Started my job in Banglore, initially, we use to walk all the way to our offices knowing the traffic in Banglore. Takes around 40 mins to reach our offices already tired and hungry somehow use to reach our offices. Then one of our seniors told us about Yulu, we did notice some blue bikes going around our areas but didn't know it was that easy to hire one. After downloading the app we came to know the ease of bike renting and sharing. The 40 mins got reduced to a max of 10-15 mins and cardio was a plus. So decided to build a model to predict the count of the bike that would be rented.
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 a 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. In this competition, participants are asked to combine historical usage patterns with weather data in order to forecast bike rental demand in the Capital Bikeshare program in Washington, D.C.
Day 1 (EDA): May 24, 2021
Started out with an EDA notebook. Link to the Kaggle competition. I usually prefer doing an EDA in a question-answer format where we come up with the questions around the data and then visualize or find answers to them from the data. We will start with some basic analysis and later as specified in the roadmap we will do some advanced explorations on the data in order to perform feature engineering. Tried to answer a basic question about the data as follows: