Smart E-Commerce System with AI Recommendation Engine and Customer Behavior Analytics

E-commerce platforms usually contain a large number of products, which can make it difficult for customers to quickly find items that match their interests. This project presents a Smart E-Commerce System that combines a machine learning recommendation engine with customer behavior analytics and supports the main activities of customers, vendors, and administrators. The recommendation system uses a hybrid approach that combines collaborative filtering and content-based filtering. Collaborative filtering identifies similar product preferences based on user interactions using cosine similarity, while content-based filtering uses TF-IDF to find similarities between product information. When both methods are available, the system combines them using 75% collaborative filtering and 25% content-based filtering. Customer activities are also considered when generating recommendations. Different actions are given different weights based on their importance: viewing a product has a weight of 1.0, clicking 1.5, adding to a wishlist 2.0, adding to cart 3.0, and purchasing 5.0. Recent activities are given more importance through a time-decay method with a 45-day half-life. For new customers or users with fewer than five product interactions, the system recommends popular products as an alternative. The recommendation results are stored using Redis caching and can be accessed through the system API. The model is evaluated using temporal hold-out testing with metrics such as Precision@10, Recall@10, MAP@10, and catalog coverage. Overall, the project demonstrates how machine learning can be integrated into a practical e-commerce system to provide personalized product recommendations while supporting normal online shopping and management processes.

An Intelligent Dealer Retention and Engagement Platform Using Machine Learning, Natural Language Processing, and Explainable AI

In the highly competitive telecommunications industry, indirect sales channels managed by independent dealers contribute significantly to market share and revenue growth. However, dealer churn and declining engagement present severe operational challenges. This paper proposes a comprehensive framework for an Intelligent Dealer Retention and Engagement Platform that leverages Machine Learning (ML), Natural Language Processing (NLP), and Explainable AI (XAI) to proactively mitigate dealer attrition. The system architecture comprises four core pillars: (1) a Dealer Churn Prediction model utilizing XGBoost, optimized via F1-Score and evaluated using ROC-AUC; (2) an Explainable AI layer driven by SHAP (SHapley Additive exPlanations) to provide local and global feature transparency; (3) a Dealer Feedback Classification engine using TF-IDF and Logistic Regression to process qualitative field complaints; and (4) an automated Rule-Based Recommendation engine that translates predictive insights into prescriptive retention strategies. Utilizing Business Intelligence (BI) historical snapshots and qualitative agent visit data, this research demonstrates how foundational, highly interpretable ML models can be synthesized into a robust enterprise solution. The proposed framework bridges the gap between predictive accuracy and operational decision-making, offering telecom operators a practical blueprint for sustainable channel partner management.

Machine Learning-Based Loan Risk Prediction for Credit Scoring

The rapid expansion of consumer lending, digital banking, and other technology-enabled financial services increases the need for timely and consistent credit-risk assessment. Traditional loan underwriting commonly relies on predefined rules and manual review of income, employment, collateral, guarantors, and credit-bureau information. Although such approaches remain important, they can be slow and may have limited ability to identify complex, non-linear relationships among borrower and loan characteristics. This study develops and evaluates a machine-learning-based loan risk prediction framework for binary classification of loans into lower-risk and higher-risk outcomes. The project uses an approximately one-million-record historical loan dataset obtained from Kaggle, with 57 attributes in the supplied schema. The methodology includes data cleaning, missing-value handling, categorical encoding, numerical scaling, outlier treatment, feature engineering, stratified train-test splitting, model training, hyperparameter tuning, and iterative refinement. Five supervised learning approaches were compared: Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and XGBoost. The reported test ROC-AUC scores were 0.7024, 0.5515, 0.7165, 0.7193, and 0.7266, respectively, making XGBoost the best untuned model. After hyperparameter tuning, XGBoost achieved a test ROC-AUC of 0.7273. The results indicate that XGBoost provides the strongest overall discrimination among the tested models, while threshold selection remains a critical business decision because it changes the balance between approving good customers and preventing default exposure. The proposed framework also includes model export using joblib and deployment through a REST API and web application. The study demonstrates the potential of machine learning as a decision-support tool for credit risk management, while emphasizing that local Cambodian loan data, cost-sensitive thresholding, monitoring, and explainability are required before production deployment.

