[{"data":1,"prerenderedAt":105},["ShallowReactive",2],{"page-\u002Fprojects":3},{"id":4,"title":5,"body":6,"date":97,"description":94,"extension":98,"meta":99,"navigation":100,"path":101,"seo":102,"stem":5,"subtitle":103,"tags":97,"__hash__":104},"content\u002Fprojects.md","projects",{"type":7,"value":8,"toc":93},"minimark",[9,13,17,34,37,64,67,90],[10,11],"terminal-prompt",{"cmd":12},"ls -la .\u002Fprojects",[14,15,16],"p",{},"total 2",[18,19,20,28],"ul",{},[21,22,23,27],"li",{},[24,25,26],"code",{},"peroxide-ml.md","   multi-stage XGBoost for chemical safety · Dow + The Data Mine",[21,29,30,33],{},[24,31,32],{},"tweet-sent.md","    tweet sentiment with Spark Streaming · big data project",[10,35],{"cmd":36},"cat peroxide-ml.md",[38,39,43,49],"terminal-callout",{"id":40,"label":41,"tone":42},"peroxide-ml","time-sensitive chemical identification tool","info",[14,44,45],{},[46,47,48],"strong",{},"Dow · The Data Mine (TDM 511)",[18,50,51,58,61],{},[21,52,53,54,57],{},"Worked with corporate mentors to develop a ",[46,55,56],{},"multi-stage XGBoost model"," predicting chemical peroxide formation, using feature engineering on ionic charges and molecular weights paired with systematic feature selection",[21,59,60],{},"Collected and preprocessed data on 300+ chemicals",[21,62,63],{},"Deployed the model as a web application in R Shiny for non-technical users",[10,65],{"cmd":66},"cat tweet-sent.md",[38,68,71,76],{"id":69,"label":70,"tone":42},"tweet-sent","tweet sentiment analysis · spark + hadoop",[14,72,73],{},[46,74,75],{},"Big Data Project",[18,77,78,81,84,87],{},[21,79,80],{},"Conducted sentiment analysis on streaming tweets using Spark Streaming and PySpark",[21,82,83],{},"Trained multiple machine-learning models (Logistic Regression, Naive Bayes, SVM) for sentiment classification",[21,85,86],{},"Implemented K-means clustering for data segmentation",[21,88,89],{},"Optimized model performance through hyperparameter tuning and cross-validation",[10,91],{"cmd":92},"exit",{"title":94,"searchDepth":95,"depth":95,"links":96},"",2,[],null,"md",{},true,"\u002Fprojects",{"title":5,"description":94},"ml pipelines · streaming · big data","PsvaW91P8gEpRQUWsWRfplzW8C1T-jZBA6UAnUe9XtI",1782496776467]