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📊 Wage Data Analysis (EDA & Regression)

🔍 Overview

This project performs Exploratory Data Analysis (EDA) on wage data to understand how socio-economic factors such as education, experience, gender, and marital status influence hourly wages.

⚙️ Tools & Technologies

  • Python
  • Pandas, NumPy
  • Matplotlib, Seaborn
  • Statsmodels

📈 Analysis Performed

  • Data cleaning & handling missing values
  • Descriptive statistics
  • Data visualization (histograms, boxplots, heatmaps)
  • Correlation analysis
  • Hypothesis testing (Chi-square, T-test)
  • Regression modeling

📊 Key Insights

  • Education and work experience positively impact wages
  • Wage distribution is right-skewed with outliers
  • Gender-based wage differences observed
  • Regression model explains significant variation in wages

📁 Project Files

  • wage_eda.ipynb → Full analysis notebook
  • wage.xlsx → Dataset
  • Wage_EDA.pdf → Detailed report