In this project, I took the imaginary position as an employee at an Internet Service Provider who was tasked with improving the email filtering system for its customers.
With a dataset containing information about emails (2 classifications of either spam or not spam), I took this dataset and developed a supervised machine learning model that will accurately detect spam emails so it can filter them out of its customers' inboxes.
I created a logistic regression model and a random regression model to fit the dataset,then evaluated which model was more accurate at detecting spam.
The dataset for this project was sourced from UCI Machine Learning library and can be accessed here https://static.bc-edx.com/ai/ail-v-1-0/m13/challenge/spam-data.csv