🚆 Train delay prediction ⏱️

🇷🇴 Context 🚃

The national railway company 🇷🇴 wants to predict the delay of a train (in minutes, integer) at its final station. For this purpose, you are provided with a dataset containing details of train trips over the past year.

For each trip, the following characteristics are known:

Name	Type	Description
SampleID	int	Unique identifier for the sample
departure_time	string (HH:MM)	Train departure time
distance_km	float	Total distance of the route
avg_speed_kmh	float	Actual average speed
num_stops	int	Number of intermediate stops
weather	category	Weather conditions: sunny, rain, snow, fog
weekday	category	Day of the week
special_events	0/1	Exceptional events on the route
num_cars	int	Number of train cars
ticket_price	float	Ticket price
comfort_class	category	standard, intermediate, premium
delay_minutes	int	Target variable – train delay in minutes

The delay_minutes information is only available in the training set (train.csv).

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🎯 Problem goal ⏱️

You need to train a model capable of predicting delay_minutes based on the other features.

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📝 Submission format

You must submit a CSV file (submission.csv) with the following format:

SampleID,delay_minutes
0,12
1,3
2,15

Where:

• SampleID must match the values in test.csv
• delay_minutes is your model's prediction, rounded to the nearest integer

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📊 Evaluation

The evaluation will be based on MAE (Mean Absolute Error):

The final score is calculated according to the following rules:

• MAE ≤ 5 → 100 points
• MAE ≥ 20 → 0 points
• Intermediate values receive a proportional score between 0 and 100.