AI-Based Hiring Prediction System

Project Overview

This project builds an end-to-end Machine Learning model to predict whether a candidate will be:

• Hired (1)
• Rejected (0)

based on resume features such as skills, experience, education, certifications, projects, and salary expectations.

The system simulates a real-world AI resume screening tool used in HR analytics.

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Dataset Description

The dataset contains 1000+ synthetic resumes with the following columns:

• Resume_ID (Dropped)
• Name (Dropped)
• Skills
• Experience (Years)
• Education
• Certifications
• Job Role
• Recruiter Decision (Target Variable)
• Salary Expectation ($)
• Projects Count
• AI Score (Not used to avoid data leakage)

Target Encoding:

• Hire -> 1
• Reject -> 0

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Project Workflow

1. Data Loading & Inspection

   • Checked shape, column names, data types
   • Analyzed missing values
   • Reviewed summary statistics

2. Data Cleaning

   • Dropped Resume_ID and Name
   • Converted Recruiter Decision to binary (0/1)
   • Handled missing values

3. Text Feature Engineering

   • Combined Skills, Certifications, and Job Role
   • Cleaned text (lowercase, removed special characters, trimmed spaces)
   • Applied TF-IDF Vectorization

4. Categorical Encoding

   • Encoded Education using Label Encoding / One-Hot Encoding

5. Feature Scaling

   • Applied StandardScaler to:
     • Experience (Years)
     • Salary Expectation
     • Projects Count

6. Train-Test Split

   • 80% Training
   • 20% Testing
   • random_state = 42

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Models Implemented

• Logistic Regression
• Random Forest
• Support Vector Machine (SVM)
• K-Nearest Neighbors (KNN)

Models were evaluated using:

• Accuracy Score
• Classification Report

The best-performing model was selected based on accuracy and overall performance.

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Final Output

The system predicts:

• Hire or Reject
• Probability Score

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Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn

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Conclusion

This project demonstrates:

• Data preprocessing and feature engineering
• Text vectorization using TF-IDF
• Model training and comparison
• Practical implementation of an AI hiring prediction system

It reflects real-world HR automation and resume screening applications.