Daily Average Temperature Prediction

🌡️ Daily Average Temperature Prediction

Problem Description

The goal is to build a regression model that predicts the daily average temperature in a certain city, based on meteorological characteristics.

Each record is characterized by the following attributes:

• humidity – relative humidity (%)
• wind_speed – wind speed (km/h)
• pressure – atmospheric pressure (hPa)
• rainfall – precipitation amount (mm)
• cloud_cover – cloud coverage (%)
• solar_radiation – solar radiation (W/m²)
• day_of_year – day of the year (1–365)

The label (target column) is:

• temperature – daily average temperature (°C)

This problem belongs to the continuous regression category.

📘 Input File Structure

train.csv

Contains all feature columns plus the target column temperature.
Values are continuous numeric.

test.csv

Contains the same columns without temperature, but includes SampleID.

📤 Submission

The output file (submission.csv) must contain exactly two columns:

• SampleID
• temperature – the value predicted by the model (float, °C)

⚙️ Evaluation

Model evaluation will be done using Root Mean Squared Error (RMSE):

where:

• N is the number of examples in the test set
• y_i is the actual temperature value
• ŷ_i is the value predicted by the model

RMSE provides a measure of the mean squared deviation between predictions and actual values, expressed in the same unit as the target (°C).
A lower RMSE score indicates a more accurate model.

📊 Source

The dataset is a synthetic set inspired by real meteorological data.