The NLP Password Strength Predictor is a machine learning project aimed at evaluating and predicting the strength of passwords using Natural Language Processing (NLP) techniques. The project leverages advanced NLP methods to analyze passwords and classify their strength into predefined categories such as weak, medium, and strong.
Data Preprocessing: Clean and preprocess password data, including handling missing values and normalizing text. Feature Extraction: Utilize techniques like TF-IDF to transform password text data into meaningful features. Model Training: Train machine learning models, including RandomForestClassifier, to predict password strength. Evaluation: Assess model performance using metrics like accuracy, precision, recall, and F1-score, as well as confusion matrices. Technologies Used Python: Primary programming language used for development. Pandas: Data manipulation and analysis. NumPy: Numerical computing. Scikit-learn: Machine learning library for model building and evaluation. Matplotlib & Seaborn: Data visualization.
Source: Custom-made in sql.
This project is a Password Strength Predictor API built using BentoML and a pre-trained Logistic Regression model. The API takes a password as input and classifies its strength as either "weak", "medium strength", or "strong".
- service.py: Contains the BentoML service definition and the classify function.
- log_model.pkl: Pickle file containing the pre-trained Logistic Regression model.
- vectorizor.pkl: Pickle file containing the pre-trained vectorizer (CountVectorizer).
- requirements.txt: Lists the dependencies required for the project.
git clone https://github.com/yourusername/Password_Strength_Predictor-NLP-.git cd Password_Strength_Predictor-NLP-
Create and Activate a Virtual Environment
conda create --name password_strength_predictor python=3.12 conda activate password_strength_predictor Install Dependencies Ensure you have all the necessary dependencies installed. Create a requirements.txt file if you haven't already and add the required packages.
bentoml numpy scikit-learn
pip install -r requirements.txt Download the Pre-trained Models Ensure that log_model.pkl and vectorizor.pkl are placed in the root directory of your project.
Running the API
bentoml serve service.py:svc --reload --port 3001 Note: You can change the port if 3001 is already in use.
Make API Requests
curl -X POST "http://127.0.0.1:3001/classify" -H "Content-Type: application/json" -d '{"password": "your_password_here"}'
Using Postman
- Open Postman and create a new POST request.
- Set the URL to http://127.0.0.1:3001/classify.
- Go to the Headers tab and set Content-Type to application/json.
- Go to the Body tab, select raw and choose JSON from the dropdown.
- Enter the following JSON payload:
{ "password": "your_password_here" }
Click Send. Using Python requests
import requests
url = "http://127.0.0.1:3001/classify" payload = { "password": "your_password_here" } headers = { "Content-Type": "application/json" }
response = requests.post(url, json=payload, headers=headers) print(response.json())
The API will return a JSON response indicating the strength of the password:
{ "strength": "weak password" } or
{ "strength": "medium strength password" } or
{ "strength": "strong password" }
Port Issues: If the specified port is already in use, you can change the port by modifying the bentoml serve command with a different port number using the --port flag. Model Loading Errors: Ensure that the log_model.pkl and vectorizor.pkl files are present in the root directory and are correctly saved.
This Password Strength Predictor API allows you to evaluate the strength of passwords using a pre-trained machine learning model. By following the setup instructions and running the API, you can easily integrate this functionality into your applications. If you encounter any issues or have questions, please refer to the logs for detailed error messages and troubleshooting information.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent