In this notebook we try to practice all the classification algorithms that we learned in this course.
We load a dataset using Pandas library, and apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.
Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.
You can go above and change the pre-processing, feature selection, feature-extraction, and so on, to make a better model.
You should use either scikit-learn, Scipy or Numpy libraries for developing the classification algorithms.
You should include the code of the algorithm in the following cells.
Notice: You should find the best k to build the model with the best accuracy.
warning: You should not use the loan_test.csv for finding the best k, however, you can split your train_loan.csv into train and test to find the best k.
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
Thanks for completing this lesson!
Copyright © 2018 Cognitive Class. This notebook and its source code are released under the terms of the MIT License.