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Python pandas NumPy scikit-learn (LinearRegression

Full-Funnel Retail Pricing & Revenue Optimization Engine

This project builds a unified retail pricing analytics system that cleans and merges multi-sheet data, engineers key sales features, analyzes patterns like discounts and seasonality, trains OLS and Ridge models to predict order revenue, and exports a Tableau-ready Excel workbook with insights and diagnostics.

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Python pandas NumPy seaborn

Security Awareness Training Impact Analyzer

Evaluates how effective different security awareness training types are by cleaning assessment and phishing test data, tuning a Random Forest model to predict post-training scores, comparing it with other classifiers, and using rich visual and statistical analyses (correlations, t-tests, ANOVA) to show improvements and differences across departments and training methods.

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Python pandas NumPy requests

Real-Time Tech Job Market Analytics Dashboard

Pulls live remote tech job postings from the RemoteOK API, cleans and analyzes skills, companies, locations, and posting trends, and builds rich visualizations and a mini dashboard to explore in-demand skills and market activity.

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feedparser NLTK (WordNet SentiWordNet tokenizers stopwords) VADER gensim Word2Vec

Multimodel News Sentiment Intelligence Pipeline

Builds a full NLP pipeline that pulls live BBC RSS headlines, cleans and vectorizes text, applies both VADER and SentiWordNet sentiment models, trains Word2Vec embeddings, and generates dashboards (word clouds, stacked sentiment charts, and summary tables) to compare sentiment across World, Business, and Technology news.

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Python pandas tabula-py matplotlib

Loan Portfolio Data Extraction & Risk Insights Dashboard

Converts raw loan datasets from PDF and Excel into structured tables using Tabula, performs data cleaning, standardizes categorical variables, merges datasets, and generates analytical insights on loan amounts, approvals, demographics, property profiles, self-employment effects, and applicant income distribution with supporting visualizations.

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Python pandas scikit-learn (KMeans LinearRegression

YouTube Engagement Intelligence & Clustering Analysis

Cleans and preprocesses a large GB YouTube trending dataset, extracts temporal insights, performs KMeans clustering on engagement metrics, fits a linear regression model to understand what drives view counts, computes correlations, applies PCA for dimensionality reduction, and visualizes category trends, top channels, disabled features, and time-based publishing patterns.

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Python pandas NumPy statsmodels (Logit

Comprehensive PHI Market Intelligence & Policy Impact Modeling System

Loads multi-sheet cleaned Excel datasets for Australian Private Hospital Insurance (PHI), restructures and harmonizes complex quarterly/monthly blocks, constructs national hospital and general-treatment insured-person series, and produces interactive Plotly dashboards for long-term trend analysis, COVID-period comparisons, state-level heatmaps, age-gender cohort analysis, YoY growth, and composition shifts. Also implements full statistical modeling of PHI uptake using micro-level demographic and socioeconomic variables, including logistic regression, marginal effects, MLS (Medicare Levy Surcharge) policy impact estimation, confusion matrices, ROC-AUC evaluation, and scenario-based prediction changes.

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Python pandas NumPy scikit-learn

Loan Increase Profit Optimization & Risk-Aware Decision Engine

Develops an end-to-end decision intelligence system for determining optimal loan-limit increases. The workflow includes data cleaning, customer behavioral segmentation, acceptance-probability modeling with logistic regression, default-risk estimation, Markov-chain state transitions for evolving risk states, expected-profit calculations, portfolio-level linear programming optimization, 12-month Monte-Carlo simulation, economic stress-scenario modeling, and reinforcement learning (Q-learning) to learn data-driven offer policies. The system maximizes profit while respecting exposure, default, and risk constraints.

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Python pandas matplotlib plotly (graph_objects

Advanced Weather Pattern Visualization Suite

Loads raw weather data and performs extensive exploratory analysis using both Matplotlib and Plotly, generating time-series, bar charts, scatter plots, box plots, and multi-subplot dashboards to examine temperature, humidity, and pressure across days, months, years, hours, and cities.

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Pandas NumPy seaborn matplotlib

National Health Insurance Trends & Determinants Analytics System (Australia, 2007–2025)

Performs a full-scale analysis of Australia’s Private Hospital Insurance (PHI) ecosystem by importing multi-sheet Excel datasets, repairing inconsistent formats, harmonizing variables, constructing age and product-mix cohorts, and analyzing long-term membership trends pre-COVID, during COVID, and post-COVID. Also builds a complete micro-level logistic regression model using demographic and socioeconomic features to estimate odds of PHI uptake, including VIF diagnostics, ROC-AUC evaluation, marginal effects, and synthetic scenario simulations.

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Python pandas NumPy matplotlib

Consumer Perception & Social Media Sentiment Intelligence

Analyzes qualitative survey responses and social media posts to uncover brand associations, product perceptions, and novelty cues. The workflow includes text cleaning, tokenization, frequency analysis, thematic classification, word clouds, sentiment scoring using VADER, location-based segmentation, and comparison of novelty/exclusivity keyword usage across customer groups. It also builds respondent-level theme flags and cross-segment insights for marketing strategy.

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