Spam Mail Prediction using Machine Learning This project involves building a spam mail detector using Python within the Google Colab environment. By leveraging machine learning techniques, we aim to automatically classify emails as either spam or legitimate. The detector will enhance user security by filtering out potentially harmful emails. Source code(with describtion) Importing the Dependencies import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score Importing Libraries: The code begins by importing necessary libraries such as NumPy, Pandas, scikit-learn's train_test_split , TfidfVectorizer , LogisticRegression , and accuracy_score from sklearn.metrics . Data Preparation: It implies that you have a dataset containing email content along with labels indicating whether each emai...