Spam email prediction is the process of identifying and classifying unwanted email messages as spam. This is done using Python, NumPy, Pandas and Machine Learning techniques. The goal is to develop a model that can accurately identify spam emails.

DESCRIPTION • The project code completely done using Python

• Dataset link:https://drive.google.com/file/d/1uzbhec5TW_OjFr4UUZkoMm0rpyvYdhZw/view?usp=sharing

• Required packages installed, that are pandas, re, nltk, sklearn, seaborn, matplotlib

• Logistic Regression used as classification model for this project to get high accuracy for the text data perfomed from NLP operations.

Other Key steps to Spam Mail Detection:

• Email Filtering: One of the primary methods for spam mail detection is email filtering. It involves categorize incoming emails into spam and non-spam. Machine learning algorithms can be trained to filter out spam mails based on their content and metadata.

• Text Classification: Text classification is a supervised learning technique used for spam detection. It involves labelling emails as spam or non-spam based on their features, such as the presence of certain keywords, tone, or grammar.

• Feature Engineering: Feature engineering is the process of selecting relevant features from the email to classify it as spam or non-spam. It involves extracting features such as the sender's email address, the presence of certain words or phrases, and the length of the email.

• Supervised Learning: Supervised learning is a technique that involves training the model on labelled data to predict the labels of new, unlabeled data. It is widely used in spam detection for text classification tasks.

• Unsupervised Learning: Unsupervised learning is a technique used to find hidden patterns in the data without the need for labelled data. It can be used for anomaly detection, clustering, and association rule mining.