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https://github.com/samjoesilvano/password_strength_prediction_using_nlp

Developed a predictive model to categorize passwords as Strong, Good, or Weak, enhancing security and reducing breach risks. The project involves cleaning and analyzing data from an SQL database, using the TF-IDF technique for transformation, and implementing a Logistic Regression model to achieve accurate classifications.
https://github.com/samjoesilvano/password_strength_prediction_using_nlp

data-analysis data-classification data-cleaning data-visualization logistic-regression machine-learning natural-language-processing pandas password-security password-strength python scikit-learn sql tf-idf

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Developed a predictive model to categorize passwords as Strong, Good, or Weak, enhancing security and reducing breach risks. The project involves cleaning and analyzing data from an SQL database, using the TF-IDF technique for transformation, and implementing a Logistic Regression model to achieve accurate classifications.

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README 

        
# Password Strength Classification using Natural Language Processing



**A Predictive Model for Classifying Passwords into Strong, Good, or Weak Categories to Enhance Password Security.**



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## Table of Contents



- [Project Overview](#project-overview)

- [Features](#features)

- [Tech Stack](#tech-stack)

- [Data Pipeline](#data-pipeline)

- [Modeling](#modeling)

- [Evaluation Metrics](#evaluation-metrics)

- [Setup Instructions](#setup-instructions)

- [Usage](#usage)

- [Future Enhancements](#future-enhancements)

- [Contributing](#contributing)

- [License](#license)



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## Project Overview



The **Password Strength Classification** project aims to enhance password security by developing a predictive model that classifies passwords into three categories: **Strong**, **Good**, and **Weak**. This classification helps users understand the strength of their passwords and mitigates the risk of breaches by encouraging the use of stronger passwords.



The key goals of this project are:

- To provide an intuitive model for classifying passwords based on strength.

- To analyze and clean data from an SQL database for high-quality input.

- To apply advanced natural language processing techniques to enhance prediction accuracy.



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## Features



- **Password Classification**: Classifies passwords into Strong, Good, or Weak categories based on learned features.

- **Data Visualization**: Provides insights into password strength distribution and common characteristics of each category.

- **User-Friendly Interface**: Allows users to input passwords and receive immediate feedback on strength classification.

- **Robust Data Analysis**: Uses thorough data cleaning and transformation techniques to ensure accurate model training.



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## Tech Stack



- **Programming Language**: Python

- **NLP Libraries**: Scikit-learn, NLTK, Pandas

- **Data Processing**: NumPy, SQLAlchemy

- **Machine Learning**: Logistic Regression, TF-IDF (Term Frequency-Inverse Document Frequency)

- **Visualization**: Matplotlib, Seaborn

- **Deployment**: Flask (optional for web app), Jupyter Notebooks (for development)



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## Data Pipeline



1. **Data Collection**: Password data is collected from an SQL database, containing a diverse set of password samples.

  

2. **Data Cleaning**:

   - Removed duplicates and irrelevant entries to ensure data quality.

   - Handled missing values by imputing or removing incomplete records.



3. **Data Transformation**:

   - Utilized the **TF-IDF** technique to convert password strings into numerical vectors for model training.

   - Engineered additional features such as password length, character variety (uppercase, lowercase, numbers, symbols), and common patterns.



4. **Exploratory Data Analysis (EDA)**:

   - Visualized the distribution of password strengths and analyzed common characteristics of Strong, Good, and Weak passwords.

   - Identified patterns that contribute to password strength.



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## Modeling



### 1. **Logistic Regression Model**

   - Chose Logistic Regression for its effectiveness in binary classification problems and its interpretability.

   - Trained the model using the TF-IDF transformed features and the corresponding strength labels.



### 2. **Model Training**:

   - Split the dataset into training and testing sets to validate model performance.

   - Performed hyperparameter tuning to optimize the model’s performance.



### 3. **Model Validation**:

   - Evaluated the model using cross-validation techniques to ensure generalization.

   - Assessed performance metrics on the test set to confirm model reliability.



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## Evaluation Metrics



The model is evaluated using the following metrics to ensure accurate password classification:



- **Accuracy**: Measures the overall correctness of the model's predictions.

- **Precision**: The ratio of true positive predictions to the total predicted positives, indicating the model's ability to identify Strong passwords.

- **Recall**: The ratio of true positive predictions to the total actual positives, reflecting the model's ability to capture all Strong passwords.

- **F1 Score**: The harmonic mean of precision and recall, providing a balanced measure of performance.



The Logistic Regression model achieved a high accuracy score, demonstrating its effectiveness in classifying password strengths accurately.



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## Setup Instructions



### Prerequisites



- Python 3.7+

- Required libraries: Pandas, NumPy, Scikit-learn, NLTK, SQLAlchemy, Matplotlib, Seaborn



### Installation



1. Clone the repository:

   git clone https://github.com/SamJoeSilvano/Password_Strength_Prediction_using_NLP.git



2. Navigate to the project directory:

   cd password-strength-classification



3. Install the dependencies:

   pip install -r requirements.txt



4. Run the Jupyter Notebook or Flask app (optional):

   jupyter notebook



   or 



   python app.py



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## Usage



1. **Load Data**: Import password data from the SQL database.

2. **Visualize Trends**: Generate visualizations to understand password strength distribution.

3. **Train Model**: The Logistic Regression model is trained on the processed password data.

4. **Classify Passwords**: Input passwords into the model to receive classification as Strong, Good, or Weak.

5. **Evaluate Model**: Analyze performance metrics to ensure classification accuracy.



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## Future Enhancements



- **Advanced NLP Techniques**: Explore advanced models such as recurrent neural networks (RNNs) or transformers for improved classification.

- **User Interface**: Develop a more robust web application to allow users to test and visualize password strength interactively.

- **Real-time Feedback**: Implement real-time password strength feedback as users create passwords.

- **Broader Dataset**: Incorporate a wider range of password samples to enhance model robustness.



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## Contributing



Contributions are welcome! Here’s how you can help:



1. Fork the project.

2. Create a new feature branch (`git checkout -b feature-branch`).

3. Commit your changes (`git commit -m 'Add new feature'`).

4. Push to the branch (`git push origin feature-branch`).

5. Open a pull request.



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## License



This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.



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## Acknowledgements



- Thanks to the open-source community for their invaluable libraries and resources that made this project possible.

- Special recognition to the researchers and developers focused on enhancing password security.

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