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.

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

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

• 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.

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

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

• 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.

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

• Accuracy
• Precision
• Recall