https://github.com/thatsabhishek/spam-mail-detection-using-python

https://github.com/thatsabhishek/spam-mail-detection-using-python

Last synced: 10 months ago
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• Host: GitHub
• URL: https://github.com/thatsabhishek/spam-mail-detection-using-python
• Owner: thatsabhishek
• Created: 2023-07-21T04:15:34.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-12-09T05:58:38.000Z (about 2 years ago)
• Last Synced: 2025-01-14T15:28:44.051Z (11 months ago)
• Language: Jupyter Notebook
• Size: 241 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Spam Mail Detection Using Python

## Overview

This project implements a simple spam mail detection system using Python and machine learning techniques. The code utilizes the scikit-learn library for machine learning tasks and pandas for data manipulation. The model is based on logistic regression and employs the TF-IDF (Term Frequency-Inverse Document Frequency) vectorization technique to convert text data into numerical features.

## Code Structure

The code is organized into the following sections:

1. **Data Loading and Preprocessing:**

   - The dataset is loaded using pandas from a CSV file (`mail_data.csv`).

   - Initial exploration of the dataset is performed using `head()`, `info()`, and `isnull().sum()` functions.

   - Null values in the dataset are replaced with empty strings.

   - A binary classification is created for the 'Category' column, where 'ham' is labeled as 1 (not spam) and others as 0 (spam).

   - Unnecessary columns like 'Category' are dropped.

2. **Data Splitting:**

   - The data is split into training and testing sets using `train_test_split` from scikit-learn.

3. **Text Vectorization:**

   - TF-IDF vectorization is applied to convert the text data into feature vectors. The `TfidfVectorizer` is configured to ignore English stop words and convert all text to lowercase.

4. **Model Training:**

   - A logistic regression model is employed for training using the transformed training data.

5. **Model Evaluation:**

   - The accuracy of the model is evaluated on both the training and testing datasets.

6. **Prediction:**

   - The model is used to predict the category of a new input mail. The input mail is first transformed using the same TF-IDF vectorizer, and the model predicts whether it is spam or not.

## How to Use

1. **Clone the Repository:**

   ```

   git clone https://github.com/your-username/Spam-Mail-Detection-Using-Python.git

   cd Spam-Mail-Detection-Using-Python

   ```

2. **Install Dependencies:**

   ```

   pip install pandas numpy scikit-learn

   ```

3. **Run the Code:**

   - Ensure that the `mail_data.csv` file is present in the same directory.

   ```

   python spam_mail_detection.py

   ```

4. **Modify Input:**

   - You can modify the `input_your_mail` variable with your own email content for testing.

5. **Review Results:**

   - The code will print the accuracy on both the training and testing datasets.

   - It will also predict whether the input mail is spam or ham.

## Notes

- The dataset used (`mail_data.csv`) should have a 'Category' column indicating whether the mail is 'ham' or 'spam'.

- Ensure that the necessary libraries are installed using the provided requirements file.

Feel free to customize and extend this project according to your needs!