This project focuses on predicting password strength using various machine learning techniques and data analysis. It involves analyzing a dataset of passwords and their corresponding attributes to evaluate their strength and estimate the time required to crack them.
In this project, there are explored the relationships between password attributes, including strength, length, and entropy, to predict the time it would take to crack a password in offline. The dataset used for analysis can be found in the "dataset.csv" file.
Follow these steps to run the project:
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Clone or download the project repository to your local machine.
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Ensure you have Python 3.x installed on your system.
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Install the required Python packages by running the following command:
pip install numpy pandas matplotlib scikit-learn seaborn
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Run the project:
python3 project.py
Also, take a look at the report included in the project files for more detailed observations.
This project was a collaborative effort between myself and Inka Croell (https://github.com/c1nka). My responsibilities included sourcing the dataset, creating and plotting the Polynomial Regression model, and conducting the primary analysis of the results and conclusions, with a focus on the polynomial model.