International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
www.irjet.net
Career Guidance & Recommendation on Placement using Machine Learning and DSA Visualizer Jaldu Bhupesh Sai Sri1, Malees Greeshma2, Maddi Sateesh Reddy3 1Final Year Undergraduate Student, Dept. of Computer Science and Engineering, Joginpally B.R. Engineering
College (JBREC), Hyderabad, Telangana, India
2Final Year Undergraduate Student, Dept. of Computer Science and Engineering, Joginpally B.R. Engineering
College (JBREC), Hyderabad, Telangana, India
3Final Year Undergraduate Student, Dept. of Computer Science and Engineering, Joginpally B.R. Engineering
College (JBREC), Hyderabad, Telangana, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - In the rapidly evolving landscape of software
time data on industry trends. This research paper presents a unified web-based platform that addresses these challenges holistically. The proposed system combines:
engineering, students face a dual challenge: mastering complex Data Structures and Algorithms(DSA) required for technical interviews and navigating an overwhelming array of career specializations. While existing solutions address these issues in isolation - either through standalone algorithm visualizers or static career counselling tools- there is a lack of integrated platforms that bridge the gap between skill acquisition and career discovery. This paper proposes a novel, dual module system. The first module is an interactive Algorithm Visualizer developed using React, capable of animating sorting, searching, and graph algorithms to enhance conceptual retention. The second module is an Ai-Job Recommendation Engine built on a Flask backend. It utilizes TF-IDF vectorization and Cosine Similarity on a dataset of over 1 million job records to match user skills with optimal job roles. Furthermore, the system integrates Generative Ai (via Groq/Llama-3) to create personalized learning roadmaps and aggregates course resources. The results demonstrate a high-precision matching capability and an improved learning experience for students preparing for technical placements.
1.1 Visual Learning A dynamic visualizer for arrays, linked lists, stack, queue, trees, graphs.
1.2 Intelligent Guidance A content-based recommendation system that analyses user skills against a massive dataset of job descriptions to predict suitable roles.
1.3 Actionable Roadmaps Integration of LLMs to generate step-by-step preparation guides for the recommended roles.
2. LITERATURE SURVEY The development of this system draws upon various domains of educational technology and machine learning.
Key Words: Algorithm visualizer, sorting, searching, graph algorithms, Ai-Job Recommendation Engine, TFIDF vectorization, Cosine Similarity
2.1 Placement Prediction Systems Kulkarni et al. developed an algorithm visualizer using React.js, focusing on sorting and pathfinding algorithms like Merge Sort, Dijkstra’s algorithm. Their research highlighted that visual information is processed faster than abstract text, significantly aiding student retention. However, their scope was limited to visualization without linking these skills to specific career outcomes.
1. INTRODUCTION The demand for skilled software engineers has led to a proliferation of specialized roles, ranging from Data Science to Full Stack Development. However, a significant disconnects remains between the academic curriculum and industry requirements. Two primary hurdles exit for engineering students: the difficulty in visualizing abstract algorithmic concepts and the uncertainty in selecting a career path that aligns with their acquired skills.
2.2 Placement Prediction Systems Divya et al. proposed a placement analysis system using supervised machine learning algorithms such as Support Vector Machines(SVM) and Random Forest. Their work focused on classifying students based on academic history(grades) to predict the probability of placement. While effective for administrative forecasting, it lacks a
Traditional teaching methods for algorithms often rely on static diagrams, which fail to convey the dynamic nature of operations like recursive tree traversals or graph pathfinding. Simultaneously, career guidance is often subjective, relying on human advisors who may lack real-
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