The project aims to employ several supervised techniques to accurately predict individuals' income. The importance of this project lies in, for example, helping non-profit organizations evaluate their much-needed donation requests from different individuals.
The dataset that will be used is the Census income dataset, which was extracted from the machine learning repository (UCI), which contains about 32561 rows and 15 columns. The target variable in the data set is income level, which shows whether a person earns more than 50,000 per year or not, based on 14 features containing information on age, education, education-num, gender, native-country, marital status, final weight, occupation, work classification, gender, race, hours-per-week, capital loss, and capital gain.
So, the target variable (income) will be represented by binary classes. the class 0 for people having income less than or equal to 50k $ per year (<=50k $), and the class 1 for people having income more than 50k $ per year (>50k $).
This project requires Python 3.x and the following Python libraries installed:
CensusIncomePrediction/ (that contains this README) and run the following commands:
conda create -p venv python==3.9conda activate venv/