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:
- ‘Daily Time Spent on Site’: consumer time on site in minutes
- ‘Age’: cutomer age in years
- ‘Area Income’: Avg. Income of geographical area of consumer
- ‘Daily Internet Usage’: Avg. minutes a day consumer is on the internet
- ‘Ad Topic Line’: Headline of the advertisement
- ‘City’: City of consumer
- ‘Male’: Whether or not consumer was male
- ‘Country’: Country of consumer
- ‘Timestamp’: Time at which consumer clicked on Ad or closed window
- ‘Clicked on Ad’: 0 or 1 indicated clicking on Ad
Import a few libraries you think you’ll need (Or just import them as you go along!)
import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline import warnings warnings.filterwarnings('ignore')
Read in the advertising.csv file and set it to a data frame called ad_data.
ad_data = pd.read_csv("advertising.csv") ad_data.head()
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.
sns.jointplot("Age","Daily Time Spent on Site",data=ad_data,kind="kde",color="red");
Create a jointplot of ‘Daily Time Spent on Site’ vs. ‘Daily Internet Usage’
sns.jointplot("Daily Time Spent on Site","Daily Internet Usage",data=ad_data,color="Green");
Finally, create a pairplot with the hue defined by the ‘Clicked on Ad’ column feature.
sns.pairplot(ad_data,hue="Clicked on Ad",palette="viridis");
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
from sklearn.model_selection import train_test_split ad_data.head()
X = ad_data[["Daily Time Spent on Site","Age","Area Income","Daily Internet Usage","Male"]] y = ad_data["Clicked on Ad"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)
from sklearn.linear_model import LogisticRegression
Train and fit a logistic regression model on the training set.
logmodel = LogisticRegression()
Now predict values for the testing data.
predictions = logmodel.predict(X_test)
Create a classification report for the model.
from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix