Project Overview: Predicting Income using Supervised Classification
The objective of our project is to build a predictive model that can classify individuals' income as either above 50k or below 50k based on various demographic and occupational features. We will be using a dataset called the Adult census income dataset from Kaggle, which consists of approximately 32,561 records and 15 different features.
Problem Type: Supervised Classification
Our project falls under the category of supervised classification. Supervised learning involves training a model on labeled data, where the input features are associated with known output labels. In our case, the features include age, education, occupation, and other relevant attributes, while the output labels indicate whether an individual's income exceeds 50k or not.
Dataset Description
The Adult census income dataset contains a mix of categorical and numerical features, providing valuable insights into the socioeconomic characteristics of individuals. Some of the key features include age, education level, occupation, work hours per week, marital status, and more. Additionally, the dataset includes the target variable, which is a binary variable indicating income status (above 50k or below 50k).