Salary Prediction Using ML

Project Overview

This project aims to predict an individual's salary based on a dataset of job-related and demographic features. By analyzing factors such as age, gender, education level, job title, and years of experience, the goal is to develop a regression model that can accurately estimate an individual's salary. This is a valuable tool for career planning, salary negotiation, and labor market analysis.

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Technical Highlights

• Dataset: A dataset named Salary Data.csv is used, which contains salary information and various employee attributes.
• Size: 375 entries, 6 columns.
• Key Features:
  • Age, Gender, Education Level, Job Title, Years of Experience.
• Approach:
  • Data Cleaning: The code handles missing values by dropping rows with NaN. It also removes duplicate entries. Additionally, it drops Age, Gender, and Job Title columns, which is a significant reduction of features.
  • Exploratory Data Analysis: Histograms and box plots were used to visualize the distribution of numerical features and their relationship with salary. Count plots were also used for categorical features.
  • Label Encoding: Applied to the Education Level column to convert it into a numerical format.
  • Regression Task: The target variable is Salary.
  • Models Used:
    • A suite of regression models were trained, including Linear Regression, Ridge, XGBoost, Random Forest, AdaBoost, Gradient Boosting, and Bagging.
• Best R² Score:
  • 0.901 with Linear Regression and Ridge Regressor.
  • 0.892 with Gradient Boosting Regressor.
  • The high R² scores indicate that the models are highly effective at predicting salary based on the chosen features.

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Purpose and Applications

• Accurate Salary Forecasting: Enables individuals to estimate their potential earnings based on their experience and education.
• Recruitment and Compensation: Assists companies in setting competitive salary ranges for different roles.
• Career Planning: Provides insights into how education and experience levels correlate with income.
• Labor Market Analysis: Supports data-driven research on salary trends and economic factors.

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Installation

Install the necessary libraries:

pip install pandas numpy seaborn matplotlib scikit-learn xgboost

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Collaboration

We welcome contributions to improve the project. You can help by:

• Re-evaluating the feature selection process, as dropping Age, Gender, and Job Title may remove valuable predictive information.
• Exploring more robust methods for handling missing values and duplicates.
• Performing comprehensive hyperparameter tuning and cross-validation for all regression models to maximize predictive performance.
• Adding explainability (e.g., SHAP or LIME) to understand which factors are the most significant drivers of salary.