Online-shoppers-intention-prediction

Predict the intention (make a purchase or not) of e-commerce website visitors.

Data Source:

http://archive.ics.uci.edu/ml/datasets/Online+Shoppers+Purchasing+Intention+Dataset

Data Description:

The dataset consists of feature vectors belonging to 12,330 sessions. The dataset was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period. Of the 12,330 sessions in the dataset, 84.5% (10,422) were negative class samples that did not end with shopping, and the rest (1908) were positive class samples ending with shopping.

Numerical features

Feature name	Feature description	Max. val	SD
Admin.	#pages visited by the visitor about account management	27	3.32
Ad. duration	#seconds spent by the visitor on account management related pages	3398	176.70
Info.	#informational pages visited by the visitor	24	1.26
Info. durat.	#seconds spent by the visitor on informational pages	2549	140.64
Prod.	#pages visited by visitor about product related pages	705	44.45
Prod.durat.	#seconds spent by the visitor on product related pages	63,973	1912.3
Bounce rate	Average bounce rate value of the pages visited by the visitor	0.2	0.04
Exit rate	Average exit rate value of the pages visited by the visitor	0.2	0.05
Page value	Average page value of the pages visited by the visitor	361	18.55
Special day	Closeness of the site visiting time to a special day	1.0	0.19

Categorical features

Feature name	Feature description	Number of Values
OperatingSystems	Operating system of the visitor	8
Browser	Browser of the visitor	13
Region	Geographic region from which the session has been started by the visitor	9
TrafficType	Traffic source (e.g., banner, SMS, direct)	20
VisitorType	Visitor type as “New Visitor,” “Returning Visitor,” and “Other”	3
Weekend	Boolean value indicating whether the date of the visit is weekend	2
Month	Month value of the visit date	12
Revenue	Class label: whether the visit has been finalized with a transaction	2

Project Goal

The main goal of this project is to design a machine learning classification system, that is able to predict an online shopper's intention ( buy or no buy ), based on the values of the given features (from google analytics). A number of different classification algorithms is tested, in order to pick the best one for the project.

Conclusion

In this project, we used Online Shoppers Intention dataset to build models that can classify website visitor, and predict which of them is likely going to make a purchase on the website. 7 different learning classifiers (Naive Bayes, KNN, SVM, Logistic Regression, Random Forest, Gradient Boosting, and Adaboosting) were tested and optimized, and we have achieved the best classification performance using Gradient Boost classifier, followed by random Forest, and then Adaboost.

Classifier	Accuracy	F1-Score	Precision	Recall
Naive Bayes	0.775	0.491	0.394	0.652
KNN	0.873	0.506	0.723	0.39
SVM	0.889	0.6	0.751	0.5
Logistic Regression	0.879	0.529	0.758	0.406
Random Forest	0.902	0.662	0.774	0.578
Gradient Boost	0.905	0.689	0.761	0.63
AdaBoost	0.889	0.624	0.713	0.555