Spam Filtering NLP Project

Project Overview:

The goal of this project is to develop a robust spam filtering system using Natural Language Processing techniques. Spam filtering is a critical application in various domains, including email systems and messaging platforms. The project will focus on building a machine learning model capable of distinguishing between spam and non-spam messages by analyzing the text content.

Libraries Used

This project utilizes the following Python libraries:

NLTK (Natural Language Toolkit): A powerful library for working with human language data.

Make sure to download the necessary NLTK resources using the following commands:
```
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('averaged_perceptron_tagger')
nltk.download('tagsets')
```

Key Components:

Data Collection:

Gather a dataset containing labeled examples of spam and non-spam messages. Common sources include spam email datasets or SMS spam datasets.

Data Preprocessing:

Clean and preprocess the text data. This may involve tasks such as removing punctuation, converting text to lowercase, handling stopwords, and tokenization.

Feature Extraction:

Utilize vectorization techniques (e.g., CountVectorizer, TF-IDF) to convert text data into a numerical format suitable for machine learning models.

Model Selection:

Choose and implement a suitable machine learning algorithm for classification. Common choices include Naive Bayes, Support Vector Machines (SVM), or more advanced models like ensemble methods or neural networks.

Model Training:

Split the dataset into training and testing sets. Train the chosen model on the training set and evaluate its performance on the testing set.

Evaluation Metrics:

Assess the model's performance using metrics such as accuracy, precision, recall, and F1 score. Evaluate its ability to correctly classify spam and non-spam messages.

Shahad-Mohammed/NLP_Course_Spam_Filtering