A package for running Group 4's data analysis
Note: This package requires python version 3.9.1 or greater
$ pip install group4packageThe following functions in this package were created for Group 4's Project Predicting Defaults of Credit Card Clients. However, they are useful for any data analysis that involves pre-processing data and calculating classification metrics and statistics.
calculate_metrics(FP, TN, TP)
- calulates Recall, F1-Score and Precision for a classification model
Example:
from group4package import metrics_function as cm
TN, FP, FN, TP = confusion_matrix(y_test, predict).ravel()
res = cm.calculate_metrics(FP, FN, TP)get_summary_stats(df)
- calculates summary statistics including mean, std, min, and max of numeric columns of a dataframe
Example:
from group4package import summary_stats_function as ss
ss.get_summary_stats(train_df)count_plot(df, x, name)
- creates a plot of the counts between x and y values of the given dataframe, while the latter two arguments are used as the x-axis label and title of the produced plot
Example:
from group4package.function_count_plot import count_plot
new_plot = count_plot(df=train_df, x="x-axis label", name="Plot Title")pre-process data(df, train_frac, seed)
- drops all rows with missing or null values and then splits the dataframe into training and testing sets according to the given split ratio
Example:
from group4package import preprocess as pp
train_df, test_df = pp.preprocess(df, 0.8, 200)Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.
group4package was created by Hannah, Jordan, Diana, and Shravan. It is licensed under the terms of the MIT license.
group4package was created with cookiecutter and the py-pkgs-cookiecutter template.