📊 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