Introduction
In this blog post, we will provide a sample project requirement and outline a solution approach for optimizing e-commerce operations through predictive analytics and machine learning techniques. This serves as a demonstration of how businesses can leverage data-driven insights to enhance decision-making processes and improve operational efficiency in the e-commerce domain.
Project Requirement
ASSIGNMENT DESCRIPTION
This assignment requires you to study a system case study and then write a report summarising their findings. The objective is to offer recommendations to improve the operations process of the case study organisation. The amount of internet information and data keeps growing at an exponential rate with new images and websites being uploaded every microsecond. The idea now is how can an organisation in e-Commerce observe, analyse, manage, and process this information and data. Based on these collected information and data, some customer actions can be predicted using predictive modelling and analytics, so that appropriate decisions and actions can be taken in order to achieve the organisational target. Predictive Analytics are methodologies that help in predicting customer behaviour. Predictive Analytics strategies help to achieve the following:
Predictive Analytics serve as a guide to shut out the noise.
Analytical models help to comprehend the difficult patterns between the different data points which is the basis of the decision-making process.
It aids to organise a data driven marketing procedure and plan, to assign appropriate investments.
Dataset Information:
The dataset contains data points of 12,330 customer session visits to the website. The dataset was designed so that every single session would fit in a different user in a 1-year gap to avoid any trend to a particular day, precise campaign, user profile, or specific period (Sahu, 2021).
Attribute Information:
The dataset entails of ten (10) numerical and eight (8) categorical attributes.
Revenue: Class level. Possible values: False and True.
Administrative, Administrative Duration: Represent the administrative pages stop at by the visitor in that session and the total time spent in each of this page category.
Informational, Informational Duration: Represent the information related pages visited by the visitor in that session and total time spent in each of this page category.
Product Related and Product Related Duration: Represent the product related pages visited by the visitor in that session and total time spent in each of this page category.
Bounce Rate denotes to the percentage of visitors who enter the site from that page and then leave without activating any other requests to the analytics server during that session.
Exit Rate portrays the percentage of exits on a page.
Page Value part signifies the average value for a web page that a user visited before finalising an e-commerce transaction.
Special Day part shows the closeness of the site visiting time to a precise special day (Sahu, 2021).
The dataset also comprises of some other structures such as browser, operating system, region, traffic type, visitor type as returning or new visitor, a Boolean value representing whether the date of the visit is month and weekend of the year.
Objective
To construct a predictive model and analysis to choose whether the customer will buy or not.
The implementation of predictive models in Python; the results of the experiments lead to the evaluation of the suggested strategies and lead to particular suggestions. The strategies can be found by reading the notes with the following topics
Models used in decision-making
Mathematics and statistical foundations of decision-making
Principles of algorithm-based models
Use of predictive analytics and machine learning in decision-making
Analysis of case studies
Assessment of accuracy, propagation of uncertainty and probabilities of uncertain events
Utility vs. cost benefit/effectiveness
Maximisation of expected utility of models"
Solution Approach
In this project, we tackled the challenge of optimizing operational performance and enhancing decision-making processes for an e-commerce company. Here's a breakdown of our approach:
Dataset Used
We utilized the Online Shoppers Purchasing Intention Dataset, sourced from the UCI Machine Learning Repository. This dataset contains valuable information about online shopper behavior, which is crucial for predictive modeling and analytics.
Basic Data Information
We began by loading and exploring the dataset to gain insights into its structure and contents. This step involved checking for missing values, duplicates, and understanding the distribution of variables.
Data Visualization
To gain a deeper understanding of the data, we employed various data visualization techniques, including:
Histograms and Countplots: Visualized the distributions of categorical variables such as special days, months, operating systems, browsers, regions, visitor types, and weekends.
Kernel Density Estimation (KDE) Plots: Examined the distributions of numerical variables such as administrative, informational, and product-related features.
Heatmaps: Constructed correlation matrices to identify relationships between different features, helping us understand potential correlations and dependencies.
Bar Plots: Explored average revenue trends across different months and regions to identify seasonal and geographical patterns.
Data Processing Techniques
To prepare the data for analysis, we performed several preprocessing steps:
Removing Duplicate Rows: Eliminated duplicate entries to ensure data integrity.
Handling Missing Values: Removed rows with missing or null values to maintain data quality.
Scaling the Data: Utilized standard scaling to normalize numerical features, ensuring consistency in model training.
Feature Selection
We identified both categorical and numerical variables in the dataset and visualized their distributions. This step helped us understand the importance of each feature and its potential impact on the predictive model.
Testing and Training
We split the dataset into training and testing sets to train and evaluate machine learning models. Additionally, we addressed the challenge of class imbalance by employing the Synthetic Minority Over-sampling Technique (SMOTE) to balance the dataset.
Algorithms Used
We experimented with various classification algorithms, including:
Random Forest
Decision Tree
Logistic Regression
Support Vector Machines (SVM)
Evaluation Metrics
For model evaluation, we focused on key metrics such as accuracy and F1 score. These metrics provided insights into the models' performance in predicting online shoppers' purchase intentions.
Model Selection
After comparing the performance of models on both imbalanced and balanced datasets, we identified the Balanced Random Forest as the best-performing model based on test accuracy and F1 score.
Hyper-Parameter Tuning
To further optimize model performance, we conducted hyper-parameter tuning using GridSearchCV. This process helped us identify the best combination of parameters for the Random Forest classifier.
Decision Making Using the Model
Finally, we discussed how the trained model can be leveraged to make informed decisions and improve revenue generation for the e-commerce company. By focusing on key features identified by the model, businesses can enhance operational efficiency and customer satisfaction.
Output :
At Codersarts, we specialize in providing tailored assistance for your e-commerce optimization project, aligning with the outlined tasks and objectives in your blog. Our team excels in guiding you through each stage of the project, from understanding the dataset to implementing advanced analytics techniques. We offer hands-on support in utilizing Python for data preprocessing, analysis, and predictive modeling, ensuring the accuracy and reliability of your solutions.
Our expertise extends to employing various machine learning algorithms such as Random Forest, Decision Tree, Logistic Regression, and Support Vector Machines (SVM) to construct predictive models that accurately forecast customer purchasing behavior. Through rigorous testing and evaluation, we identify the most suitable model for your specific requirements, leveraging techniques like hyper-parameter tuning to optimize performance.
Moreover, Codersarts facilitates comprehensive project evaluation, providing quantitative assessments and insightful interpretations of your findings. We offer documentation review and problem-solving sessions to enhance the quality and success of your e-commerce optimization endeavor. With our dedicated support, you can confidently navigate the complexities of predictive analytics and machine learning, driving operational efficiency and maximizing business outcomes in the competitive e-commerce landscape.
If you require any assistance with the project discussed in this blog, or if you find yourself in need of similar support for other projects, please don't hesitate to reach out to us. Our team can be contacted at any time via email at contact@codersarts.com.