This project focuses on classifying emails as spam or legitimate (ham) using logistic regression. It involves several steps, including data preprocessing, feature extraction, model training, and evaluation.
- The emails are preprocessed to remove stopwords, punctuation, and non-sense words using NLTK.
- Tokenization and lemmatization are performed to normalize the text data.
- The emails are converted into a features matrix using a count vectorizer.
- The count vectorizer converts the text data into numerical features representing the frequency of each token.
- The feature matrix is normalized using Min-Max scaling to ensure consistency across features.
- Logistic regression is applied to the training data to build a classification model.
- The trained model is evaluated using the test data, and a classification report is generated to assess its performance.
The model's performance on the test data is as follows:
| Class | Precision | Recall | F1-Score | Support |
|---|---|---|---|---|
| 0 | 0.85 | 0.99 | 0.91 | 558 |
| 1 | 0.91 | 0.40 | 0.55 | 168 |
| Accuracy | 0.85 | 726 | ||
| Macro Avg | 0.88 | 0.69 | 0.73 | 726 |
| Weighted Avg | 0.86 | 0.85 | 0.83 | 726 |
The classification report provides insights into the model's performance, including precision, recall, F1-score, and support for both spam and legitimate classes.
This project demonstrates an end-to-end process for spam classification using logistic regression. By preprocessing the data, extracting meaningful features, and training a classification model, it offers a systematic approach to identifying spam emails.