Data Science Portfolio

Education

• Doctor of Philosophy in Chemical & Molecular Engineering at Stony Brook University (2018-2022)

Professional Certifications & Specializations

• Machine Learning Specialization by DeepLearning.AI (2023)
• Advanced-Data Analytics by Google (2023)
• Data Analytics by Google (2023)
• Business Intelligence by Google (2023)

Competency Skills

• Skillset: EDA - Data & Business Analysis - Modeling - Dashboarding & Reporting
• Programming Languages: Python and SQL
• Libraries: Numpy, Pandas, Matplotlib, Seaborn, and SciKit Learn
• Software Programs: Jupyter Notebooks, Excel, Tableau and Power BI

Projects

1. Predictive Machine Learning Analytics for Telecom Customer Churn

• Demonstrated expertise in business analytics, encompassing a range of objectives, such as understanding business cases, classifier model implementation, data handling, exploratory data analysis, and interactive data visualization.
• Proficiently prepared and employed data to develop and train logistic regression, support vector machine, random forest, k-nearest neighbor, and naive bayes classifier models for predicting customer churn.
• The results of the comparison between different classification models reveal that the Random Forest model achieved an F1 score of 90%.
• Future work will aim to enhance the model further through the utilization of advanced techniques such as grid search.

2. Medical Insurance Premium Prediction with Machine Learning

• Collected data from a diverse sample of insured individuals, covering age, gender, BMI, smoking habits, region, and medical charges.
• Conducted feature selection to pinpoint key factors for predicting insurance premiums.
• Utilized linear regression and artificial neural network (ANN) models, resulting in R-squared values of 0.69 and 0.74.
• Optimized ANN hyperparameters, increasing the R-squared value to 0.97.

3. Deep Learning Respiratory Disease Detection and Classification

• Compiled a medical imaging dataset with X-rays and CT scans featuring respiratory disease conditions, including Viral and Bacterial pneumonia, and Covid-19.
• Explored deep learning models, focusing on employing ResNet50 in conjunction with additional Convolutional Neural Networks (CNNs) for image classification tasks.
• Conducted model evaluation and hyperparameter tuning, leading to the choice of a robust CNN architecture.
• Achieved effective classification of healthy and viral pneumonia (F1 > 87%), with room for improvement in bacterial pneumonia (F1 < 63%) and Covid-19 (F1 < 75%).
• Future work will focus on enhancing bacterial pneumonia and Covid-19 classification.

Additional Projects

• Machine Learning with PySpark: Customer Churn Analysis
• Deep Learning for Real-Estate Price Prediction
• Predict Sales Revenue with scikit-learn
• NBA Rookies Career Longevity using Scikit-learn
• Diabetes Disease Detection with XG-Boost and Neural Networks
• Medical Diagnosis using Support Vector Machines Will add when as soon as possibe. TBD