Spam Message Classifier

Overview

The Spam Message Classifier is a machine learning project aimed at classifying text messages as "spam" or "non-spam" using logistic regression. This project demonstrates a ground-up approach to machine learning classification, from data preprocessing to model evaluation. The classification is carried out using the logistic regression algorithm, and various Python libraries are utilized to efficiently handle data, preprocess it, and perform vector operations.

Key technologies used in this project include:

• NumPy for fast and efficient vectorized operations
• Pandas for data manipulation and preprocessing
• SciKit-Learn for machine learning tasks, including model training and evaluation
• TfidfVectorizer for transforming text data into feature vectors suitable for machine learning

Dataset

The primary dataset used in this project is the SMS Spam Collection dataset, which consists of SMS messages classified as either "spam" or "ham" (non-spam). The dataset is publicly available and can be accessed from the following link:

SMS Spam Collection Dataset

The dataset contains:

• Messages: Text data in the form of SMS messages
• Labels: A binary classification label indicating whether a message is spam (1) or non-spam (0)

Steps Involved

1. Data Collection:

   • Load the SMS Spam Collection dataset into a Pandas DataFrame.

2. Data Preprocessing:

   • Clean the text data by removing special characters, stop words, and performing tokenization.
   • Split the data into training and testing sets.

3. Feature Extraction:

   • Use TfidfVectorizer to convert text messages into numerical features that can be used by the logistic regression model.

4. Model Training:

   • Apply logistic regression using SciKit-Learn to classify the messages.

5. Evaluation:

   • Evaluate the performance of the model using common metrics such as accuracy, precision, recall, and F1 score.

6. Model Optimization:

   • Optionally, perform hyperparameter tuning to improve the performance of the logistic regression model.

Installation

To run the Spam Message Classifier project, you will need the following Python libraries:

• NumPy: For numerical operations
• Pandas: For data manipulation
• SciKit-Learn: For machine learning tasks
• Matplotlib (optional, for visualizations)

You can install the required dependencies using pip:

pip install numpy pandas scikit-learn matplotlib