Predict Data Science Salaries with Python - Kaggle Dataset, Full EDA and Machine Learning Models

This Python notebook walks you through a complete data-driven salary prediction project using real-world Data Science Salaries data from Kaggle.
You’ll learn how to clean, analyze and model salary data for various job roles, experience levels and countries - exactly how a professional data analyst or data scientist would approach a pay prediction problem.

🔍 What’s Inside

• Python notebook, Kaggle dataset and README file with setup instructions
• Full Exploratory Data Analysis (EDA): salary trends by role, experience, and geography
• Comprehensive outlier detection and cleaning using IQR, boxplots, and real-world salary benchmarks (Glassdoor validation)
• Feature engineering and encoding for model-readiness
• Model comparison across Linear Regression, Decision Trees, Random Forest, and XGBoost
• Performance metrics (R², MSE, MAE) for transparent evaluation
• Clean, readable code with detailed plain-English comments explaining each step

📊 Use Cases

• Build a data-driven salary prediction model from scratch
• Strengthen your portfolio for data analytics or ML job interviews
• Learn practical EDA and regression techniques on a real Kaggle dataset
• Explore salary insights across roles, experience, and countries

📁 Requirements

• Python 3.7+
• Jupyter Notebook
• Libraries: pandas, numpy, matplotlib, seaborn, sklearn, xgboost

📎 Dataset Source
Public Kaggle dataset: Data Science Salaries 2023 by Arnab Chaki

💡 Ideal For

• Data Science & ML learners
• Analysts and aspiring professionals building real-world portfolio projects
• Anyone interested in salary analytics and predictive modeling