Luigi Link - http://ec2-52-32-210-114.us-west-2.compute.amazonaws.com:8082/
Project Link - http://ec2-52-32-210-114.us-west-2.compute.amazonaws.com:8000/
docker run -it -p 8000:8000 -p 8082:8082 sreeragsreenath/finalads cd mlbcsite/ python3 sunprocess.py "aws s3 id" "aws s3 key"
MLBC is a Web based platform for candidates who are seeking a job and recruiter who are looking for the right talent. The platform scrapes and analyzes publicly available job posting data from sites like Glassdoor, LinkedIn, etc. The system will recommend the candidate which job title will fit him/her based on their resume. The system will also give job links to the application form. The platform will also help us to gain statistical information about the latest job trend.
The Supervising authority The Supervising authority is a person that governs the entire system. Having supervisory powers over some aspects of management decision-making. Basically a person with more power than others. The Beneficiaries (or users) The beneficiaries (or users) are all natural persons or corporate body, students , recipients of the services provided by MBCS.
The Recruiters A person whose job is to enlist or enroll people as employees as members of an organization.
The steps involved
We have used an existing dataset provided by Kaggle DataSet : Scrapped information and skill sets from websites such as Indeed This will be done by giving the url as input to a web scraper Library such as scrapy will be used to run multiple web crawlers on a single website to optimise scrapping process Analysing the data and segregating the content to skills and tools mandatory and the expertise level The data is stored in SQLite database supported by Django server for further processing We tried out different machine learning models and hybrid techniques to optimise the result Made a web based application to interact with the system through which the user can upload his/her data. The web based application and the authentication is done using python Django. The Web application allows the user to upload the document and it will Parse the users resume and scrape relevant information i.e. tools, skills, location, work experience and serve it as parameters for our prediction model. After the model is executed, it will output “Best suited position” according to his/her resume along with Ranking. It also outputs latest Job postings where the user can apply. The entire process is pipelined using LUIGI. We have deployed the Django application on EC2 server and storing and retrieving the model from Amazon S3. Finally we have dockerized the entire application, so a person with any operating system can simply pull the docker image and run it on his own machine.
Project Tools: Language : Python Pipeline : Luigi Framework : Django Databse : SQL Tools used: Jupyter Notebooks and Docker , Xamp.
Libraries Used :- Django PyPDF2 NLTK TextBlob Boto3
Exploratory Data Analysis Conduct EDA using Python libraries - Seaborn, Numpy, MissingNo, Pickle, Plotly, Pandas, Scipy, Sklearn, Matplotlib
- Language used : Python
- Process Followed : Data Ingestion, Data Wrangling, Exploratory Data Analysis
- Tools used : Jupyter Notebook
Our website will show you the Preferred Job Postings as well as the Positions one should apply.