Intern
Almabetter,
Nov 2023 - Current (2 years 9 months)
• Identified demand patterns, temporal trends, and environmental influences through comprehensive EDA. • Developed and optimized machine learning models, such as Random Forest, Decision Tree, and XG Boost, achieving a training accuracy of 76% for predicting bike demand in urban settings. • Utilized skills in EDA, machine learning, model training, urban environment analysis, and regression algorithms. • Skills: EDA, ML, Model Training, Urban Environment Analysis, Regression Algorithms