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Building a Password Strength Classifier Model Using Machine Learning

January 26, 2022

Password strength is used to measure how effective a password is against external attacks. External attacks are in form of password cracking or brute-force attacks. They aim to gain unauthorized access to a computer system or network.

The strength of a password is usually measured using its complexity, length, and unpredictability.

Cyber crimes and data breaches are on the rise, and the main cause for this is usually compromised passwords.

In this tutorial, we will build a password classification model using Scikit-Learn. The model will give the strength of a password from the lowest strength labeled 0 to the highest strength labeled 2.

Table of contents


To understand the concepts explained in this tutorial easily, you should:

Passwords dataset

The dataset used was collected from different websites through web scraping. The dataset contains several passwords with different strengths. We will use this dataset to train our model.

A glimpse of the dataset is shown below:

Passwords dataset

To download the password dataset, click this link. After downloading the passwords dataset, load the dataset into the Google Colab notebook.

Loading dataset into Google Colab

For us to load the dataset, we need the Pandas Python library. Let’s import pandas.

import pandas as pd

To load the dataset using Pandas, run this code:

pswd_data = pd.read_csv("/content/data.csv",error_bad_lines=False)

In the code above, we have added the error_bad_lines=False command so that pandas reads only the complete data samples from the datasets. Adding this command our dataset will be loaded with no errors.

To know if our dataset is successfully loaded, run this code:


This code will output the structure of the loaded dataset with all the columns. The output is shown below:

Loaded dataset

From the image above, our dataset has password and strength columns. The password column holds the password text and the strength columns contain the password strengths.

To check all the password strength values, run this code:


The output is shown below:

array([1, 2, 0])

From the output above, our password strength has three values. 0 represents the weakest passwords, 1 for medium passwords, and 2 for strongest passwords.

This dataset may have missing values, let’s now check for missing values in our dataset.

Checking missing values

Missing values make the dataset incomplete and this gives inaccurate results. To check for missing values, use this code:


The output is shown below:

Missing values

Removing missing values

From the image above, our dataset has one missing value. Let’s remove the missing value using the following code:


To check if the missing value is removed, run this code:


The output is as shown below:

Removing missing values

After removing the missing values, we will convert our dataset into an array.

Convert dataset into an array

An array is much easier to work with. We will therefore use the NumPy Python library. To import NumPy, use this code:

pswd = np.array(pswd_data)

To see the array, run this code:


The output is shown below:

Removing missing values

The next step is to shuffle our dataset randomly. Shuffling the dataset will make the dataset more robust. This will prevent the model from memorizing the dataset and ensure the model learns from the dataset.

Dataset shuffling

When using a shuffled dataset, the model will understand patterns and relationships within our dataset.

To shuffle our dataset, we will import the random Python package.

import random

We can now randomly shuffle the dataset using the following code:


Adding features and labels

In machine learning, features are all the unique independent variables in our dataset that are used as the model’s input. Here, our features are located in the password column. The password column holds all the text passwords that will train our model.

Labels are variables in our dataset that are used as output for the model. Here, our labels are located in the strength column. The strength column has three values, 0, 1, and 2.

We will add the features and labels using the following code:

ylabels  = [s[1] for s in pswd]
allpasswords = [s[0] for s in pswd]

In the code above, we save our labels in the ylabels variable. The code loops through the columns and selects the last column(1) as the labels. We also save our features in the allpasswords column. The code loops through the columns and selects the column(0) as the features.

Run the following code to check the number of passwords and labels in our dataset:

Number of labels:


The output is shown below:


Number of passwords:


The output:


From the output above, the dataset is big and has many data samples. We can now use the dataset to build our model.

Tokenization process

Tokenization is the process of breaking text data into simpler characters called tokens. We will break our password text into word tokens which we will use as input for our model.

