📧 AI Project #2: Spam Email Detector with NLP

🎯 Project Goal
Build an AI app that reads any email text and tells you if it’s Spam or Ham in 1 second.

Spam = unwanted/promotional mail. Ham = legit mail like “Team meeting at 4 PM”.

🧠 Skills You’ll Learn

Python: Strings, Functions, Lists

Data: Pandas, NumPy for dataset handling

NLP: Text cleaning, Tokenization, Stopwords, Stemming, TF-IDF

ML: Naive Bayes, Logistic Regression, Random Forest

Deployment: Streamlit for web app

📂 Step 1: Dataset
Typical format: 2 columns
Email Text | Label

"Win ₹1 Lakh now, click here" | 1 → Spam
"Project report attached" | 0 → Ham

Label: 0 = Ham, 1 = Spam

📊 Step 2: Load & Explore Data

import pandas as pd
df = pd.read_csv("spam.csv")
print(df.head())
print(df['label'].value_counts())

Check: total emails, spam %, missing values.

🧹 Step 3: Text Preprocessing

Raw: "Congratulations!!! You WON ₹50,000... CLICK NOW!!!"

Clean: "congratulation won click"

Convert to lowercase

text = text.lower()

Remove punctuation

import string
text = text.translate(str.maketrans('', '', string.punctuation))

Step 4: Tokenization
Split sentence into words

"you won prize" → ["you", "won", "prize"]

from nltk.tokenize import word_tokenize
tokens = word_tokenize(text)

Step 5: Remove Stopwords

Remove common words:
the, is, a, an
["you", "won", "the", "prize"] → ["won", "prize"]

from nltk.corpus import stopwords
words = [w for w in tokens if w not in stopwords.words('english')]

Step 6: Stemming

Reduce words to root form

running, runs, ran → run
playing, played → play

from nltk.stem import PorterStemmer
ps = PorterStemmer()
words = [ps.stem(w) for w in words]

Step 7: Convert Text to Numbers with TF-IDF

ML models can’t read text. TF-IDF gives importance score to words.

Spam words like “win, free, offer” get high scores.

from sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer()
X = tfidf.fit_transform(df['text'])

Step 8: Train Model

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)

Other models to try: Multinomial Naive Bayes, Random Forest, XGBoost

Step 9: Predict New Email

sample = ["Congratulations you won free iPhone"]
sample_vec = tfidf.transform(sample)
pred = model.predict(sample_vec)
print("Spam" if pred[0]==1 else "Ham")

Step 10: Check Performance

from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix
print("Accuracy:", accuracy_score(y_test, y_pred))

Also check Precision, Recall, F1-Score, Confusion Matrix.

🎨 Step 11: Build Streamlit App

import streamlit as st
st.title("Spam Email Detector")
email = st.text_area("Paste email text here")
if st.button("Check"):
    email_vec = tfidf.transform([email])
    result = model.predict(email_vec)
    if result[0]==1:
        st.error("🚨 Spam Email Detected")
    else:
        st.success("✅ Legit Email")

Run: streamlit run app.py

⭐ Features to Add

Beginner: Show accuracy, simple UI

Intermediate: Add spam probability %, save email history

Advanced: Multi-language support, Phishing detection, Gmail API integration

📁 Project Folder Structure
spam-detector/
├── data/spam.csv
├── models/model.pkl
├── notebooks/training.ipynb
├── app.py
├── train.py
├── requirements.txt
└── README.md

💼 Resume Bullet
Spam Email Classifier using NLP
Built end-to-end text classification pipeline with Python, NLTK, TF-IDF, and Logistic Regression. Achieved 97%+ accuracy. Deployed interactive Streamlit app for real-time spam detection.

🚀 Mini Challenge for You
1. Add phishing email detection
2. Show spam probability percentage
3. Deploy on Hugging Face Spaces for free

🔥 Double Tap ❤️ For Part-3