The Job-Matching-Engine project is designed to bridge the gap between job seekers and their ideal job positions. By leveraging advanced machine learning algorithms, this system aims to automatically match job seeker profiles with available job descriptions, ensuring the most relevant opportunities are presented to each candidate. This project encompasses a range of tasks, from data collection and preprocessing to model deployment, with a focus on creating a highly accurate and user-friendly job recommendation service.
Automatic Job Matching: Automatically matches job seekers with job descriptions based on various relevant features. Comprehensive Feature Selection: Utilizes features such as keywords, location, experience, education, work type, job title, and visa sponsorship status to ensure precise matching. Advanced Modeling Techniques: Employs neural networks or decision trees, trained on AWS SageMaker, to predict the best job matches. Model Evaluation and Tuning: Leverages metrics such as accuracy, precision, recall, and F1-score for model optimization. Performance Monitoring: Incorporates AWS CloudWatch for continuous monitoring of model performance. Deployment as a Service: Offers the model as a service that inputs job seeker profiles and outputs the top matching job descriptions.
Data Collection and Preprocessing: Aggregating training data from diverse sources, including resumes and job descriptions, followed by cleaning and formatting the data for model training. Feature Selection: Identifying and selecting the most relevant features that contribute to the effectiveness of job matches. Model Training: Choosing an appropriate machine learning model and training it with the preprocessed data on AWS SageMaker. Model Evaluation: Evaluating the model's performance using various metrics and fine-tuning it to enhance its prediction accuracy and reliability. Monitoring: Setting up AWS CloudWatch or similar services to monitor the model's performance in real-time, ensuring consistent accuracy over time. Deployment: Deploying the model as a scalable service, capable of providing real-time job matching based on job seeker profiles.
AWS SageMaker: For training and deploying machine learning models. AWS CloudWatch: For monitoring model performance and system logs. Python: Primary programming language for data preprocessing, model training, and evaluation. Various Machine Learning Libraries: Such as TensorFlow or Scikit-learn, depending on the chosen model architecture.
AWS account for using SageMaker and CloudWatch Python environment set up with necessary libraries (TensorFlow/Scikit-learn, Pandas, NumPy)
Clone the Repository: bash Copy code git clone https://github.com/murtazahh/Job-Matching-Engine/tree/main
Configure AWS CLI with your credentials. Ensure SageMaker and CloudWatch have the necessary permissions set up. Prepare Your Data:
Collect and preprocess your datasets. Upload the prepared data to an S3 bucket. Train Your Model:
Follow the instructions in the training scripts to launch a training job on SageMaker. Deploy the Model:
Use the deployment script to create an endpoint for your model. Monitor Performance:
Set up monitoring on CloudWatch to track the model's performance.
The deployed service can be accessed via a simple API call, where you pass in the job seeker's profile information and receive a list of the top matching job descriptions.
We welcome contributions from the community. Please read the contributing guide for more information on how you can contribute to the Job-Matching-Engine project.
Use of AWS SageMaker and CloudWatch for training, deploying, and monitoring the machine learning models. Leveraging existing research and best practices in job recommender systems.
The project would also leverage existing research on job recommender systems and use the best practices and techniques from the literature.