Email_spam_classifier for issue #237

[Garvanand]

Email Spam Classifier

This Python script utilizes machine learning techniques to classify emails as either spam or legitimate (ham). The classifier is built using logistic regression and employs the TF-IDF (Term Frequency-Inverse Document Frequency) vectorizer for feature extraction. Here's a brief overview of the workflow:

Data Preparation: The script loads email data from a CSV file and preprocesses it, handling missing values and converting categorical labels (spam, ham) to binary values.

Feature Extraction: TF-IDF vectorization is used to transform the text data into numerical features, capturing the importance of words in the documents.

Model Training: A logistic regression model is trained on the extracted features to learn patterns distinguishing between spam and ham emails.

Evaluation: The model's performance is evaluated on both training and testing datasets using accuracy metrics.

Prediction: Users can input an email message, and the model predicts whether it's spam or legitimate.

This classifier enables efficient identification of spam emails, contributing to a more organized and secure inbox experience.

Kindly assign it GSSoC label and mark it as a contribution under it

Addressing issue : #237 @sanjay-kv

[github-actions]

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