salary prediction classification

This project focuses on building a Machine Learning Classification model to predict whether an individual's salary is greater than 50K or less than or equal to 50K based on demographic and work-related features.

The dataset contains over 32,000 records and includes features such as age, education, occupation, working hours, and marital status. The main objective is to analyze these factors and understand their impact on salary levels.

I started by exploring the dataset using Exploratory Data Analysis (EDA) to understand the structure, distributions, and relationships between variables. The data was clean with no missing values, which made preprocessing more straightforward.

Next, I performed necessary preprocessing steps, including handling categorical variables through encoding and preparing the data for model training.

Finally, I built a classification model to predict salary categories and evaluated its performance using appropriate metrics.

This project demonstrates the complete workflow of a machine learning pipeline, from data exploration to model evaluation, and highlights how data-driven approaches can be used to make predictions in real-world scenarios.