Data Set Information:

Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues.

Apart from interesting real world applications of bike sharing systems, the characteristics of data being generated by these systems make them attractive for the research. Opposed to other transport services such as bus or subway, the duration of travel, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of important events in the city could be detected via monitoring these data.

1. What is the problem you are trying to address?

Answer:

The goal of this problem is to predict the count of total rental bikes (“cnt” variable) using the other variables in the dataset.

2. What dataset(s) will you be using? how many variables do you have in the dataset ? If you are using a public dataset, please provide a link to the dataset.

Answer:

This is Bike sharing dataset and download the dataset using below link.
Dataset link: https://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset#

Dataset has 730 rows and 16 columns. These columns description given below below.

  • instant: record index
  • dteday : date
  • season : season (1:winter, 2:spring, 3:summer, 4:fall)
  • yr : year (0: 2011, 1:2012)
  • mnth : month ( 1 to 12)
  • hr : hour (0 to 23)
  • holiday : weather day is holiday or not (extracted from [Web Link])
  • weekday : day of the week
  • workingday : if day is neither weekend nor holiday is 1, otherwise is 0.
  • weathersit :
    1: Clear, Few clouds, Partly cloudy, 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 derived via (t-t_min)/(t_max-t_min), t_min=-8, t_max=+39 (only in hourly scale)
  • atemp: Normalized feeling temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-16, t_max=+50 (only in hourly scale)
  • hum: Normalized humidity. The values are divided to 100 (max)
  • windspeed: Normalized wind speed. The values are divided to 67 (max)
  • casual: count of casual users
  • registered: count of registered users
  • cnt: count of total rental bikes including both casual and registered

3. What type of model (classification, regression, clustering) will you be using to solve the problem in question 1. If you are using a classification or regression, explain what is the outcome variable that you are trying to predict/classify?

Answer:
The predicted variable is numeric so this is going to be a regression model. Here the outcome variable is “cnt” i.e count of total rental bikes and rest others are independant/input variables.

4. Please specify the name of the team members. Please make only one submission per team and both the team members will receive grade for the post.

Answer:
We are from group 6 and below given team members name:

  • Venkata Naga Vamsidhar reddy karasani(vkara4)
  • Anila Cheekati(vchee3)
  • Venkata sai ram tirunagari(Vtiru5)
  • Pradeep kumar Naidu(Pnaid2)
  • Simhadri Ramanjaneyulu(rsimh3)
  • Subhalaxmi Rout(srout2)