https://github.com/bibeklakra91/spam-filter

https://github.com/bibeklakra91/spam-filter

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• Host: GitHub
• URL: https://github.com/bibeklakra91/spam-filter
• Owner: BibekLakra91
• Created: 2022-08-24T08:50:09.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-01-28T15:18:39.000Z (10 months ago)
• Last Synced: 2024-01-28T16:29:38.326Z (10 months ago)
• Language: Jupyter Notebook
• Size: 515 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Machine Learning Model for Spam Email Classification

## Overview

This script implements a machine learning model for spam email classification using the Linear Support Vector Classification (LinearSVC) algorithm. The task involves distinguishing between 'spam' and 'ham' (non-spam) messages based on textual content.

## Usage

1. **Data Loading:**

   - The script assumes the presence of a CSV file named 'mail_data.csv' containing the relevant data for training and testing.

   - Ensure that the file is located in the correct path or provide the correct path if needed.

2. **Data Preprocessing:**

   - Missing values in the dataset are handled by replacing them with empty strings.

   - The 'Category' column is converted to numerical values: 0 for 'spam' and 1 for 'ham'.

3. **Train-Test Split:**

   - The data is split into training and testing sets using an 80-20 split.

   ```python

   X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=3)

   ```

4. **Text Vectorization:**

   - TF-IDF vectorization is applied to convert text data into a numerical format.

   ```python

   feature_extraction = TfidfVectorizer(min_df=1, stop_words='english', lowercase='True')

   X_train_features = feature_extraction.fit_transform(X_train)

   X_test_features = feature_extraction.transform(X_test)

   ```

5. **Model Training:**

   - The Linear Support Vector Classification (SVC) model is initialized with hinge loss and trained on the training set.

   ```python

   clf = LinearSVC(loss="hinge")

   clf.fit(X_train_features, Y_train)

   ```

6. **Prediction and Accuracy:**

   - The model predicts labels on the test set, and accuracy is calculated.

   ```python

   clf_prediction_on_test_data = clf.predict(X_test_features)

   clf_accuracy_on_test_data = accuracy_score(Y_test, clf_prediction_on_test_data)

   ```

7. **Results:**

   - The achieved accuracy on the test data is 98.2%.

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