Email Classification using NLP

This project focuses on classifying emails into spam and non-spam categories using Natural Language Processing (NLP) techniques. We'll preprocess the text data, visualize the label distribution, perform feature engineering, and train machine learning models for classification.

Dataset

The dataset used for this project can be found on Kaggle: Email Classification - NLP

Columns

1. Message Body: Contains the email content.
2. Label: Indicates whether the email is spam or non-spam.

Tasks

Task 1: Text Cleaning and Preprocessing

• Load the training and testing datasets.
• Check for missing values and remove them if any.
• Convert all text to lowercase.
• Remove stop words.
• Remove punctuation.
• Perform stemming or lemmatization.

Task 2: Data Visualization

• Visualize the distribution of the labels in the training dataset using a histogram, bar chart, or pie chart.

Task 3: Feature Engineering

• Apply text representation techniques:
  • Bag of words
  • TF-IDF

Task 4: Model Training

• Train SVM model after applying Bag of Words.
• Train SVM model after applying TF-IDF.
• Train Random Forest model after applying Bag of Words.
• Train Random Forest model after applying TF-IDF.

Evaluation Metrics

Evaluate the performance of the trained models on the testing dataset using the following metrics:

• Accuracy
• Precision
• Recall