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:
You will also need to have the software installed to run and execute an iPython Notebook.
In a terminal or command window, navigate to the top-level project directory Census-Income-Prediction/ (that contains this README) and run one of the following commands:
ipython notebook income_prediction.ipynbor
jupyter notebook income_prediction.ipynbThis will open the iPython Notebook software and project file in your browser.
After cleaning and preparing the data for the models, cross-validation has been done and other operations to compare between the models.
Finally, The Random Forest Classifier has been chosen, for achieving very high results in terms of evaluation metrics. A 93% accuracy score, also the same result for F1-score.