sufianadnan / ml-spam-email-filter

README for the Jupyter Notebook "project.ipynb"

Overview

The Jupyter Notebook "project.ipynb" contains a comprehensive analysis and implementation of a machine learning model for spam detection. The notebook is structured into various sections, each addressing specific aspects of the model development process, including data preprocessing, feature extraction, model training, and evaluation.

Contents

1. Introduction

   • Brief overview of the project's objective.
   • Discussion on the significance of spam detection.

2. Data Loading and Exploration

   • Importing necessary libraries.
   • Loading the dataset used for spam detection.
   • Initial exploration of the data, including checking the data format, sample entries, and distribution of spam vs. non-spam messages.

3. Data Preprocessing

   • Cleaning and preprocessing of text data.
   • Techniques include tokenization, removal of stopwords, and normalization.
   • Implementation of a custom function for text preprocessing.

4. Feature Extraction

   • Introduction to feature extraction techniques used in text analysis.
   • Implementation of Count Vectorization and TF-IDF Vectorization.
   • Transformation of text data into numerical format suitable for machine learning models.

5. Model Training

   • Splitting the dataset into training and testing sets.
   • Training a Naive Bayes classifier using both Count Vectorizer and TF-IDF Vectorizer features.
   • Discussion on the choice of Naive Bayes for text classification.

6. Model Evaluation

   • Evaluation of the model's performance using metrics such as accuracy, precision, recall, and F1-score.
   • Comparison of model performance with different feature extraction methods.

7. Model Testing with Custom Inputs

   • Testing the model with custom text inputs.
   • Observing the model's predictions on whether the input text is spam or not.

8. Saving the Model and Vectorizers

   • Code for saving the trained Naive Bayes models and the vectorizers using Pickle for later use.

Usage

To use this notebook:

1. Ensure all required libraries are installed.
2. Run each cell sequentially to understand the workflow.
3. Customize the dataset or parameters for specific needs.

Conclusion

This notebook provides an end-to-end solution for spam detection using machine learning. It is well-documented, making it suitable for educational purposes or as a starting point for similar text classification projects.

Notes

• Model performance might vary with different datasets or parameters.