/Spam-Mail-Detection-Using-Python

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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!