Job Prediction This project predicts the likelihood of a candidate getting a job based on various features such as skills, experience, education, and other factors.

Table of Contents Overview Data Collection Model Training Prediction Process Requirements Overview The goal of this project is to predict if a job applicant will get a job based on multiple factors, such as their qualifications, experience, and skills. A machine learning model is trained on historical job data to make accurate predictions.

Data Collection Input Data Data includes:

Candidate Information: Skills, education, experience, location, etc. Job Requirements: Skills required, job location, experience level. Outcome: Whether the candidate was hired or not (1 = hired, 0 = not hired). Data Format The data is stored in a structured format like CSV or JSON:

candidate_skills, education_level, years_of_experience, job_location, hired (0/1), etc. Model Training The model is trained using a dataset with historical job application data. Common models used for this project include:

Logistic Regression Decision Trees Random Forest Support Vector Machine (SVM) The model predicts the likelihood of being hired based on the candidate's features.

Prediction Process Once the model is trained, you can input a new candidate’s details and receive a prediction on whether they are likely to be hired for a given job.

Requirements Python 3.x Libraries: scikit-learn, pandas, numpy, matplotlib, etc. Jupyter Notebook or any Python IDE