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.

ការស្រាវជ្រាវសម្រាប់និស្សិតបរិញ្ញាបត្រ

ការស្រាវជ្រាវសម្រាប់និស្សិតបរិញ្ញាបត្រ គឺជាដំណើរការរៀនតាមរយៈការស៊ើបអង្កេតបញ្ហាជាក់លាក់ ក្រោមការណែនាំរបស់សាស្ត្រាចារ្យ ដោយប្រើចំណេះដឹង និងវិធីសាស្ត្រសមស្រប ដើម្បីប្រមូលភស្តុតាង វិភាគទិន្នន័យ បង្កើត ឬវាយតម្លៃដំណោះស្រាយ និងបង្ហាញលទ្ធផលតាមរបៀបវិទ្យាសាស្ត្រ។ នៅកម្រិតបរិញ្ញាបត្រ គោលដៅសំខាន់មិនមែនតម្រូវឱ្យនិស្សិតបង្កើតទ្រឹស្តីថ្មីដ៏ធំទូលាយនោះទេ ប៉ុន្តែត្រូវបង្ហាញថា និស្សិតអាចកំណត់បញ្ហាបានច្បាស់ ស្វែងរក និងប្រើឯកសារវិទ្យាសាស្ត្របានត្រឹមត្រូវ ជ្រើសរើសវិធីសាស្ត្រសមស្រប ប្រមូល និងវិភាគទិន្នន័យដោយសុចរិតភាព និងបង្កើតការរួមចំណែកដែលមានតម្លៃក្នុងបរិបទជាក់លាក់។ សម្រាប់និស្សិតកុំព្យូទ័រ គម្រោងអភិវឌ្ឍកម្មវិធីគួរត្រូវភ្ជាប់ជាមួយបញ្ហាស្រាវជ្រាវ ការវាយតម្លៃ និងភស្តុតាង ដើម្បីឱ្យគម្រោងនោះមានលក្ខណៈជាស្នាដៃស្រាវជ្រាវ និងអភិវឌ្ឍន៍។

ជំនាញឌីជីថលសុវត្ថិភាព និងការផ្សព្វផ្សាយប្រកបដោយទំនួលខុសត្រូវ

ក្នុងយុគសម័យឌីជីថល ទូរសព្ទឆ្លាតវៃ និងបណ្ដាញសង្គមអាចជួយឱ្យប្រជាពលរដ្ឋបញ្ចេញមតិ រាយការណ៍បញ្ហាសង្គម ផ្សព្វផ្សាយសកម្មភាព និងចូលរួមក្នុងជីវិតសាធារណៈបានយ៉ាងឆាប់រហ័ស។ ប៉ុន្តែការប្រើប្រាស់ដោយខ្វះការប្រុងប្រយ័ត្ន ក៏អាចបង្កហានិភ័យដល់សុវត្ថិភាពគណនី កេរ្តិ៍ឈ្មោះ សិទ្ធិឯកជន និងទំនួលខុសត្រូវផ្នែកច្បាប់ផងដែរ។

គោលការណ៍ប្រើ AI ក្នុងសាកលវិទ្យាល័យ៖ ពីការអនុញ្ញាតទៅការទទួលខុសត្រូវ

ការប្រើ AI ក្នុងសាកលវិទ្យាល័យត្រូវការគោលការណ៍ច្បាស់លាស់ ដើម្បីឱ្យគ្រូ និងនិស្សិតដឹងថា កិច្ចការណាអាចប្រើ AI កិច្ចការណាត្រូវប្រើដោយមានលក្ខខណ្ឌ និងកិច្ចការណាមិនអនុញ្ញាត។ អត្ថបទនេះនឹងណែនាំពីការកំណត់តួនាទី កម្រិតប្រើ AI ការប្រកាសការប្រើប្រាស់ ការការពារទិន្នន័យ និងនីតិវិធីដោះស្រាយការប្រើ AI ខុសគោលការណ៍។

