📧 Email Spam Classifier

📌 Project Overview

The Email Spam Classifier is a Machine Learning project that detects whether an email/message is Spam or Ham (Not Spam) using Natural Language Processing (NLP) techniques.

This project uses:

• Python for implementation
• Pandas & NumPy for data handling
• TF-IDF Vectorization for text feature extraction
• Logistic Regression for classification
• Scikit-learn for machine learning utilities

The model is trained on a dataset containing labeled email messages and predicts whether a given message is spam or not.

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🎯 Objectives

• Preprocess email/message text data
• Convert text into numerical features using TF-IDF
• Train a machine learning model for spam detection
• Evaluate the model accuracy
• Predict whether new messages are spam or ham

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🛠️ Technologies Used

• Python
• Jupyter Notebook
• NumPy
• Pandas
• Scikit-learn

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📂 Project Structure

Email-Spam-Classifier/
│
├── Email Spam Classifier.ipynb   # Main project notebook
├── mail_data.csv                 # Dataset used for training
├── README.md                     # Project documentation

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📊 Dataset Information

The dataset contains:

• Category → Label indicating whether the message is spam or ham
• Message → The actual email/text message

Example:

Category	Message
ham	Hello, how are you?
spam	Congratulations! You won a prize.

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⚙️ Workflow of the Project

1. Import Required Libraries

The project imports libraries for:

• Data manipulation
• Machine learning
• Text vectorization
• Model evaluation

2. Load Dataset

The dataset is loaded using Pandas.

import pandas as pd

df = pd.read_csv('mail_data.csv')

3. Data Preprocessing

• Handle missing values

• Convert labels:

  • spam → 0
  • ham → 1

data = df.where((pd.notnull(df)), '')

4. Feature Extraction

Text messages are converted into numerical vectors using TF-IDF Vectorizer.

from sklearn.feature_extraction.text import TfidfVectorizer

feature_extraction = TfidfVectorizer(min_df=1, stop_words='english', lowercase=True)

5. Split Dataset

The dataset is divided into training and testing data.

from sklearn.model_selection import train_test_split

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=3)

6. Train the Model

The project uses Logistic Regression.

from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
model.fit(X_train_features, Y_train)

7. Model Evaluation

Accuracy is calculated using:

from sklearn.metrics import accuracy_score

8. Prediction

The trained model predicts whether a new email is spam or ham.

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🚀 How to Run the Project

Step 1: Clone the Repository

git clone https://github.com/your-username/email-spam-classifier.git

Step 2: Navigate to the Project Folder

cd email-spam-classifier

Step 3: Install Required Libraries

pip install numpy pandas scikit-learn jupyter

Step 4: Run the Jupyter Notebook

jupyter notebook

Open:

Email Spam Classifier.ipynb

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📈 Machine Learning Model Used

Logistic Regression

Logistic Regression is a supervised machine learning algorithm used for binary classification problems.

Advantages:

• Simple and efficient
• Fast training
• Good performance for text classification
• Works well with TF-IDF features

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✅ Expected Output

The model predicts:

Spam Message

OR

Ham Message

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📌 Future Improvements

• Use advanced NLP techniques
• Implement Deep Learning models
• Build a web application interface
• Improve accuracy with larger datasets
• Add real-time email filtering

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📷 Sample Prediction

input_mail = ["Congratulations! You have won a free ticket"]

Output:

Spam Mail

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🤝 Contribution

Contributions are welcome.

Steps:

1. Fork the repository
2. Create a new branch
3. Make changes
4. Submit a pull request

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