An interactive R Markdown analysis exploring patterns in subscriber count, content category, and geography — powered by bootstrap regression and statistical testing.

📌 Featured Visual:

Highlights:

• 🎶 Entertainment and Music lead in both views and subscribers.
• 📉 More uploads ≠ more subscribers — the relationship is weak and nonlinear.
• 🌍 U.S. and India dominate the top channel landscape.

🔗 View GitHub Repo 📄 Read the Report on RPubs

An interactive dashboard project analyzing over 50,000 Boston crime records using Power BI and Excel. Focused on uncovering crime hotspots, temporal patterns, and offense trends to support public safety decisions.

📌 Featured Visual:

Highlights:

• 🕓 Crimes are most frequent between 10 AM – 10 PM, peaking around 4 PM.
• 🔍 Common offenses include larceny, assault, and drug violations.
• 🗺️ Maps and filters help identify high-risk neighborhoods and time zones.

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This project analyzes Spotify’s 2024 streaming data to classify tracks as explicit or not. The analysis involved data cleaning, EDA, and applying supervised learning models — all performed in Python.

📌 Featured Visual:

Highlights:

• ⚖️ Addressed class imbalance using Balanced Random Forest and baseline Logistic Regression.
• 📊 Included ROC curve evaluation and comparison between models.
• 🧹 Comprehensive preprocessing and EDA to understand feature distributions.

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An end-to-end time series modeling project using manual ARIMA tuning and backward stepwise regression to forecast Malaysia’s Consumer Price Index (CPI). The final model was evaluated against actual CPI values from Sep 2023–Aug 2024.

📌 Featured Visual:

Highlights:

• 🔍 Built ARIMA(1,1,2) model manually with statistical testing (ADF, KPSS).
• 📉 Included backward stepwise regression with lag features to optimize prediction.
• 📊 Compared forecast results against actual CPI for accuracy assessment.

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A lighthearted NLP project exploring Reddit sentiment toward a midweek break. Using over 1,100 comments from a viral post on r/unpopularopinion, the analysis combines scraping, sentiment scoring, and quirky word clouds to uncover what the internet really thinks of #MidweekReset.

📌 Featured Visual:

Highlights:

• 🧼 Full NLP pipeline: text cleaning, tokenizing, stopword removal.
• 🧠 VADER sentiment scoring with negation-aware processing.
• 📊 Violin plots and word clouds reveal emotional tones and common themes.
• 🔍 Top-voted comments skewed positive or neutral, supporting midweek reset.

🔗 View GitHub Repo

A structured SQL and Tableau project analyzing Major League Baseball (MLB) player data from Maven Analytics. The SQL portion focuses on player origins, salaries, and career spans using CTEs, window functions, and aggregation techniques. Tableau brings the findings to life through interactive visuals.

📌 Featured Visual:

Highlights:

• 🧮 Applied ROW_NUMBER, NTILE, and LAG to analyze debut/final games and career lengths.
• 🏫 Identified top schools producing high-earning, long-career MLB players.
• 💸 Explored team salary distributions and milestone thresholds ($1B+ in salary spend).
• 📊 Built a Tableau dashboard featuring:
  • Treemap of top 10 salary-producing schools
  • Funnel from schools to $5M+ players
  • Scatter plot of career span vs. school salary
  • Choropleth map of player origins by state
  • Bar chart of top schools by career longevity

🔗 View Project