• Deepak N
• Gowtham M
• Mithilesh J
• Naresh Kumar A

The Career Recommendation System (CRS) leverages machine learning algorithms and data analytics to provide personalized career guidance based on individual skills, interests, and aspirations. This paper outlines the CRS architecture, methodology, and key components, including data preprocessing techniques and integration with Django models. Challenges encountered during development are discussed, alongside performance evaluation results. The CRS empowers users with tailored career recommendations, facilitating informed decisions in navigating the modern job market. Keywords: Career Recommendation, Personalized Guidance, Machine Learning, Data Analytics, Django Integration.

Career Recommendation, Personalized Guidance, Machine Learning, Data Analytics, Django Integration, User Profiling, Data Preprocessing, Model Evaluation, Job Market, Career Exploration.

"Personalized Career Recommendation System Based on Customer Segmentation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.k348-k352, March-2024, Available :http://www.jetir.org/papers/JETIR2403A44.pdf

"Personalized Career Recommendation System Based on Customer Segmentation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppk348-k352, March-2024, Available at : http://www.jetir.org/papers/JETIR2403A44.pdf