Spam Message Classification

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Overview

This project is aimed at building a spam message classifier using machine learning. The goal is to classify messages as either "ham" (non-spam) or "spam" (unwanted messages). The classification model is built using various machine learning algorithms and natural language processing techniques.

Table of Contents

1. Project Description
2. Technologies Used
3. Dataset
4. Steps Involved
5. Model Evaluation
6. How to Run

Project Description

This project is a spam message classification system. Using a dataset of labeled messages, we build a machine learning model to identify whether a message is spam or ham. Various preprocessing techniques like tokenization, stopword removal, and stemming are applied to prepare the text for machine learning models.

Technologies Used

• Python: Programming language used for this project.
• Pandas: For data manipulation and analysis.
• Numpy: For numerical computations.
• Sklearn: For machine learning models and preprocessing.
• Matplotlib & Seaborn: For data visualization.
• NLTK: For natural language processing tasks like tokenization and stopword removal.
• WordCloud: To generate word clouds for data visualization.

Dataset

The dataset used in this project is called spam.csv, which contains text messages and their respective labels (spam or ham).

The dataset has the following columns:

• target: 1 for spam, 0 for ham.
• text: The content of the message.

Dataset Sample:

Here is a preview of a few rows from the dataset:

Label	Message
ham	Go until jurong point, crazy.. Available only in bugis n great world la e buffet...
ham	Ok lar... Joking wif u oni...
spam	Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121...
ham	U dun say so early hor... U c already then say...
ham	Nah I don't think he goes to usf, he lives around here though
spam	FreeMsg Hey there darling it's been 3 week's now and no word back! I'd like some fun...
ham	Even my brother is not like to speak with me. They treat me like aids patent.

In this dataset:

• ham: Non-spam messages (e.g., social messages, personal conversations).
• spam: Spam messages (e.g., promotions, scams, unwanted advertisements).

Steps Involved

1. Data Cleaning

• Removing irrelevant columns and renaming necessary columns for clarity.

2. Exploratory Data Analysis (EDA)

• Analyzing the distribution of spam and ham messages.
• Adding features like the number of characters, words, and sentences in each message.

3. Data Preprocessing

• Converting text to lowercase.
• Tokenizing the text and removing special characters, stopwords, and punctuation.
• Stemming words to their root form.

4. Model Building

• Using multiple machine learning algorithms like Naive Bayes, Logistic Regression, SVM, and Random Forest to train and evaluate the model.

5. Model Evaluation

• The models are evaluated using accuracy, precision, and confusion matrix to assess the performance.

Model Evaluation

The model is evaluated based on:

• Accuracy: Percentage of correct predictions.
• Precision: Percentage of relevant results among the retrieved instances.

Here are some evaluation results:

Model	Accuracy	Precision
MultinomialNB	97.2%	100%
BernoulliNB	98.3%	99.2%
SVC	86.6%	0% (Precision issue with SVC)

How to Run

1. Clone this repository to your local machine.
2. Install the required libraries:
3. Run the Jupyter notebook (sms-spam-detection.ipynb) to see the results and model evaluation.

Acknowledgments

Thank you for exploring this project! If you found this repository helpful or interesting, please consider giving it a ⭐. Contributions, suggestions, and improvements are always welcome. Feel free to open an issue or submit a pull request.