analyzing bike sharing trends "all work and no playis well-known proverb and we certainly do not want to be dull so farwe have covered the theoretical conceptsframeworksworkflowsand tools required to solve data science problems the use case driven theme begins with this in this section of the bookwe cover wide range of machine learning/data science concepts through real life case studies through this and subsequent we will discuss and apply concepts learned so far to solve some exciting real-world problems this discusses regression based models to analyze data and predict outcomes in particularwe will utilize the capital bike sharing dataset from the uci machine learning repository to understand regression models to predict bike usage demand through this we cover the following topicsthe bike sharing dataset to understand the dataset available from the uci machine learning repository problem statement to formally define the problem to be solved exploratory data analysis to explore and understand the dataset at hand regression analysis to understand regression modeling concepts and apply them to solve the problem the bike sharing dataset the crisp-dm model introduced in the initial talks about typical workflow associated with data science problem/project the workflow diagram has data at its center for reason before we get started on different techniques to understand and play with the datalet' understand its origins the bike sharing dataset is available from the uci machine learning repository it is one of the largest and probably also the longest standing online repository of datasets used in all sorts of studies and research from across the world the dataset we will be utilizing is one such dataset from among hundreds available on the web site the dataset was donated by university of portoportugal in more information is available at ##note we encourage you to check out the uci machine learning repository and particularly the bike sharing data set page we thank fanaee et al for their work and sharing the dataset through the uci machine learning repository fanaee-thadiand gamajoaoevent labeling combining ensemble detectors and background knowledgeprogress in artificial intelligence ( )pp - springer berlin heidelberg (cdipanjan sarkarraghav bali and tushar sharma sarkar et al practical machine learning with python