-
Project write-ups can be found at Medium
Predicted customer’s next purchases through market basket analysis and NLP
Utilized AWS instance to process 32 million observations from the InstaCart open dataset. Uncovered customer and product relationships through extensive exploratory data analysis. Predicted purchasing pattern based on the association rules formed through MBA. Recommended promotions of products based on customer's buying frequencies. Developed a Flask application and deployed it with Heroku
Recommended users with better and relevant restaurants through content-based filtering
Built a content-based recommender using Yelp's open dataset (managed data via MongoDB). Utilized text preprocessing techniques such as TF-IDF, stemming, and lemmatization. Performed dimensionality reduction for topic modeling using Latent Semantic Analysis. Used Cosine Similarity as a comparison metric to match user queries to relevant results. Developed a Flask application and deployed it using the Heroku platform
Predicted if a user will make a reservation after visiting the site for the very first time
Utilized supervised machine learning models such as Logistic, KNN, Bayesian, RandomForest, XGBoost, and CatBoost to determine if a new user would make a reservation within 5 days after visiting the site. Insights on user behavior led to making strategic recommendations for a marketing campaign. Developed and deployed a web app using Flask and hosted it with Heroku.
Predicted hotel listings price in Las Vegas and determined if you are being offered a deal
Scraped over 3 months of hotel listing data using BeautifulSoup and Selenium. Trained, tuned, and cross-validated a regularized linear regression model to predict the hotel listing price. Exploited algorithms such as Lasso, Ridge, ElasticNet, and GridSearch for model optimization. Utilized the model to predict future hotel listing price to determine if you are being offered a deal.
Developed tourist and commuter index by conducting diagnostic on NYC's subway traffic flow. Determined the most impactful subway stations based on the time and day of the week. Provided actionable insights and recommendations for event promoters based on their business goals and deliverables.