Agenda/Aim:
1) Preprocess the data:
Clean the data and remove any irrelevant information. As our data is already in numerical form so we don’t need to convert it.
2) Train the models:
Train several supervised classification models such as Logistic Regression, KNN, SVM, Naive Bayes, Decision Trees, Random Forest, and Gradient Boosting using the preprocessed data.
3) Evaluate the models:
Evaluate the performance of the models using metrics such as accuracy, precision, recall, and F1-score.
4) Choose the best model:
Based on the evaluation, choose the best model that provides the highest accuracy and has the best overall performance.
The overall aim of this project is to train a machine learning model on the given email data to predict whether an email is spam or not spam, and to choose the best model for this classification task.
About the dataset
The dataset is from kagge Link
The emails.csv file contains 5172 rows, each row for each email….