This project aims to compare two diffrent logistic regression models. One trains with imbalanced data and one used random oversampling with the goal of finding out differences in their respecitive predictive performances. The dataset is viewed below.
After doing predictive analysis using both models, we found accuracy scores for the two types of models:
- Imbalanced Data Balanced Accuracy Score: ~95%
- Randomly Oversampled Data Balanced Accuracy Score: ~99%
From looking at this, we can understand that using the Random Oversampling model is better than the alternative method we compared.
If you would like to clone the repository, type "git clone https://github.com/kheller18/credit_risk_resampling.git". In the terminal, with the conda dev environment activated, install the following packages and dependencies before running the crime analysis application. To understand how to install these, refer to the Usage
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csv - Used to store data
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Jupyter Lab - version 3.4.4 - Used to create and share documents that contain live code, equations, visualizations and narrative text.
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pandas - For data analysis.
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pathlib - version 1.0.1 - This was used to locate through the directory or file path.
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scikit-learn - version 1.2 - Tools for data analysis
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imbalanced-learn - version 0.10.1 - Tools for data analysis
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NumPy - version 1.24.0- Provides tools when dealing with classification with imbalanced classes
After cloning the repository locally, you'll need to have the packages listed in Installation installed on your machine. To do so, you'll need to activate your conda dev environment and running the following commands:
```
pip install pandas
pip install hvplot
pip install jupyterlab
pip install scikit-learn
pip install imbalanced-learn
pip install numpy
```
After all of these are installed, please refer to the Deployment section for instructions on how to view or edit the notebook.
MIT License
Copyright (c) 2022 Keenan Heller
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- There are currently no tests associated with this project.
- There is currently no live deployment of this notebook on a common server, but the user has the ability to run this notebook locally on their machine via:
Jupyter Lab: Navigate to root of the directory and type "jupyter lab credit_risk_resampling.ipynb".