/loan-payment-foresee

Primary LanguagePython

Puzzle

Observations:

  • To run the project just execute the main.py file like this if you have all the requeriments installed.
    • python main.py
  • If you receive any message about a missing module, try installing it via pip or mail me at marcelloaborges@gmail.com for details.
  • The project has already a trained checkpoint file that can be used if you want to avoid the training. The tester uses the checkpoint, so if you remove or rename it, run the training again before executing the test.
  • The execution of the main will replace the checkpoint file and the predictions file.

Requeriments:

  • python: 3.6
  • numpy: 1.11.0
  • torch: 0.4.1
  • pandas
  • scipy
  • ipykernel

The problem:

As a credit company, it is important to know beforehand who is able to pay their loans and who is not. The goal of this puzzle is to build a statistical/machine learning model to figure out which clients are able to honor their debt.

The goal:

Predict the probability of default, which is identified by the default variable in the training dataset.

The solution:

As this is a classification problem with one answer, yes or no, I decided to use a binary classification structure for the neural model with a sigmoid activation function in the output.
After deciding the neural model, the first step was to analyse the data and identify the columns that could be removed due to its nonnumeric or non categorical values.
So, I moved to the cleaning data phase. For the missing data, categorical columns were filled with default values and for the numerical columns, I used the median.
With this scenery, after applying the default data normalization, I got 84% of accuracy into a confusion matrix evaluation with the neural model structure described below.
Then I tried to improve the results using regression for the missing data on the numerical columns. The loss established a little but the accuracy kept the same.
The last improvement was removing the outliers and here there is a curious fact for this database. I used the quantile approach to generate NaN for the outliers and then remove them from the data, but I found that all the rows had at least one column with a NaN value generated after the quantile apply. Remove the rows with NaN generated by the quantile wasn't an option anymore, so I tried to use the mean instead of removing the rows with NaN values generated by the quantile but the results got worse, so I removed the outliers treatment and kept the solution as it was before this last step.

Results:

  • Loss: ~40% (binary cross-entropy)
  • Accuracy: ~84% (round of the sigmoid output compared to the default column)

The hyperparameters:

  • The file with the hyperparameters configuration is the main.py.

  • Configuration:

    • FEED_SIZE = 14
    • FC1_UNITS = 64
    • FC2_UNITS = 32
    • OUTPUT_SIZE = 1
    • LR = 1e-3
    • BATCH_SIZE = 512
    • EPOCHS = 10

  • The neural model configuration is into the model.py file.

  • The actual architecture is:

    • Hidden: (feed_size, fc1_units) - ReLU
    • Dropout: fixed with 0.2
    • Hidden: (fc1_units, fc2_units) - ReLU
    • Output: (fc2, output_size) - Sigmoid