nirbhay12345/average_salary_model

Average Salary Dataset Analysis README

Overview

This dataset contains information about individuals, including demographic attributes such as age, education, occupation, etc., as well as whether their income exceeds $50K per year. The dataset is useful for various types of analyses, including demographic studies, income prediction modeling, and exploratory data analysis.

Dataset Information

• Format: CSV (Comma Separated Values)
• Columns:
  1. age: Age of the individual.
  2. workclass: Type of employer the individual works for.
  3. fnlwgt: Final weight used in sampling.
  4. education: Highest level of education achieved.
  5. educational-num: Numerical representation of education.
  6. marital-status: Marital status of the individual.
  7. occupation: Type of occupation.
  8. relationship: Relationship status.
  9. race: Race of the individual.
  10. gender: Gender of the individual.
  11. capital-gain: Capital gains of the individual.
  12. capital-loss: Capital losses of the individual.
  13. hours-per-week: Number of hours worked per week.
  14. native-country: Country of origin.
  15. income_>50K: Binary indicator of whether the individual's income exceeds $50K per year.

Analysis Tasks

1. Descriptive Statistics: Compute summary statistics for numerical columns (e.g., mean, median, standard deviation) and frequency distributions for categorical columns.
2. Data Cleaning: Check for missing values, outliers, and inconsistencies in the data. Handle them appropriately.
3. Exploratory Data Analysis (EDA): Explore relationships between different variables using visualizations (e.g., histograms, bar plots, scatter plots).
4. Feature Engineering: Create new features if necessary, derive insights from existing features.
5. Income Prediction Modeling: Build predictive models to predict whether an individual's income exceeds $50K per year based on available attributes. Evaluate model performance using appropriate metrics (e.g., accuracy, precision, recall).
6. Feature Importance: Determine which features have the most significant impact on predicting income levels.
7. Ethnicity and Gender Analysis: Explore any disparities in income levels based on ethnicity and gender.

Tools Used

• Python
• Pandas
• NumPy
• Matplotlib
• Scikit-learn
• Seaborn
• statsmodels.api

Contributors

• Nirbhay