to the ongoing discourse on the intersection of technology and education, particularly in the context of placement preparation. Through empirical analysis and user feedback, this research aims to assess the efficacy and impact of AI/ML-driven approaches in improving candidate outcomes and bridging the gap between academic knowledge and industry requirements
Key Words: Machine Learning, Interview Assessment, Candidate Evaluation, Predictive Analysis, Image Recognition.
1.INTRODUCTION 1.1 Context Online placement preparation has become increasingly crucial in today's competitive job market, where candidates must demonstrate proficiency in various skills to secure employment opportunities. Traditional methods of preparation often lack personalized feedback and fail to adapt to individual learning styles, leading to suboptimal outcomes for candidates. In this context, the development of advanced online tools like SMART PREP, utilizing AI/ML technologies, offers a promising solution to enhance the efficiency and effectiveness of placement preparation.
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Impact Factor value: 8.226
2. LITEATURE SURVEY Title: Advancements in Student Placement Prediction Through Machine Learning Techniques Publication year: March 2022 Findings: In the research, Author delves into machine learning algorithms, focusing on Decision Tree, Naïve Bayes, and Random Forest. The study concludes that the Random Forest classifier exhibits superior accuracy, reaching an impressive 86% compared to Decision Tree and Naïve Bayes.
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