Email Spam Detector with TF-IDF and Random Forest

A spam/ham email classification project using TF-IDF, Random Forest, and Logistic Regression. The dataset is hosted on Kaggle, but its size, origin, and update history are unspecified. The description indicates the data is intended for building a text-based classifier.

Use Cases

• Train a spam email classifier based on the text content mentioned in the description
• Benchmark Random Forest and Logistic Regression models for text classification
• Learn TF-IDF feature extraction for email text data
• Demonstrate a binary classification pipeline for educational purposes

Strengths

• The description specifies the use of established ML techniques (TF-IDF, Random Forest, Logistic Regression) for the task.
• The dataset is focused on a well-defined binary classification problem (spam/ham).

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Kaggle

Collection Method

unknown

Time Range

unknown

Freshness

unknown

Geography

unknown