This study presents the development of the Transit Analytics Lab Electric Bus (TALe-Bus) Dashboard, an integrated decision-support tool designed to support transit agencies in planning the transition from conventional diesel buses to battery-electric buses. The dashboard combines predictive modeling, data analytics, and interactive visualization to estimate electric bus energy consumption rates, fleet size requirements, replacement factors, and maximum operational range for the case of overnight depot charging. The dashboard was built based on real-world data collected from the operations of 60 battery-electric buses on 48 routes in Toronto. The modeling workflow includes data preprocessing, feature selection, and the development and comparison of multiple statistical and machine learning energy prediction models. Tree-based modeling techniques outperformed the other techniques, demonstrating strong capability in capturing the relationships between operational, environmental, vehicle, and route characteristics and the energy consumption rate, achieving a root mean square error (RMSE) of 0.13 kWh/km. These techniques were therefore adopted as the core predictive engine of the system. The fleet-sizing module integrates traditional transit planning formulations (for diesel fleets) with electric-bus energy and range constraints to estimate both electric and diesel fleet requirements and compute the replacement factor, a key indicator reflecting the relative fleet needs for electrification. The dashboard also estimates the maximum operational bus range based on the battery capacity and real-world operating conditions, supporting reliable service planning. The system is implemented as an interactive web-based platform that provides geospatial visualization of route electrification feasibility and a scenario-based interface for customized operational analysis. By translating complex predictive analytics into a practical planning tool, the TALe-Bus Dashboard supports informed, data-driven decision-making for fleet electrification and infrastructure planning.