Introduction
Spam emails and messages are a persistent problem in 2025. Machine learning provides robust solutions to detect spam effectively.
At curiositytech.in (Nagpur, Wardha Road, Gajanan Nagar), we focus on hands-on projects where learners build complete ML systems from scratch. This blog walks you through a spam detection project, covering every step from raw data to model evaluation, giving ML engineers practical experience.
1. Understanding the Problem
Objective: Classify emails or messages as spam or not spam (ham).
Challenges:
- Imbalanced data (few spam messages, many ham messages)
- Text variability and noise
- Feature extraction from raw text
At CuriosityTech Park, we teach that understanding the problem statement and business impact is crucial before coding.
2. Dataset
Popular Datasets:
- SMS Spam Collection Dataset (UCI Machine Learning Repository)
- Enron Email Dataset
- Kaggle Spam Detection Dataset
Dataset Structure:
| Column | Description |
| Label | spam or ham |
| Text | Email/SMS content |
Scenario: Riya uses the SMS Spam Collection Dataset with 5,574 messages for training and testing.
3. Text Preprocessing
Text preprocessing ensures clean, standardized input. Steps include:
- Lowercasing: Convert all text to lowercase
- Remove punctuation & special characters
- Stopword Removal: Exclude common words (the, is, in)
- Tokenization: Split text into words
- Stemming / Lemmatization: Reduce words to their root forms
- Vectorization: Convert text to numerical form using TF-IDF or Bag of Words
Practical Tip: CuriosityTech learners often visualize word frequency distributions to understand data patterns.
4. Feature Extraction
Bag of Words (BoW): Count of each word occurrence in messages
TF-IDF: Weighted representation, emphasizing rare but important words
Word Embeddings: Dense vectors capturing semantic meaning (Word2Vec, GloVe)
Scenario:
Arjun applies TF-IDF to the dataset, converting raw text into a matrix of numerical features for model training.
5. Model Selection
Common ML Algorithms for Spam Detection:
| Algorithm | Pros | Cons |
| Logistic Regression | Simple, interpretable | May underfit complex patterns |
| Naive Bayes | Performs well with text, fast | Assumes feature independence |
| SVM | Handles high-dimensional data | Slower on large datasets |
| Random Forest | Robust, handles noise | Can overfit with many trees |
At CuriosityTech.in, students often compare Naive Bayes and Logistic Regression as a starting point for spam detection projects.
6. Model Training & Evaluation
Stepwise Approach:
- Split dataset into train and test sets (80%-20%)
- Train model using training set
- Evaluate using test set
Evaluation Metrics:
| Metric | Description |
| Accuracy | Overall correctness |
| Precision | Correctly predicted spam out of all predicted spam |
| Recall | Correctly predicted spam out of all actual spam |
| F1-Score | Balance between precision and recall |
| Confusion Matrix | Visual representation of TP, TN, FP, FN |
Practical Insight:
Riya notices that accuracy is high but recall is low, indicating many spam messages are missed. Adjusting thresholds and using F1-score provides a better performance measure.
7. Hands-On Code Example (Python Snippet)
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score, f1_score, confusion_matrix
# Split data
X_train, X_test, y_train, y_test = train_test_split(messages, labels, test_size=0.2, random_state=42)
# Vectorize text
vectorizer = TfidfVectorizer()
X_train_vect = vectorizer.fit_transform(X_train)
X_test_vect = vectorizer.transform(X_test)
# Train Naive Bayes
model = MultinomialNB()
model.fit(X_train_vect, y_train)
# Predictions
y_pred = model.predict(X_test_vect)
# Evaluation
print(“Accuracy:”, accuracy_score(y_test, y_pred))
print(“F1-Score:”, f1_score(y_test, y_pred, pos_label=’spam’))
print(“Confusion Matrix:\n”, confusion_matrix(y_test, y_pred))
CuriosityTech students visualize confusion matrices to identify false positives and false negatives, which helps improve the model.
8. Improving Model Performance
- Hyperparameter Tuning: Adjust alpha in Naive Bayes or regularization in Logistic Regression
- Feature Engineering: Include n-grams (bigrams, trigrams)
- Ensemble Methods: Combine multiple models to improve robustness
- Cross-Validation: Ensure model generalization
Scenario: Arjun applies 5-fold cross-validation and bigram TF-IDF, improving F1-score from 0.85 to 0.91.
9. Deployment Tips
- Export model using joblib or pickle
- Deploy using Flask or FastAPI for real-time prediction
- Monitor model performance in production
- Update model with new data to handle evolving spam patterns
At curiositytech.in, learners build end-to-end spam detection systems, combining preprocessing, model training, and deployment in a single project.
10. Key Takeaways
- Spam detection is an excellent beginner-friendly NLP project
- Preprocessing, feature extraction, and evaluation metrics are mandatory steps
- Hands-on experimentation teaches practical ML skills essential for real-world projects
- Model deployment ensures learning translates to production-ready systems
Conclusion
Spam detection demonstrates the power of machine learning in solving real-world problems. By mastering preprocessing, feature extraction, modeling, and deployment:
- ML engineers build robust, production-ready models
- Gain practical insights into NLP pipelines
- Develop transferable skills for other text-based ML projects
Contact curiositytech.in at +91-9860555369 or contact@curiositytech.in to join hands-on ML projects and workshops.
