The annual bike and pedestrian count is a volunteer data collection effort each fall that helps the City understand where and how many people are biking and walking in Somerville, and how those numbers are changing over time. This program has been taking place each year since 2010. Counts are collected Tuesday, Wednesday, or Thursday for one hour in the morning and evening using a “screen line” method, whereby cyclists and pedestrians are counted as they pass by an imaginary line across the street and sidewalks. Morning count sessions begin between 7:15 and 7:45 am, and evening count sessions begin between 4:45 and 5:15 pm. Bike counts capture the number of people riding bicycles, so an adult and child riding on the same bike would be counted as two counts even though it is only one bike. Pedestrian counts capture people walking or jogging, people using a wheelchair or assistive device, children in strollers, and people using other micro-mobility devices, such as skateboards, scooters, or roller skates. While the City and its amazing volunteers do their best to collect accurate and complete data each year and the City does quality control to catch clear errors, it is not possible to ensure 100% accuracy of the data and not all locations have been counted every year of the program. There are also several external factors impacting counts that are not consistent year-to-year, such as nearby construction and weather. For these reasons, the counts are intended to be used to observe high-level trends across the city and at count locations, and not to extrapolate that biking and walking in Somerville has changed by a specific percentage or number. Data in this dataset are available at the location count level. To request data at the movement level, please contact transportation@somervillema.gov.

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. Specifically, they want to understand the factors affecting the demand for these shared bikes in the American market. The company wants to know:

Which variables are significant in predicting the demand for shared bikes. How well those variables describe the bike demands Based on various meteorological surveys and people's styles, the service provider firm has gathered a large dataset on daily bike demands across the American market based on some factors.

Business Goal: You are required to model the demand for shared bikes with the available independent variables. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels and meet the customer's expectations. Further, the model will be a good way for management to understand the demand dynamics of a new market.

Dataset characteristics: day.csv have 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 : weather 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

Bike sharing systems are a means of renting bicycles where the process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city. Using these systems, people are able to rent a bike from one location and return it to different place on an as-needed basis. Currently, there are over 500 bike-sharing programs around the world.

The data generated by these systems makes them attractive for researchers because the duration of travel, departure location, arrival location, and time elapsed is explicitly recorded. Bike sharing systems therefore function as a sensor network, which can be used for studying mobility in a city.

Problem Statement

In this project, you are asked to combine historical usage patterns with weather data in order to forecast hourly bike rental demand.

Data

You are provided with following files:

1. train.csv : Use this dataset to train the model. This file contains all the weather related features as well as the target variable “count”. Train dataset is comprised of first 18 months.
2. test.csv : Use the trained model to predict the count of total rentals for each hour during the next 6 months.

Data Dictionary

Here is the description of all the variables :

How good are your predictions?

Evaluation Metric

The Evaluation metric for this project is Root Mean Squared Logarithmic Error (RMSLE)

Solution Checker

You can use solution_checker.xlsx to generate score (RMSLE) of your predictions. This is an excel sheet where you are provided with the timestamp and you have to submit your predictions in the count column. Below are the steps to submit your predictions and generate score:

a. Save the predictions on test.csv file in a new csv file. b. Open the generated csv file, copy the predictions and paste them in the count column of solution_checker.xlsx file. c. Your score will be generated automatically and will be shown in Your Score column.

European cyclists bought around **** million bicycles (including electric battery-powered bicycles) in 2024. This figure represents a year-on-year fall from the roughly ** million units that were sold in 2023. Pandemic boom in cycling falters The rise in bicycle sales in the European Union in 2020 and 2021 was largely driven by the impacts of the COVID-19 pandemic. As many other recreational activities were restricted during this period and public health authorities discouraged the use of shared transport, cycling became a popular mode of exercise and transport. This was supported by national and local governments, who invested in additional cycling infrastructure. However, easing of pandemic restrictions, coupled with supply chain problems in 2022, heavily affected the sales of bikes that year and bicycle sales have continued to drop in 2023 and 2024. Sales are projected to continue falling, declining to **** million units by 2029. E-Bike market share increasing While total bike sales dropped, e-bikes continued on their upward trajectory of recent years. In 2019, the market share of electric bicycles in the EU still stood at around ** percent but had grown to over a quarter by 2024. Bicycles with electric assist, also known as EPACs, are particularly popular among consumers. These bicycles provide electrically powered assistance while pedaling, but motor assistance is capped at a speed of 25 kilometers per hour, which allows them to be legally treated as pedal cycles.