https://tanvipatil.journoportfolio.comRSS Feed for Tanvi PatilenTue, 02 Apr 2024 00:00:00 +0100https://tanvipatil.journoportfolio.com/articles/skills/Tue, 02 Apr 2024 00:00:00 +0100https://tanvipatil.journoportfolio.com/articles/skills/https://github.com/tanvi2804/AN-ANALYSIS-AND-RESEARCH-ON-THE-DAILY-ACTIVITIES-AND-SLEEP-SCHEDULE-OF-WOMENThis project aims to analyze smart device data to gain insights into how women are using their smart devices by tracking their movements and sleep schedules. Projects include developing a predictive model for home loan approvals and conducting an analysis on the daily activities and sleep schedules of women using smart device data. My work involves data collection, preprocessing, feature selection, and model evaluation using algorithms such as logistic regression, decision trees, and random forests. I am passionate about leveraging data to solve real-world problems and improve decision-making processes.Tue, 02 Apr 2024 00:00:00 +0100https://github.com/tanvi2804/AN-ANALYSIS-AND-RESEARCH-ON-THE-DAILY-ACTIVITIES-AND-SLEEP-SCHEDULE-OF-WOMENhttps://github.com/tanvi2804/Churn-Classification.gitWe explored random forest, decision tree, and logistic regression algorithms for model building. The random forest model emerged as the most promising, achieving 67% accuracy and an AUC score of 0.727. This ensemble method reduced overfitting and increased predictive power, making it suitable for our predictive modeling assignment.Tue, 02 Apr 2024 00:00:00 +0100https://github.com/tanvi2804/Churn-Classification.githttps://github.com/tanvi2804/HOME-LOAN-APPROVAL-PREDICTIONProject involved developing a predictive model for home loan approvals, utilizing techniques such as logistic regression, decision trees, and random forests. Our Decision Tree model achieved the highest accuracy in predicting loan approvals, demonstrating my ability to effectively apply data science methods to real-world problems.Tue, 02 Apr 2024 00:00:00 +0100https://github.com/tanvi2804/HOME-LOAN-APPROVAL-PREDICTIONhttps://github.com/tanvi2804/Analysis-of-Hydrocarbon-Emissions-from-Ships-at-a-Port-Insights-and-Predictive-ModelingDeveloped machine learning models to predict port emissions from ship characteristics and operational data. Utilized regression techniques such as Linear Regression, Random Forest, and XGBoost, and employed TPOT for automated model selection. Evaluated model performance using MSE, RMSE, and R-squared metrics.Tue, 02 Apr 2024 00:00:00 +0100https://github.com/tanvi2804/Analysis-of-Hydrocarbon-Emissions-from-Ships-at-a-Port-Insights-and-Predictive-Modelinghttps://github.com/tanvi2804/Transformative-Potential-of-Large-Language-models-in-HealthcareThis research project delves into the application of Large Language Models (LLMs) in healthcare, focusing on their potential to revolutionize patient care, clinical documentation, and medical research.Tue, 02 Apr 2024 00:00:00 +0100https://github.com/tanvi2804/Transformative-Potential-of-Large-Language-models-in-Healthcarehttps://github.com/tanvi2804/Transformative-Potential-of-Large-Language-models-in-HealthcarerdtuygiuhjijijijhuhTue, 02 Apr 2024 00:00:00 +0100https://github.com/tanvi2804/Transformative-Potential-of-Large-Language-models-in-Healthcarehttps://media.journoportfolio.com/users/383426/uploads/66181b8e-72c8-44c5-9e27-00c22f73e5de.pdfTue, 02 Apr 2024 00:00:00 +0100https://media.journoportfolio.com/users/383426/uploads/66181b8e-72c8-44c5-9e27-00c22f73e5de.pdf