This project is based on "Online Shoppers Purchasing Intention Dataset Data Set" https://archive.ics.uci.edu/ml/datasets/Online+Shoppers+Purchasing+Intention+Dataset

Goal: Develop Machine Learning Models to present a complete analysis for online shopping behavior data set.

Task 1: To build a predictive classification model (ensuring optimal features and classifier) on data entries corresponding to the months of June-Dec, and test the model on data entries corresponding to Feb-March.

Methods:

1. EDA

2. Data pre-processing: selecting input/target features, label encoding

3. Feature selection: pearson correlation and feature importance from decision trees
	
4. Feature scaling such that all features lie b/w 0 and 1

5. Classifiers: Linear: Logistic Regression/Non-linear: Kerne SVM(Poly/RBF)

6. Hyper parameter tuning

7. Auto-ML for comparison and verification

8. Output Metrics: Accuracy, Precision, Recall, F1-Score, Classification report, Confusion matrix, and ROC_AUC curve.

Results:

Linear and Non-linear Classifiers

		 LR_not_tuned	LR_tuned	KSVM_POLY	KSVM_RBF
      
accuracy	0.854137	0.947872	0.898135	0.932568

precision	0.384454	0.653571	0.476684	0.588235

recall		0.938462	 0.938462	0.943590	0.923077

f1 score	0.545455	0.770526	0.633391	0.718563

roc_auc	 	0.957698	0.944436	0.963843	0.973034

Verification with AutoML:

AutoML resulted in Gradient Boosting Classifier as the best classifier to perform the classification task.

Kernel SVM (rbf) vs GradientBoostingClassifier

		KSVM_RBF	XGB_CLASS
		
accuracy	0.932568	0.933525

precision	0.588235	0.811111

recall		0.923077	0.374359

f1 score	0.718563	0.512281

roc_auc		0.973034	0.959034

The accuracy of the GradientBoosting Classifer and Kernel SVM (rbf) are similar. However, the precision for the former is: 81.1% which is higher than the latter with 58.8%. Moreover, the roc_auc for Kernel SVM (rbf) is higher than the "GradientBoostingClassifier" which implies that the Kernel SVM (rbf) model does a good job in separating the classes.Though, recall is lower for the "GradientBoostingClassifier".

Conclusion:

Best performance with regards to separating the postive and negative classes, among the chosen linear/non-linear classifier is the RBF Kernel SVM classifier with roc_auc of 0.97.

Task 2: To generate user-bahavior clusters based on the purchasing behavior data for the complete dataset.

Methods:

Clustering using K-means; Identifying clusters of customers that are highly likely to purchase vs non-purchasers and determining potential customers to increase purchase. #Clusters found from: ProductRelatedDuration, BounceRates, PageValues, VisitorType, and Revenue

Output metrics: Silhouette score

Results:

Clusters being categorized into categories: Non-Purchasers, Targets (potential customers), and Purchasers based on the 'ProductRelated_Duration' i.e. Purchasers are the ones with largest 'ProductRelated_Duration' and vice-versa for 'Non-Purchasers'. Targets are the intermediate ones who do not end-up purchasing.Categorized them into 3 instead of 5; which is equivalent to having 3 clusters instead of 5. This seems to be better solution as seen from the analysis shown.

Conclusion:

Based on clustering and further analysis, we see that the returning visitors dominate as customers. How can we encourage new_visitors customers so that they become purchasers?

Task3: Consider we have training data (with the 'Revenue' attribute) for records from June-Sept only. For all records from Oct-Dec, the 'Revenue' attribute is missing. Build a semi-supervised self labelling model to estimate 'Revenue' for the missing records in Oct-Dec and then fit the classifier. Further, report classification performance on Feb-March data set with and without the self-labelled data.

Methods:

1. Data Pre-processing

2. Label Spreading (Semi-Supervised)

3. Post self-labeling fitting Logistic Regression classifier using self-labeled vs actual labels

4. Output metrics: Accuracy, Precision, Recall, F1-Score, Classification report, Confusion matrix, and ROC_AUC curve.

Results:

1. Classification performance on Feb-Mar dataset with self-labeled vs with actual labels 

Feb-March dataset with vs without self-labels i.e.actual labels

	self-labeled	actual labels
	
accuracy	0.927308	0.930177

precision	0.905660	0.810127

recall	        0.246154	0.328205

f1 score	0.387097	0.467153

roc_auc	        0.878062	0.929750

2. Classification performance on Oct-Dec dataset with self-labeled vs with actual labels 

	Oct-Dec dataset with vs without self-labels i.e.actual labels
	
		self-labeled	actual labels
		
accuracy	0.797497	0.819681

precision	0.925926	0.804348

recall	        0.022915	0.169569

f1 score	0.044723	0.280091

roc_auc	        0.697132	0.802893

Conclusions:

On Feb-March dataset, the classifier performs reasonably well and is only 5 % worse with self-labeled data.

However, on the Oct-Dec dataset,the classifier with self-labels performs not that well but then with actual labels the classifier doesn't peform exceedingly as well.