Personalized Career Pathway Recommendation System

This repository contains the code and documentation for a personalized career pathway recommendation system using machine learning and deep learning techniques. The system utilizes a dataset of resume summaries categorized into various job roles and explores different models to provide personalized and accurate career recommendations.

Table of Contents

• Introduction
• Dataset
• Methodology
• Models
  • K-Nearest Neighbors (KNN)
  • Support Vector Machines (SVM)
  • Logistic Regression
  • Random Forest
  • DistilBERT
  • TF-IDF and Cosine Similarity
• Evaluation
• Usage
• Contributing
• License

Introduction

In today's competitive job market, finding the right career path is crucial for individuals seeking professional growth. This project presents a practical approach to developing a career pathway recommendation system using machine learning and deep learning techniques. The system utilizes a dataset of resume summaries categorized into 28 job categories and explores various models to provide personalized and accurate career recommendations.

Dataset

The dataset used in this study is the Updated_Resume_Dataset.csv, which consists of three columns: labels, category (28 different job categories), and resume summary. This dataset provides a comprehensive collection of resume summaries that are categorized into various job roles, making it suitable for developing a career pathway recommendation system.

Methodology

The methodology involves several preprocessing steps, including text cleaning, tokenization, removal of stop words, lemmatization, and feature extraction using TF-IDF. The preprocessed data is then used to train and evaluate various machine learning and deep learning models.

Models

K-Nearest Neighbors (KNN)

The KNN model is trained using the TF-IDF transformed data. The code snippet for training and evaluating the KNN model can be found in the repository.

Support Vector Machines (SVM)

The SVM model is trained using the TF-IDF transformed data. The code snippet for training and evaluating the SVM model can be found in the repository.

Logistic Regression

The Logistic Regression model is trained using the TF-IDF transformed data. The code snippet for training and evaluating the Logistic Regression model can be found in the repository.

Random Forest

The Random Forest model is trained using the TF-IDF transformed data. The code snippet for training and evaluating the Random Forest model can be found in the repository.

DistilBERT

The DistilBERT model is trained using the preprocessed resume data. The code snippet for training and evaluating the DistilBERT model can be found in the repository.

TF-IDF and Cosine Similarity

TF-IDF (Term Frequency-Inverse Document Frequency) is used to convert textual data into numerical features. Cosine similarity is then used to find the most similar resumes. The code snippet for performing TF-IDF transformation and using cosine similarity can be found in the repository.

Evaluation

The performance of the models is evaluated using accuracy, precision, recall, and F1-score. Confusion matrices are also plotted to visualize the performance of the models.

Usage

To use the recommendation system, follow these steps:

1. Clone the repository: git clone https://github.com/Zeesejo/recommendsys.git cd recommendsys