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, interview cheat sheet 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

💡 Why This Notebook Beats GitHub or ChatGPT

• GitHub repos may show fragments of regression code, but they rarely include curated salary datasets with outlier cleaning and Glassdoor validation.
• ChatGPT can generate models, but it doesn’t guarantee reproducible projects with feature engineering, clear benchmarks or portfolio-ready framing.
• Skip days of trial‑and‑error: get a plug‑and‑play salary prediction project with clear comments, polished visuals, transparent metrics and freedom to add your own code.

🎁 Bonus: Includes a concise interview cheat sheet for data analytics and machine learning interviews, designed for quick revision and transferable beyond salary prediction use cases.

📊 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