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.

13 Aug 2026 2 299

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.

13 Aug 2026 1 100

My Blog!

I have created a blog that contains all the information related to my work experience, personal projects, and other activities that I thought would be useful for all viewers. I have done several projects, yet there is one project that I love the most, it's the 100 Temples Project that I funded myself for all expenses to visit more than 100 ancient Khmer temples in several provinces in Cambodia, especially Siem Reap Province.

26 Jun 2026 0 109