next steps decision trees helped us achieve better performance over linear regression based modelsyet there are improvements possible the following are few next steps to ponder and keep in mindmodel fine-tuningwe achieved drastic improvement from using decision treesyet this can be improved further by analyzing the results of trainingcross validationand so on acceptable model performance is something that is use case dependent and is usually discussed upon while formalizing the problem statement in our casean -squared of might be very good or missing the markhence the results need to be discussed with all stakeholders involved other models and ensemblingif our current models do not achieve the performance criteriawe need to evaluate other algorithms and even ensembles there many other regression models to explore ensembles are also quite useful and are used extensively machine learning pipelinethe workflow shared in this was verbose to assist in understanding and exploring concepts and techniques once sequence of preprocessing and modeling steps have stabilizedstandardized pipelines are used to maintain consistency and validity of the entire process you are encouraged to explore sklearn' pipeline module for further details summary this introduced the bike sharing dataset from the uci machine learning repository we followed the machine learning workflow discussed in detail in the previous section of the book we started off with brief discussion about the dataset followed by formally defining the problem statement once we had mission to forecast the bike demandthe next was to get started with exploratory data analysis to understand and uncover properties and patterns in the data we utilized pandasnumpyand seaborn/matplotlib to manipulatetransformand visualize the dataset at hand the line plotsbar chartsbox plotsviolin plotsetc all helped us understand various aspects of the dataset we then took detour and explored the regression analysis the important conceptsassumptionsand types of regression analysis techniques were discussed brieflywe touched on various performanceevaluation criteria typically used for regression problems like residual plotsnormal plotsand coefficient of determination equipped with better understanding of dataset and regression itselfwe started off with simple algorithm called linear regression it is not only simple algorithm but one of the most well studied and extensively used algorithms for regression use cases we utilized sklearn' linear_model to build and test our dataset for the problem at hand we also utilized the model_selection module to split our dataset and cross validate our model the next step saw us graduating to decision tree based regression model to improve on the performance of linear regression we touched upon the concepts and important aspects related to decision trees before using them for modeling our dataset the same set of preprocessing steps was used for both linear regression and decision trees finallywe concluded the by briefly discussing about next steps for improvement and enhancements this sets the flow and context for coming which will build on the concepts and workflows from here on