The problem CarPool Connect solves

Optimal Routing: Utilized Mapping API for efficient route planning, minimizing travel time for both drivers and users.
Collaborative Filtering: Implemented collaborative filtering to match users with similar preferences and commuting patterns, enhancing the carpooling experience.
Efficiency: Our approach ensures route optimization and compatible carpooling partners, making commuting efficient and enjoyable.
Enhanced Experience: By combining optimal routing and collaborative filtering, we offer a seamless and personalized carpooling solution.
Safety Assurance: Implemented in-app communication and emergency alert system for user safety and assistance.
Fair Fare Calculation: Utilized machine learning for transparent fare calculation considering distance, fuel costs, and occupancy.

Challenges we ran into

Mapping API Integration: Integrating the Mapping API posed initial challenges, as it required careful configuration and handling. Ensuring optimal routes for both drivers and users was crucial, and resolving technical intricacies took time and effort.
Data Collection: Gathering comprehensive data for user preferences, commuting patterns, and peak-hour predictions proved to be a hurdle. To address this, we had to generate synthetic data to train our machine learning models effectively.
Algorithm Development: Developing machine learning algorithms for fare calculation and user matching was a complex task. We encountered hurdles in fine-tuning these algorithms to provide accurate results.