Exp 12 — Spam detection with TF-IDF

3) Dataset

• Source: Use SMS Spam Collection dataset (UCI / Kaggle) and cite it.
• Features/Labels: [Describe X and y; mention classes if classification]
• Split: [Train/Validation/Test or K-fold]
• Preprocessing: [Scaling/Normalization, resizing, tokenization, etc.]

4) Procedure / Steps

1. Load dataset and perform preprocessing.
2. Define model architecture and justify key choices.
3. Compile model (loss + optimizer + metrics).
4. Train with validation and log curves.
5. Evaluate on test set and compute required metrics.
6. Summarize observations and limitations.

5) Code (Skeleton)

Paste your complete runnable code below (or attach notebook link in the final submission).

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
from sklearn.metrics import classification_report, confusion_matrix

# TODO: load spam dataset
# df = pd.read_csv(...)
# X = df['text']
# y = df['label']

vec = TfidfVectorizer(max_features=20000, ngram_range=(1,2))
Xv = vec.fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(Xv, y, test_size=0.2, random_state=42, stratify=y)

clf = LinearSVC()
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
print(classification_report(y_test, pred))

6) Results / Output

• Metrics: [Write your final values: accuracy/F1 or MAE/MSE]
• Plots: [Attach loss/metric curves; prediction vs actual plots if forecasting]
• Screenshots: [Paste screenshots of outputs, confusion matrix, sample predictions]

7) Observations / Discussion

• [Observation 1: what changed when you tuned epochs/batch size?]
• [Observation 2: evidence of overfitting/underfitting?]
• [Observation 3: what improved performance (augmentation, regularization, fine-tuning)?]

8) Conclusion

Write 3–6 lines summarizing what you implemented, key result, and what you learned.

9) Viva Questions

1. Why is precision important for spam detection?
2. What is a confusion matrix?
3. What is the difference between training and validation data?
4. Explain overfitting and two ways to reduce it.
5. Why do we normalize/scale inputs?
6. What does batch size and epoch mean?
7. How do you choose a loss function for a task?

10) Post-lab Assignment

• Try LinearSVC vs LogisticRegression.
• Tune n-grams and max_features and compare.