Machine Learning-Based Smart EV Charging Station Route Recommendation

Electric vehicles introduce routing constraints that are different from those of conventional vehicles. A charging station can be close on a map yet still be unsuitable because the remaining battery is insufficient, the connector does not match, the station is inactive, or the charger is likely to be occupied at the driver's estimated arrival time. The research problem is therefore not simply shortest-path navigation; it is a prediction and ranking problem under safety constraints. The EV Fan prototype provides a practical application context for this Machine Learning study. Its current route planner already uses battery state, vehicle range, reserve target, connector compatibility, station status, and road distance to identify safe charging stops. The Phase 3 system also exposes station provenance and explicit unsafe-route warnings. These implemented rules form a deterministic baseline. The Machine Learning subject of this paper is the next layer: learning from charging-station usage data so the system can predict which safe station is most likely to be usable when the driver arrives. The central principle of the research is that a learned model should improve station selection without replacing hard safety rules. A prediction can influence ranking, but it should not override insufficient battery reserve or connector incompatibility. This hybrid design makes the ML component easier to evaluate and reduces the risk of treating uncertain predictions as safety guarantees.

Student Performance Prediction Using Machine Learning

This paper investigates the application of supervised machine learning algorithms for early student performance prediction and failure-risk classification. Driven by the proliferation of educational data within Learning Management Systems (LMS), early-warning systems are critical for identifying at-risk students before formal midterm or final evaluations occur. Utilizing a comprehensive dataset of 5,000 student records spanning demographic, academic history, behavioral engagement, and assessment scores, the target variable was collapsed into a binary outcome separating "At Risk" from "Not At Risk" students. Four classification models—Decision Tree, Gaussian Naive Bayes, Logistic Regression, and Random Forest—were trained, tuned, and evaluated on a stratified held-out test set. Experimental results indicate that Random Forest achieved the highest overall accuracy (92.30%) and F1-score (0.9272), whereas Logistic Regression yielded the highest ROC-AUC (0.9711) and superior recall on the minority at-risk class (0.915). Furthermore, feature importance analysis reveals that historical and ongoing academic performance indicators carry substantially more predictive weight than behavioral or demographic features. This study highlights the fundamental trade-off between aggregate accuracy and error-cost sensitivity when deploying early-warning systems in higher education.

Smart AI Rental Management System

មេរៀនទី ១៣៖ Object-Oriented Programming (OOP) ក្នុង Python Class, Object, Attribute និង Method

ក្នុងមេរៀនទី ១៣ នេះ យើងបានសិក្សាអំពី Object-Oriented Programming (OOP) ក្នុង Python ដែលជាវិធីរៀបចំកម្មវិធីដោយប្រើ Class និង Object ដើម្បីដាក់ទិន្នន័យ និងសកម្មភាពដែលពាក់ព័ន្ធគ្នាចូលក្នុងរចនាសម្ព័ន្ធតែមួយ។ Class គឺជាគំរូ ឬ Blueprint សម្រាប់បង្កើត Objects ខណៈ Object គឺជា Instance ដែលបង្កើតចេញពី Class។ Object នីមួយៗអាចមាន Attributes សម្រាប់រក្សាទុកទិន្នន័យ និង Methods សម្រាប់កំណត់សកម្មភាព ឬ Behavior របស់ Object។ យើងបានរៀនប្រើ __init__() ដើម្បីកំណត់ Initial Data នៅពេលបង្កើត Object និងប្រើ self ដើម្បីសំដៅទៅលើ Object បច្ចុប្បន្ន។ លើសពីនេះ យើងបានសិក្សាការបង្កើត Objects ច្រើនពី Class តែមួយ ការកែប្រែ Attributes ការហៅ Methods និងការប្រើ OOP រួមជាមួយ if, Loop, List និង Exception Handling។ មេរៀននេះក៏បានណែនាំគំនិតមូលដ្ឋានអំពី Encapsulation, Instance Attribute, Class Attribute, __str__(), @classmethod និង @staticmethod ដែលជួយឱ្យកម្មវិធីមានរចនាសម្ព័ន្ធល្អ ងាយអាន ងាយកែប្រែ និងងាយពង្រីក។ ចំណុចសំខាន់ដែលត្រូវចងចាំគឺ៖ Class → Object → Attribute → Method ឬ៖ គំរូ → Object → ទិន្នន័យ → សកម្មភាព