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---
tags:
- phishing-detection
- logistic-regression
- tfidf
- sklearn
- datasets
- huggingface
license: mit
---
# Phishing Detection Model using Logistic Regression and TF-IDF
This model is a phishing detection classifier built using TF-IDF for feature extraction and Logistic Regression for classification. It processes text data to identify phishing attempts with high accuracy.
## Model Details
- **Framework**: Scikit-learn
- **Feature Extraction**: TF-IDF Vectorizer (top 5000 features)
- **Algorithm**: Logistic Regression
- **Dataset**: [ealvaradob/phishing-dataset](https://huggingface.co/datasets/ealvaradob/phishing-dataset) (combined_reduced subset)
## Installation
Before using the model, ensure you have the necessary dependencies installed:
```bash
pip install scikit-learn
pip install -U "tensorflow-text==2.13.*"
pip install "tf-models-official==2.13.*"
pip uninstall -y pyarrow datasets
pip install pyarrow datasets
```
## How to Use
Below is an example of how to train and evaluate the model:
```python
from datasets import load_dataset
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# Load the dataset
dataset_reduced = load_dataset("ealvaradob/phishing-dataset", "combined_reduced", trust_remote_code=True)
# Convert to pandas DataFrame
df = dataset_reduced['train'].to_pandas()
# Extract text and labels
text = df['text'].values
labels = df['label'].values
# Split the data into train and test sets
train_text, test_text, train_labels, test_labels = train_test_split(
text, labels, test_size=0.2, random_state=42
)
# Create and fit the TF-IDF vectorizer
vectorizer = TfidfVectorizer(max_features=5000)
vectorizer.fit(train_text)
# Transform the text data into numerical features
train_features = vectorizer.transform(train_text)
test_features = vectorizer.transform(test_text)
# Create and train the logistic regression model
model = LogisticRegression()
model.fit(train_features, train_labels)
# Make predictions on the test set
predictions = model.predict(test_features)
# Evaluate the model's accuracy
accuracy = accuracy_score(test_labels, predictions)
print(f'Accuracy: {accuracy}')
```
## Results
- **Accuracy**: The model achieves an accuracy of `{{accuracy}}` on the test set.
## Dataset
The dataset used for training and evaluation is the [ealvaradob/phishing-dataset](https://huggingface.co/datasets/ealvaradob/phishing-dataset). It contains a variety of phishing and non-phishing samples labeled as `1` (phishing) and `0` (non-phishing).
## Limitations and Future Work
- The model uses a simple Logistic Regression algorithm, which may not capture complex patterns in text as effectively as deep learning models.
- Future versions could incorporate advanced NLP techniques like BERT or transformer-based models.
## License
This project is licensed under the MIT License. Feel free to use, modify, and distribute this model as per the terms of the license.
## Acknowledgements
- [Hugging Face Datasets](https://huggingface.co/datasets)
- [Scikit-learn](https://scikit-learn.org/)
---
license: apache-2.0
---
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