The problem NidarSafar solves

1. Inefficient Management of Accident Data
   The current method of accident data management is inefficient, resulting in delays and inaccurate reporting.

2. Lack of Real-time Accident Severity Prediction
   The absence of a reliable and efficient system to predict the severity of accidents in real-time is a major challenge in ensuring timely emergency services and medical aid to accident victims.

3. Lack of effective tools to help commuters plan safely.
   The lack of information about accident-prone areas or severe accidents can lead to delays in travel and put the user's safety at risk. This can lead to delays and put their safety at risk.

This is our proposed solution:

1. Machine Learning Model
   Develop an accurate machine learning model that predicts accident severity based on various features such as location, time, weather, and road conditions.

2. Real-Time Alert System
   Develop a real-time alert system that notifies users of potential accidents in their vicinity, based on the predictions made by the machine learning model.

3. Trip Planning Feature
   Provide users with a trip planning feature that suggests the safest route to their destination based on the accident severity predictions and real-time alerts.

Challenges we ran into

The major challenge for us was finding a dataset for the predictive analysis. Since, there is no particular dataset of India on which our ML model can make predictions, thus it was very hard for us to showcase the severity of nearby areas.