This is a multiple linear regression programming assignment wherein we have to build a multiple linear regression model for the prediction of demand for shared bikes.
- Bike-sharing lets you rent bikes from stations (docks), pay, and unlock them with a computer system. You can return the bike to any other station in the network.
- BoomBikes, a US bike-sharing company, is struggling due to COVID-19's revenue slump. To survive, they're crafting a plan to boost income once restrictions ease and the economy recovers.
- The task involves developing a model to predict shared bike demand using various independent variables. This model will help management understand how demand fluctuates with different factors, allowing for strategic adjustments to align with demand and customer expectations. Additionally, it offers insights into demand dynamics in new markets, aiding in strategic planning and market understanding.
- The raw data is given as a CSV files which contains 730 rows and 16 columns. It also accompany data dictionary in text format
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- yr : indicates a significant year-on-year increase in bike rental demand, reflecting possibly the growing popularity of the bike-sharing service.
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- temp : Higher temperatures are positively associated with increased bike rental demand, likely due to more favorable weather conditions for biking.
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- atemp (Feeling temperature in Celsius): Similar to temp, the 'feels like' temperature also positively influences bike rental demand, indicating that people's perception of comfortable weather conditions plays a significant role in their decision to rent bikes.
- Python - version 3.10.9
- scikit-learn - version 1.2.1
- statsmodels - version 0.13.5
- Pandas - version 1.5.3
- Numpy - version 1.23.5
- Seaborn - version 0.12.2
- Matplotlib -version 3.7.0
- conda - version 23.3.1
- This project is based on Bike Sharing Case Study by upGrad
Created by [@mo-arindam] - feel free to contact me!