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1. LOS_Regression_Analysis_Code_R_SAS LOS_Regression_Analysis_Code_R_SAS Public

   Patient Length of Stay (LOS) is crucial for assessing emergency department (ED) performance. This study explores the association between LOS and multi-level factors, including patient-, service-, a…

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2. Product_Recommender_System_LLM_NLP Product_Recommender_System_LLM_NLP Public

   Personalized product recommendation system using Amazon Electronics data. Combines collaborative filtering, NLP, and LLM evaluation to optimize user experience. Includes model tuning, precision-rec…

   Jupyter Notebook

3. Lead_Conversion_Optimize_via_ML_Python Lead_Conversion_Optimize_via_ML_Python Public

   Analyzed leads data for ExtraaLearn, developed a machine learning model to predict lead conversion, and provided actionable insights. Recommended enhancements for lead engagement, marketing optimiz…

   Jupyter Notebook 1

4. Predicting--Self-Efficacy_ML_Code_R_SAS Predicting--Self-Efficacy_ML_Code_R_SAS Public

   Utilized Decision Tree, Random Forest, XGBoost, and Neural Networks to predict student self-efficacy in Muslim societies. Analyzed factors like self-regulation, problem-solving, and belonging, deli…

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5. Health_Insurance_Fraud_Detect_R_Paython Health_Insurance_Fraud_Detect_R_Paython Public

   Predicting fraudulent health insurance claims using machine learning (Decision Tree & Random Forest) with Python and R, including EDA, model evaluation, and feature importance analysis.

   Jupyter Notebook

6. insurance_cost_prediction_R_Python insurance_cost_prediction_R_Python Public

   Predicting individual health insurance costs using demographic and health factors with Linear Regression and Decision Tree models. Includes full EDA, modeling, and insights using R and Python.

   Jupyter Notebook