Credit Card Fraud Detection

Credit card fraud is a significant challenge in electronic payment systems because fraudulent transactions can cause financial losses and reduce customer trust. This project develops a machine-learning-based Credit Card Fraud Detection System to identify potentially fraudulent transactions from transaction data. The dataset contains 100,000 transaction records with attributes including transaction date, amount, merchant, transaction type, and location. The data is processed through feature engineering, categorical encoding, numerical scaling, and SMOTE to address class imbalance. Four machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, and XGBoost—are trained and compared using Accuracy, Precision, Recall, F1 Score, and Confusion Matrix. F1 Score is used as the primary metric for selecting the best-performing model because it provides a balance between Precision and Recall for the imbalanced fraud detection problem. The selected model is integrated into a Streamlit web application that allows users to upload transaction data, predict fraudulent transactions, visualize prediction results, and download a report containing detected fraudulent transactions. The system demonstrates how machine learning can support automated transaction screening and provide a practical prototype for fraud detection.

Machine Learning-Based Demand Prediction for Factory Production Planning of Shampoo, Body Wash, and Laundry Detergent (Regression)

Accurate demand prediction can support factory production planning by reducing the risk of overproduction, stockouts, and inefficient inventory decisions. This project develops a supervised machine learning regression workflow for shampoo, body wash, and laundry detergent. Because real factory records were not supplied, a synthetic demonstration dataset was used transparently to implement and test the proposed pipeline without claiming real factory performance. The dataset contains 177 monthly product records and includes product type, month and season, previous sales, stock quantity, price, promotion, customer order quantity, and previous production quantity. Three regression algorithms - Linear Regression, Decision Tree Regressor, and Random Forest Regressor - were trained using a time-aware train/test split. Performance was evaluated using mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2). On the synthetic test set, Linear Regression achieved the best result with MAE 62.5, RMSE 81.2, and R2 0.913. The workflow also produces feature-importance evidence and an illustrative production recommendation based on predicted demand, current stock, and a 10% safety-stock rule. The project demonstrates an end-to-end, reproducible framework that can be rerun with authorized real factory data for operational use.

មេរៀនទី ៩៖ Observer Pattern and Event-Driven OOP

មេរៀនទី ៩ ផ្ដោតលើ Observer Pattern និង Event-Driven OOP ដើម្បីឱ្យនិស្សិតយល់ពីរបៀបធ្វើឱ្យ Objects ជាច្រើនអាចឆ្លើយតបទៅនឹង Event មួយដោយមិនចងគ្នាខ្លាំងពេក។ និស្សិតសិក្សាតួនាទីរបស់ Subject/Publisher, Observer/Subscriber, subscribe(), unsubscribe(), notify() និង publish() ព្រមទាំងការបង្កើត Domain Events ដោយប្រើ dataclass និង Event Handler Contracts ដោយប្រើ Protocol។ ក្នុងការអនុវត្ត និស្សិតបង្កើត EventBus សម្រាប់ចែកចាយ Events ដូចជា TaskCreatedEvent, TaskAssignedEvent និង TaskCompletedEvent ទៅកាន់ Audit, Notification, Dashboard និង Activity Feed Handlers ដោយឯករាជ្យ។ សរុបមក មេរៀននេះជួយឱ្យនិស្សិតអាចរចនាប្រព័ន្ធដែលមាន Loose Coupling, ងាយពង្រីក, ងាយសាកល្បង និងត្រៀមសម្រាប់ Repository, Service Layer និង Event-Driven Architecture នៅមេរៀនបន្ទាប់។

