Train delay prediction - Difficulty: EASY

Problem Description

🚆 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).

🎯 Problem goal ⏱️

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

📝 Submission format

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

Python
1SampleID,delay_minutes20,1231,342,15

Where:

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

📊 Evaluation

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

$MAE$

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.

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