problem statement with environmental issues and health becoming trending topicsusage of bicycles as mode of transportation has gained traction in recent years to encourage bike usagecities across the world have successfully rolled out bike sharing programs under such schemesriders can rent bicycles using manualautomated kiosks spread across the city for defined periods in most casesriders can pick up bikes from one location and return them to any other designated place the bike sharing platforms from across the world are hotspots of all sorts of dataranging from travel timestart and end locationdemographics of ridersand so on this data along with alternate sources of information such as weathertrafficterrainand so on makes it an attractive proposition for different research areas the capital bike sharing dataset contains information related to one such bike sharing program underway in washington dc given this augmented (bike sharing details along with weather informationdatasetcan we forecast bike rental demand for this programexploratory data analysis now that we have an overview of the business case and formal problem statementthe very next stage is to explore and understand the data this is also called the exploratory data analysis (edastep in this sectionwe will load the data into our analysis environment and explore its properties it is worth mentioning again that eda is one of the most important phases in the whole workflow and can help with not just understanding the datasetbut also in presenting certain fine points that can be useful in the coming steps ##note the bike sharing dataset contains day level and hour level data we will be concentrating only on hourly data available in hour csv preprocessing the eda process begins with loading the data into the environmentgetting quick look at it along with count of records and number of attributes we will be making heavy use of pandas and numpy to perform data manipulation and related tasks for visualization purposeswe will use matplotlib and seaborn along with pandasvisualization capabilities wherever possible we begin with loading the hour csv and checking the shape of the loaded dataframe the following snippet does the same in [ ]hour_df pd read_csv('hour csv'print("shape of dataset::{}format(hour_df shape)shape of dataset::( the dataset contains more than records with attributes let' check the top few rows to see how the data looks we use the head(utility from pandas for the same to get the output in figure -