We employ several supervised algorithms to accurately predict individuals' income using data collected from the 1994 U.S. Census. This sort of task can arise in a non-profit setting, where organizations survive on donations and would be greatly benefitted by such a model for better targeting of specific individuals. Aim is to construct a model that accurately predicts whether an individual makes more than $50,000. We try out three models viz. SVM, Decision Trees and AdaBoost. We finally choose the AdaBoost implementation as it had higher accuracy on test-set and significantly faster training time compared to the SVM algorithm
The implementation uses the UCI Machine Learning data
- Install jupyter notebook to open the main file. For new users, its recommended to install Anaconda from here
- Navigate to the specific folder and clone the repository using the following command:
git clone https://github.com/jsarneja/finding_donors.git - Open the jupyter notebook
finding_donors.ipynb- Some additional datasets would be needed. Relevant links are provided in the jupyter notebook
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