មេរៀនទី ៨៖ Strategy, Template Method and Command Patterns

មេរៀនទី ៨ ផ្ដោតលើ Strategy, Template Method និង Command Patterns ដើម្បីឱ្យនិស្សិតយល់ពីការរចនា Behavior និង Workflow ឱ្យមានភាពបត់បែន និងងាយពង្រីក។ Strategy Pattern អនុញ្ញាតឱ្យបំបែក Algorithm ឬ Behavior ដែលអាចជំនួសគ្នាបាន និងអាចប្ដូរនៅ Runtime, Template Method កំណត់ Workflow រួមនៅ Base Class ហើយឱ្យ Subclass កែតែជំហានជាក់លាក់, ចំណែក Command Pattern បម្លែង Action ឬ Request ទៅជា Object ដែលអាច execute(), រក្សាទុក History, Queue និង undo() បាន។ ក្នុងការអនុវត្ត និស្សិតប្រើ Discount/Payment Strategies, Report Export Workflow និង Task Commands ដើម្បីយល់ពី Composition, Inheritance, Loose Coupling និងការបែងចែក Responsibility ឱ្យច្បាស់។ សរុបមក មេរៀននេះជួយឱ្យនិស្សិតអាចរចនា Business Behavior ដែលងាយសាកល្បង ងាយថែទាំ និងងាយបន្ថែមមុខងារថ្មីដោយមិនប៉ះពាល់ Core Logic ច្រើន។

AI-Driven HR Analytics & Intelligent Recruitment System

Traditional, manual resume screening processes have become major organizational bottlenecks. Human recruiters spend an average of only a few seconds skimming each resume, a process that is highly inefficient, prone to human fatigue, and susceptible to cognitive biases. This bottleneck often results in two critical failures: top-tier talent gets buried and overlooked in massive databases, while unqualified applicants exhaust valuable administrative resources. Furthermore, traditional automated tools rely heavily on static, keyword-matching tracking systems. These systems fail to comprehend context, meaning a highly qualified candidate who uses creative phrasing or synonyms instead of the exact terminology found in the job description is often prematurely filtered out.

មេរៀនទី ៧៖ SOLID Principles and Refactoring

មេរៀនទី ៧ ផ្ដោតលើ SOLID Principles និង Refactoring ដើម្បីឱ្យនិស្សិតយល់ពីការរចនា Software ឱ្យមាន Responsibility ច្បាស់ មាន Coupling ទាប និងងាយពង្រីក។ និស្សិតសិក្សា SRP សម្រាប់បំបែក Responsibility, OCP សម្រាប់ពង្រីក Behavior ដោយមិនកែ Core Logic, LSP សម្រាប់ធានាថា Subclass អាចជំនួស Base Class បានដោយសុវត្ថិភាព, ISP សម្រាប់បំបែក Interface ធំៗទៅជា Contracts តូចៗ និង DIP សម្រាប់ឱ្យ High-Level Logic ពឹងផ្អែកលើ Abstraction ជំនួស Concrete Implementation។ ក្នុងការអនុវត្ត និស្សិតធ្វើ Refactoring ដោយបំបែក UserService, Repository, Notifier, Auditor និង PasswordHasher, ប្រើ Dependency Injection និង Fake Dependencies សម្រាប់ Testing។ សរុបមក មេរៀននេះជួយឱ្យនិស្សិតអាចបម្លែង Code ដែលស្មុគស្មាញទៅជា Structure ដែលងាយអាន ងាយសាកល្បង ងាយថែទាំ និងងាយពង្រីក។

