My Machine Learning Portfolio 🔥

Projects 🚀

Analysis 📊🔍

Keywords: Pandas, Numpy, Matplotlib, Seaborn

• Exploratory Data Analysis on MTA Turnstile Data: Used Pandas, Matplotlib/Seaborn to clean, transform and visualize data to extract insights.

Regression 📈

Keywords: scikit-learn, Linear Regression, Logistic Regression, Beautiful Soup

• IMDb Movies Gross Prediction: Used BeautifulSoup to collect and clean job listing data from IMDb.com

• Forcasting Historical Sales Data: This was a case study that I’ve worked on that relies solely on previeos sale numbers.

Classification 🍎🍏

Keywords: scikit-learn, Random Forest, Flask, SQL Alchemy

• Rain Prediction: Predictied next-day rain using Australia’s Weather data. Imported data to a database, connected with SQLAlchemy and deployed using Flask.

Clustering 🌌

Keywords: NLTK, Gensim, PyMongo, scikit-learn, seaborn, spacy, Selenium, Beautiful Soup

• NLP with LDA: Topic Modeling: Implemented topic modeling using Latent Dirichlet Allocation (LDA) on food review data

• Steam Games’ Recommendation System: Built a game recommendation system using clustering techniques on Steam data