Dataset: Fake Jobs Posting Detection

About this Dataset

Fake Jobs Posting Detection

Classification of Real and Fake Jobpostings Using Ensemble Model
Classification of dataset with imbalanced classes and the treatment of missing values are two of the tricky issues of machine learning. In this notebook on a dataset of real and fake job postings, I am trying to tackle these two problems with an ensemble model, by training three different machine learning models with different segments of samples, and taking a simple majority vote of three models as the final predictions. Apparently the three models complement each other and achieve decent results.

Tables

Fake Job Postings

@kaggle.josereimondez_fake_jobs_posting_detection.fake_job_postings

21.96 MB
17880 rows
18 columns


CREATE TABLE fake_job_postings (
  "job_id" BIGINT,
  "title" VARCHAR,
  "location" VARCHAR,
  "department" VARCHAR,
  "salary_range" VARCHAR,
  "company_profile" VARCHAR,
  "description" VARCHAR,
  "requirements" VARCHAR,
  "benefits" VARCHAR,
  "telecommuting" BIGINT,
  "has_company_logo" BIGINT,
  "has_questions" BIGINT,
  "employment_type" VARCHAR,
  "required_experience" VARCHAR,
  "required_education" VARCHAR,
  "industry" VARCHAR,
  "function" VARCHAR,
  "fraudulent" BIGINT
);