Predict Mean Temperature


🛠 Contribute: Found a typo? Or any other change in the description that you would like to see? Please consider sending us a pull request in the public repo of the challenge here.

🕵️ Introduction

Up for a magic trick? Want us to conjure a challenge out of thin air? Well this time we may just have done that, literally!, Feel the magic in the air.

Given some non-temperature based information for weather conditions on a given day, can you predict the mean temperature ?

Understand with code! Here is getting started code for you.😄

💾 Dataset

This data contains the weather information of Izmir region from 01/01/1994 to 31/12/1997. From given features, the goal is to predict the mean temperature. There will be 9 attributes that shall be provided to you and you are required to predict the mean temperature.

The 9 attributes are :

  • Max_temperature: range[36.7,105.0]

  • Min_temperature: range[15.8,78.6]

  • Dewpoint: range[13.6,64.4]

  • Precipitation: range[0.0,7.6]

  • Sea_level_pressure: range[29.26,30.48]

  • Standard_pressure: range[2.3,10.1]

  • Visibility: range[0.92,29.1]

  • Wind_speed: range[4.72,68.8]

  • Max_wind_speed: range[16.11,55.24]

  • Mean_temperature: range[29.4,89.9] [target]

For simplification, attributes have been stored in the CSV file. The train.csv has 10 columns, the last column is the mean_temp.

📁 Files

Following files are available in the resources section:

  • train.csv - (1168 samples) This csv file contains the attributes describing an day conditions of the place along with the mean temperature.

  • test.csv - (293 samples) File that will be used for actual evaluation for the leaderboard score but does not have the value of the mean temperature.

🚀 Submission

  • Prepare a CSV containing header as mean_temp and predicted values of the mean temperatures.
  • Name of the above file should be submission.csv.
  • Sample submission format is available in the resources section of the challenge page as sample_submission.csv.

Make your first submission here 🚀 !!

🖊 Evaluation Criteria

During evaluation Mean Absolute Error and Root Mean Squared Error will be used respectively.

🔗 Links

📱 Contact

📚 References


Getting Started


01 adithyasunil26 0.847
02 GiveMeData 0.885
03 andreidore 0.919
04 srinath_nair 0.923
05 ashivani 0.926