Problem Statement: A bike-sharing system is a service in which bikes are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a "dock" which is usually computer-controlled wherein the user enters the payment information, and the system unlocks it. This bike can then be returned to another dock belonging to the same system. A US bike-sharing provider BoomBikes has recently suffered considerable dips in their revenues due to the ongoing Corona pandemic. The company is finding it very difficult to sustain in the current market scenario. So, it has decided to come up with a mindful business plan to be able to accelerate its revenue as soon as the ongoing lockdown comes to an end, and the economy restores to a healthy state. In such an attempt, BoomBikes aspires to understand the demand for shared bikes among the people after this ongoing quarantine situation ends across the nation due to Covid-19. They have planned this to prepare themselves to cater to the people's needs once the situation gets better all around and stand out from other service providers and make huge profits. They have contracted a consulting company to understand the factors on which the demand for these shared bikes depends.
Requirement: Which variables are significant in predicting the demand for shared bike How well those variables describe the bike demands.
What we need to do: Create a linear model that describe the effect of various features on demand The model should be interpretable so that the management can understand it
Problem Statement: A bike-sharing system is a service in which bikes are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a "dock" which is usually computer-controlled wherein the user enters the payment information, and the system unlocks it. This bike can then be returned to another dock belonging to the same system. A US bike-sharing provider BoomBikes has recently suffered considerable dips in their revenues due to the ongoing Corona pandemic. The company is finding it very difficult to sustain in the current market scenario. So, it has decided to come up with a mindful business plan to be able to accelerate its revenue as soon as the ongoing lockdown comes to an end, and the economy restores to a healthy state. In such an attempt, BoomBikes aspires to understand the demand for shared bikes among the people after this ongoing quarantine situation ends across the nation due to Covid-19. They have planned this to prepare themselves to cater to the people's needs once the situation gets better all around and stand out from other service providers and make huge profits. They have contracted a consulting company to understand the factors on which the demand for these shared bikes depends.
Requirement: Which variables are significant in predicting the demand for shared bike How well those variables describe the bike demands.
What we need to do: Create a linear model that describe the effect of various features on demand The model should be interpretable so that the management can understand it
Dataset:
day.csv has the following fields:
- instant: record index
- dteday : date
- season : season (1:spring, 2:summer, 3:fall, 4:winter)
- yr : year (0: 2018, 1:2019)
- mnth : month ( 1 to 12)
- holiday : wheather day is a holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule)
- 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 : temperature in Celsius
- atemp: feeling temperature in Celsius
- hum: humidity
- windspeed: wind speed
- casual: count of casual users
- registered: count of registered users
- cnt: count of total rental bikes including both casual and registered
Dataset(day.csv) License [1] Fanaee-T, Hadi, and Gama, Joao, "Event labeling combining ensemble detectors and background knowledge", Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg, doi:10.1007/s13748-013-0040-3.
@article{ year={2013}, issn={2192-6352}, journal={Progress in Artificial Intelligence}, doi={10.1007/s13748-013-0040-3}, title={Event labeling combining ensemble detectors and background knowledge}, url={http://dx.doi.org/10.1007/s13748-013-0040-3}, publisher={Springer Berlin Heidelberg}, keywords={Event labeling; Event detection; Ensemble learning; Background knowledge}, author={Fanaee-T, Hadi and Gama, Joao}, pages={1-15} }
- python - version 3.9.13
- numpy - version 1.22.2
- pandas - version 1.4.1
- matplotlib - version 3.5.1
- seaborn - version 0.11.2
- sklearn - version 1.1.1
- statsmodels - version 0.13.2
Equation of best fitting line would be:
cnt = 0.1350 + 0.2327 * year - 0.1072 * holiday + 0.5471 * temp - 0.1531 * windspeed + 0.0866 * summer + 0.1323 * winter + 0.0992 * sep - 0.0500 * sun -0.2892 * Light_snow_rain -0.0819 * Misty
Significant features in predicting the demand for shared bikes:
-year
-holiday
-temp
-wind speed
-season: summer
-season: winter
-month: september
-day: sunday
-weather situation: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds
-weather situation: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, MistMisty
This project was inspired by Linear Regression Assignment as part of Data Science course. This project was based on: https://learn.upgrad.com/course/1991/segment/16594/129672/396956/2065942
Created by [@rahul2july] - feel free to contact me!
Contributor -Rahul Gupta