Welcome to my GitHub page!

MS in Statistics | BS in Mathematics and Statistics

Here are some of my notable projects:

• Clustering Spotify Podcasts with NLP-Driven Insights

  • Scraped $\approx$ 284,481 episode details from 818 podcasts using Selenium and Spotify API pipeline.
  • Preprocessed and tokenized podcast descriptions with NLTK, including lemmatization and stopword removal.
  • Developed metrics to quantify directional, overlap, diversity similarities, and engineered recommendation system.
  • Deployed a Dash app for podcast clustering and personalized recommendations.

• Predicting Flight Delays and Cancellations: An Integrated Analysis of Airport Data and Weather Data

  • Automated scraping of 23 GB airport and 30 GB weather data using Selenium.
  • Utilized reverse geocoding, Haversine-based, and UTC-normalized alignments to join datasets.
  • Trained random forest models, achieving $\approx$ 25 min test RMSE for delays and $\approx$ 98% test accuracy for cancellations.
  • Developed scalable workflows on Google Cloud, and deployed interactive web-app using Dash.

• Modeling and Forecasting Walmart Stock Prices: A Comparative Analysis of ARMA and GARCH Approaches

  • Leveraged ARIMA and GARCH models using tseries and fGarch in R to analyze Walmart stock price volatility.
  • Developed and validated ensemble models through residual diagnostics and forecast evaluation.
  • Achieved RMSE of $\approx$ $ 0.01 for log-returns and $\approx$ $ 1.17 for closing prices on unseen 10-day forecast and actual prices.

• Statistical Modeling and Deployment of Body Fat Percentage Prediction System

  • Implemented anomaly detection and imputation strategy using prior body fat estimation model.
  • Constructed Stepwise regression model with Goodness of Fit and Holm-Bonferroni F-tests to control Type I errors.
  • Developed Multiple Linear Regression model (R-squared 0.6592 and RMSE 4.38) with residual diagnostics.
  • Deployed an interactive Dash app with comprehensive explanations, detailed visuals, and predictions.

• Outlier Detection and De-noising for Audio-Based Neural Network Language Classification

  • Developed an ensemble outlier detector using Isolation Forest and Local Outlier Factor in Scikit-Learn.
  • Designed a speech detection and spectral gating algorithm with Librosa and NoiseReduce for noise suppression.
  • Processed 1320 parallel jobs for anomaly detection and de-noising using Linux Bash and HTCondor.
  • Trained a preliminary CNN on a random sample in TensorFlow, improving test accuracy by 2% and AUC by 4%.

• Finding Lyman Break Galaxy cB58 Resemblances Using High-Throughput Computing

  • Identified noisy spectra matching Lyman Break Galaxy cB58 from Sloan Digital Sky Survey (SDSS) datasets.
  • Computed similarity between spectra using distance metrics implemented in R.
  • Processed 2459 parallel jobs over 281 GB data using Linux Bash and HTCondor.

• Supervised machine learning model to predict gender based on first names

  • Performed feature pre-processing and one-hot alphabet encoding using NumPy and Pandas.
  • Implemented an ensemble gradient boosting model with fine-tuned hyper-parameters using Scikit-Learn.
  • Achieved approximately 80% in accuracy, precision, recall, and AUC metrics.