Skip to content

4 machine learning models applied to 2 seperate binary classification problems each. These models include decision trees, neural networks, random forest bagging, and k-nearest neighbor.

License

Notifications You must be signed in to change notification settings

sully020/supervised_classification_ml

Repository files navigation

Instructions:
1.) Download and open main.py.
2.) Ensure the following libraries are installed:
    a.) pandas
    b.) numpy
    c.) sklearn
3.) Ensure that the emails.csv and diabetes.csv files are in the same folder as main.py.
4.) Run main.py, and input your choice of learner
for each problem as they are typed out in the prompt.

The code will report to you the mean test score, training score, and training time of the learners which you chose for each respective problem. Un-comment the calls to 'generate_data_heatmap()' in order to receive a visual representation of the confusion matrix for each specific learner.

About

4 machine learning models applied to 2 seperate binary classification problems each. These models include decision trees, neural networks, random forest bagging, and k-nearest neighbor.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages