This project is a descriptive analysis of various supervised learning classifiers like Decision Tree, Random Forest, Naive Bayesian, Support Vector Machines and Neural Network.
- The dataset is taken from the UCI repository from here.
- The dataset contains 14 attributes describing an individual personal and working condition.
- The class label is income column giving the information whether an individual's yearly income is less than or greater that equal to 50K.
- The code is written in python using various libraries like pandas, numpy and sklearn.
- I have used Jupyter notebook for easier understanding and execution of the codes.
- Tutorial on how to use Jupyter notebook
If any libraries are missing on the system simply pip install them and re-run the code
pip install <module>
I have divided the code into 6 jupyter files where the first 5 files are running the a specific classifier and the compiler file simply compares the results and plot the data.
The files corresponding to each classifier is as following:-
Ritvik Khanna