mutthuluri / spam_email_classification

Spam_Email_Classification

📝 Description

This project tackles the issue of spam emails by automatically classifying incoming emails as either spam or legitimate (often referred to as "ham").

Who it's for:

Individuals: Anyone who receives a significant amount of email and wants to reduce the clutter and frustration caused by spam. Organizations: Businesses, schools, and other institutions that deal with a high volume of email can benefit from improved spam filtering to: Protect users from phishing attempts and malicious content. Increase productivity by reducing time spent sorting through spam. Reduce server load by filtering out unwanted emails. By automatically classifying emails, this project can help users save time and effort managing their inboxes, and organizations can improve security and efficiency.

⏳ Dataset

• Download he Dataset for Custom Training
• https://in.docworkspace.com/d/sIL6JkZqEAfP307IG?sa=cl

🖥️ Installation

🛠️ Requirements

• Python 3.5+
• Jupyter notebook/google collab

⚙️ Setup

• install numpy

  pip install numpy as np

• install pandas

  pip install pandas

• install sklearn

  from sklearn.model_selection import train_test_split

  from sklearn.feature_extraction.text import TfidfVectorizer

  from sklearn.linear_model import LogisticRegression

  from sklearn.metrics

Running Tests

To run tests, run the following command

  input_mail = [""]
  input_data_features = feature_extraction.transform(input_mail)
  prediction = model.predict(input_data_features)

🎯Accuracy

• on Training data

  0.9670181736594121

• on testing data

  0.9659192825112107

Contributors

Mutthuluri varun kumar

zeel Shah

G SUBHAM KUMAR

K MOHAN SAI VARDHAN

🔗 Links