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!