A package for doing great things!
Project description
my_krml_25552249
My Python package for data preparation, feature engineering, and model evaluation.
Package Structure
my_krml_25552249/
│
├── data/
│ └── sets.py # pop_target, split_sets_random, split_sets_by_time, save_sets, load_sets
│
├── features/
│ ├── impute.py # impute_missing
│ └── dates.py # convert_to_date
│
├── models/
│ └── performance.py # metrics, confusion matrix, plots, cross-val, etc.
Installation
$ pip install my_krml_25552249
Usage
This package is organized into data handling, feature engineering, and model evaluation modules. You can import the relevant module depending on your task, and each module provides simple utility functions to help you prepare your data, engineer features, evaluate models, and visualise results.
Below are some basic examples to get you started.
Data Handling
from my_krml_25552249.data.sets import pop_target, split_sets_random
# Example: split features and target
X, y = pop_target(df, "target")
X_train, y_train, X_val, y_val, X_test, y_test = split_sets_random(X, y)
Feature Engineering
from my_krml_25552249.features import impute_missing
from my_krml_25552249.features.dates import convert_to_date
# Example: impute missing values
df["column"] = impute_missing(df["column"], strategy="median")
# Example: convert columns to datetime
df = convert_to_date(df, cols=["date_column"])
Model Evaluation
from my_krml_25552249.models.performance import print_regressor_scores
# Example: print regression metrics
print_regressor_scores(y_preds, y_true, set_name="Test")
Visualisation
from my_krml_25552249.models.performance import plot_confusion_matrix
# Example: plot confusion matrix of a model
plot_confusion_matrix(model, X_train, y_train, title="Training Confusion Matrix")
Contributing
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.
License
my_krml_25552249 was created by Shawya. It is licensed under the terms of the MIT license.
Credits
my_krml_25552249 was created with cookiecutter and the py-pkgs-cookiecutter template.
Project details
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