Spam Email Detection using Machine Learning

Prosesu Jerál Deteksaun Spam

Email to Pc...

1. Data Collection (Koleta Dadus)

Kolesaun dataset email ne'ebé fó ona label: Spam no Ham (Laos Spam).

2. Text Preprocessing (Pre-prosesamentu)

Muda letra ba ki'ik (Case folding), hasai pontuasaun, fahe liafuan (Tokenization), hasai liafuan jerál (Stopword), no buka hun liafuan (Stemming).

Ezemplu: "Ita MANÁN!" → "ita manan"

3. Feature Extraction (Estraun Karakterístika)

Muda testu ba númeru uza téknika: Bag of Words ka TF-IDF.

4. Model Training (Treinamentu)

Algoritmu: Naive Bayes, Logistic Regression, SVM, Random Forest, no Neural Network.

5. Prediction (Prediksaun)

Email foun tama → Prosesu → Prediksaun: Spam ka Ham (Ezemplu: Spam 95%).

Ezemplu Deteksaun

“Klik iha ne'e atu manán $1000 agora!!!”

Modelu rekonhese liafuan: klik, manan, $, agora. Iha posibilidade boot atu klasifika nu'udar Spam.

Konkluzaun

Deteksaun Email Spam mak sistema automátiku hodi klasifika email. Métodu modernu uza Machine Learning atu aprende padraun spam ho loloos no akuradu. Sistema ne'e garante seguransa no konfortu ba utilizadór sira iha mundu dijitál.

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Aprende oinsá algoritmu analiza liafuan hodi detekta email spam.

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Prosesu Jerál Deteksaun Spam

1. Data Collection (Koleta Dadus)

2. Text Preprocessing (Pre-prosesamentu)

3. Feature Extraction (Estraun Karakterístika)

4. Model Training (Treinamentu)

5. Prediction (Prediksaun)

Ezemplu Deteksaun

Konkluzaun

Prontu atu koko simulasaun?

Computer Science

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Spam Detection

Prosesu Jerál Deteksaun Spam

1. Data Collection (Koleta Dadus)

2. Text Preprocessing (Pre-prosesamentu)

3. Feature Extraction (Estraun Karakterístika)

4. Model Training (Treinamentu)

5. Prediction (Prediksaun)

Ezemplu Deteksaun

Konkluzaun

Prontu atu koko simulasaun?

Computer Science

Useful Links

Address

Mail Us

Telephone