To perform this process, we will create a custom function. The function createTokens will loop through our dataset and return the converted tokens.

def createTokens(f):
    tokens = []
 for i in f:
 return tokens

Converting word tokens into numerical data

Machine learning models do not comprehend text. We therefore need to further convert the word tokens to numeric data.

We will use the TfidfVectorizer package to convert the word tokens into numeric data (vectors of numbers). Using the TfidfVectorizer, it will convert based on the frequency of occurrence of each word token in the dataset.

For further understanding of how TfidfVectorizer works in details, click here.

We will import this package using the following code:

from sklearn.feature_extraction.text import TfidfVectorizer

Lets initialize TfidfVectorizer:

Initialiazing TfidfVectorizer

vectorizer = TfidfVectorizer(tokenizer=createTokens)

In the code above we have initialized TfidfVectorizer and also passed createTokens function as an argument. We will then fit vectorizer into our dataset using the fit_transform method to convert it into numeric data.

X = vectorizer.fit_transform(allpasswords)

Our input data (features) is saved in the allpasswords variable.

Splitting vectorized dataset

Splitting datasets creates two sets; one will be used for training the model and the other for testing. To split the dataset, we will use train_test_split.

from sklearn.model_selection import train_test_split

The splitting function will be as follows:

X_train, X_test, y_train, y_test = train_test_split(X, ylabels, test_size=0.2, random_state=42)

We have used a test_size=0.2 so that 80% of the data is used for training and 20% for testing.

Let’s now build the model!

Building the model

We will build our model using the DecisionTreeClassifier algorithm. This algorithm is best suited for classification problems. It produces a model with a very accurate score:

from sklearn.tree import DecisionTreeClassifier

To use the algorithm, we will initialize it using the following code:


Finally, we fit the algorithm onto the train set dataset. This will ensure that the model fully learns from the dataset. Over time, the model will understand patterns and relationships within our dataset:, y_train)

Let’s calculate the accuracy score of this trained model.

Accuracy score using the training dataset

To get the accuracy score, use this code:

print("Accuracy :",logit.score(X_test, y_test))

The accuracy score is shown below:

Accuracy : 0.9998114655103219

This is 99.98114%. This being the first training; it is a good accuracy score. We will test this model using the test dataset.

Model testing

We will use the data as input for the trained model so that it can classify the data points in this dataset.


It will produce the following output:

array([0, 0, 1, ..., 1, 1, 0])

Accuracy score using the testing dataset

In this section, we will be checking the accuracy score using the testing dataset so we get to know if our model is over-fitted. Over-fitting occurs when the model performs very well using the training dataset but poorly using the testing dataset.

Use this code:

print("Accuracy :",clf.score(X_test, y_test))

The output is as shown below:

Accuracy : 0.9738516217669195

This is 97.385. It is still a good accuracy score if we compare it with the one obtained using the training dataset. Therefore, our model is not over-fitted and can be used to make single predictions.

For more information on model over-fitting, click here.

Single prediction

In a single prediction, we input sample passwords to our model for it to make predictions. We will use the following input passwords:

X_predict = ['drshsyqb*',

To make these predictions, run the following code:

X_predict = vectorizer.transform(X_predict)
y_Predict = clf.predict(X_predict)

From the code above, the vectorizer.transform method will convert the password text into numeric data. clf.predict method will perform the prediction. print will print the prediction results, as shown below:

[1 2 1 2 1 0 0 2]

From the output above, the model was able to classify the password strengths. 0 represents the weakest passwords, 1 for medium passwords, and 2 for strongest passwords.


In this tutorial, we have learned how to build a password strength classifier model using machine learning. We started by performing data pre-processing to correctly format our dataset. We then used this dataset to train our model.

After training the model, it was able to make accurate predictions. This model can further be deployed and used as a real application.

This tutorial has used guided steps, the Python code used in this tutorial can be found here.


I hope you find this article helpful.

Happy coding!

Peer Review Contributions by: Monica Masae