In this project we will be working with a fake advertising data set, indicating whether or not a particular internet user clicked on an Advertisement. We will try to create a model that will predict whether or not they will click on an ad based off the features of that user.
This data set contains the following features:
Read in the advertising.csv file and set it to a data frame called ad_data.
Check the head of ad_data
Use info and describe() on ad_data
Let's use seaborn to explore the data!
Try recreating the plots shown below!
Create a histogram of the Age
Create a jointplot showing Area Income versus Age.
Create a jointplot showing the kde distributions of Daily Time spent on site vs. Age.
Create a jointplot of 'Daily Time Spent on Site' vs. 'Daily Internet Usage'
Finally, create a pairplot with the hue defined by the 'Clicked on Ad' column feature.
Now it's time to do a train test split, and train our model!
You'll have the freedom here to choose columns that you want to train on!
Split the data into training set and testing set using train_test_split
Train and fit a logistic regression model on the training set.
Create a classification report for the model.