មេរៀនទី ៦៖ Decorators and Cross-Cutting Concerns

មេរៀនទី ៦ ផ្ដោតលើ Decorators និង Cross-Cutting Concerns ដើម្បីឱ្យនិស្សិតយល់ពីរបៀបបន្ថែម Behavior ទៅ Function ឬ Method ដោយមិនលាយ Logic បន្ថែមចូលក្នុង Core Business Logic។ និស្សិតរៀនពី First-Class Functions, Higher-Order Functions, Closures, Wrapper Functions, *args, **kwargs និង functools.wraps ព្រមទាំង Decorators ដែលមាន Parameter។ ក្នុងការអនុវត្ត និស្សិតប្រើ Decorators សម្រាប់ Logging, Timing, Validation, Authentication, Authorization និង RBAC Permission Checking ដូចជា @require_login និង @require_permission(...) ហើយសិក្សាពីសារៈសំខាន់នៃ Decorator Order និង Separation of Concerns។ សរុបមក មេរៀននេះជួយឱ្យនិស្សិតបង្កើត Code ដែលអាចប្រើឡើងវិញបាន ងាយអាន ងាយសាកល្បង និងត្រៀមសម្រាប់ SOLID Principles និង Flask-based application design នៅមេរៀនបន្ទាប់។

មេរៀនទី ៥៖ Factory Method and Abstract Factory

មេរៀនទី ៥ ផ្ដោតលើ Factory Pattern ដែលជាគន្លឹះសម្រាប់បំបែក Object Creation ចេញពី Business Logic និង Client Code ដើម្បីកាត់បន្ថយ coupling និងធ្វើឱ្យប្រព័ន្ធងាយពង្រីក។ និស្សិតរៀនពី Simple Factory, Factory Method, @classmethod ជា Alternative Constructor, Registry-Based Factory និង Abstract Factory សម្រាប់បង្កើត Product ឬ Product Family ដែលពាក់ព័ន្ធគ្នា។ ក្នុងការអនុវត្ត និស្សិតបង្កើត NotifierFactory, UserFactory និង Report/UI Factory ដើម្បីជ្រើស Concrete Objects តាម runtime requirement ឬ configuration ហើយភ្ជាប់ជាមួយ Abstraction, Protocol និង Dependency Injection។ សរុបមក មេរៀននេះជួយឱ្យនិស្សិតយល់ថា Factory Pattern គឺជាវិធីរៀបចំ Object Creation ឱ្យមានរចនាសម្ព័ន្ធច្បាស់ ងាយសាកល្បង ងាយបន្ថែម implementation ថ្មី និងត្រៀមសម្រាប់ SOLID Principles និង Flask Application Factory នៅមេរៀនបន្ទាប់។

មេរៀនទី ៤៖ Abstraction, Composition, Mixins and Protocols

មេរៀនទី ៤ ផ្ដោតលើ Abstraction, Composition, Mixins និង Protocols ដើម្បីឱ្យនិស្សិតយល់ពីការរចនាទំនាក់ទំនងរវាង Objects និង Classes ឱ្យមានភាពបត់បែន និងងាយពង្រីក។ និស្សិតរៀនប្រើ ABC និង @abstractmethod សម្រាប់កំណត់ Contract, ប្រៀបធៀប is-a និង has-a relationships, ប្រើ Composition និង Dependency Injection ដើម្បីកាត់បន្ថយ coupling, ប្រើ Mixins សម្រាប់ចែករំលែក behavior និងយល់ពី MRO និង super() ក្នុង multiple inheritance។ លើសពីនេះ និស្សិតសិក្សា Protocol និង Structural Typing ដើម្បីអនុញ្ញាតឱ្យ Objects ផ្សេងៗសហការគ្នាតាម behavior ដោយមិនចាំបាច់ inherit ពី Base Class ដូចគ្នា។ ក្នុងការអនុវត្ត និស្សិតបង្កើត Multi-Channel Notification System ដែលមាន Email, SMS, Console និង Service Layer ដោយប្រើ Abstraction, Composition, Protocol និង Dependency Injection ជាគ្រឹះសម្រាប់មេរៀន Design Patterns បន្ទាប់។