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AutoGluon Regression

Overview

Uses Autogluon to build, train, and test a variety of machine learning models to predict the number of casual users from a bike rental usage dataset.

Dataset

  • Dataset File: bike_sharing_daily.csv
  • Dataset Source: Hadi Fanaee-T, Laboratory of Artificial Intelligence and Decision Support (LIAAD), University of Porto INESC Porto, Campus da FEUP Rua Dr. Roberto Frias, 378 4200 - 465 Porto, Portugal

Inputs

  • instant: record index
  • dteday: date
  • season: season (1: springer, 2: summer, 3: fall, 4: winter)
  • yr: year (0: 2011, 1: 2012)
  • mnth: month ( 1 to 12)
  • hr: hour (0 to 23)
  • holiday: whether day is holiday or not - weekday : day of the week
  • Working day: if day is neither weekend nor holiday is 1, otherwise is 0.
  • weathersit:
    • 1: Clear Few clouds, Partly cloudy
    • 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist
    • 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds
    • 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog
  • temp : Normalized temperature in Celsius. The values are divided to 41 (max)
  • windspeed: Normalized wind speed. The values are divided to 67 (max)

Outputs

  • casual: count of casual users

Notebook

  • Load the dataset and perform basic exploratory data analysis
  • Split the data into training and testing sets
  • Use AutoGluon to train linear regression models
  • Evaluate performance of trained models

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