Dataset: Balitmore Salary Prediction Database

About this Dataset

Balitmore Salary Prediction Database

This project aims to leverage the power of linear regression to predict the annual salaries of employees within Baltimore City's government agencies. By analyzing the relationships between various employee and agency factors, we can potentially build a reliable model to estimate salaries with significant accuracy.

Employee Demographics: Full names, job titles.
Salary Information: Annual salaries expressed numerically.
Agency Affiliation: Names of the city agencies employing individuals.
Hiring Dates: Specific dates and months and the year of hire.

Methodology:

Linear regression is the core predictive technique employed in this project. This statistical approach helps quantify the linear relationships between a dependent variable (annual salary) and one or more independent variables . Through careful feature engineering and model training, a robust model that accurately predicts salaries based on readily available employee data.

Tables

Balitmore Salry

@kaggle.pushpakhinglaspure_balitmore_salary_prediction_datase.balitmore_salry

497.09 KB
18895 rows
9 columns


CREATE TABLE balitmore_salry (
  "unnamed_0" BIGINT,
  "name" VARCHAR,
  "jobtitle" VARCHAR,
  "agencyid" VARCHAR,
  "agency" VARCHAR,
  "hire_date" BIGINT,
  "hire_month" BIGINT,
  "hire_year" BIGINT,
  "annualsalary" BIGINT
);

Train Salary

@kaggle.pushpakhinglaspure_balitmore_salary_prediction_datase.train_salary

561.03 KB
18981 rows
7 columns


CREATE TABLE train_salary (
  "n__name" VARCHAR,
  "jobtitle" VARCHAR,
  "agencyid" VARCHAR,
  "agency" VARCHAR,
  "hiredate" TIMESTAMP,
  "annualsalary" VARCHAR,
  "grosspay" VARCHAR
);