This dataset contains 18K job descriptions out of which about 800 are fake. The data consists of both textual information and meta-information about the jobs. The dataset can be used to create classification models which can learn the job descriptions which are fraudulent.
Create a classification model that uses text data features and meta-features and predict which job description are fraudulent or real. Identify key traits/features (words, entities, phrases) of job descriptions which are fraudulent in nature. Run a contextual embedding model to identify the most similar job descriptions. Perform Exploratory Data Analysis on the dataset to identify interesting insights from this dataset. Machine learning model to predict a given job posting is fraud or not
Source : https://www.kaggle.com/shivamb/real-or-fake-fake-jobposting-prediction
All of the data is in 1 csv file: fake_job_postings.csv(50.06 MB) This file contains the dataset of job descriptions and their meta information. A small proportion of these descriptions are fake or scam which can be identified by the column "fraudulent". It has 18 coloumns
An exploratory data analysis is performed by analysing dataset as a whole and each of the 18 columns individually