income-prediction

Project Description:

This project focused on analyzing income distribution and its relationship with various demographic and socioeconomic factors. The goal was to uncover patterns and provide insights into how variables like education, occupation, age, and gender influence income levels.

What I Did:

Data Preparation:

Cleaned and processed the dataset, handling missing values and outliers.

Standardized numerical data and encoded categorical variables for analysis.

Exploratory Data Analysis (EDA):

Visualized income distribution using histograms, boxplots, and violin plots.

Explored the relationship between income and factors such as education level, work experience, and gender.

Feature Engineering:

Created new features like income brackets, experience-to-income ratios, and regional income averages for more detailed analysis.

Insights and Findings:

Identified key factors influencing income, such as education level, industry type, and geographical region.

Highlighted income disparities across gender and education levels.

Provided actionable insights on how additional qualifications or skills could improve income potential.

Predictive Modeling:

Built regression models (e.g., Linear Regression, Decision Trees) to predict income based on demographic and socioeconomic features.

Evaluated model performance using metrics like R-squared and Mean Absolute Error (MAE).

Tools and Technologies Used:

Python (pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn)

Jupyter Notebook for analysis and visualization.

Results and Insights:

Presented actionable insights on income inequality and key drivers of high-income potential.

Developed predictive models to estimate income levels for individuals based on specific attributes.