The dataframe consists of 16 different variables such as Bike Ids,Start and End Stations,Member age, gender and etc. It contains 18,63719 bike rides. Ages in dataset from 18 to 56 takes 95% of the users in dataset.There were users more than 100 years old. So, we can remove users more than 60 years old as a cleaning and tidy up. Also, I generated new fields such as Month,MonthName,Weekdays,Hours,Minutes and Age groups,Average Trip duration and Average Trip Distance in order to make grouping and analyze the date by using groups

For Data Analysis, I considered 2018 data from January to December and found very interesting facts from Exploratory Data Analysis. In 2018 people took around 18,637,19 bike rides. In which 20-60 yrs took around 1,70,8250 bikes rides. 20-30 years Age group users are rapidly growing compared to other user groups. First service is started with 30-40 years users and followed by 20-30 yrs age group who became dominant over the year. Firstly, Ford Bike offerings people based on the interest i.e, Subscribers and Customers.Bike Rentals are high in demand between April to October months due to summer season and they drop during winter season. Subscriers use the service during Weekdays from Monday to Friday for commute purposes between 8-9 am and 5-6pm are busy hours for daily bike riders.70 % of 20-40yrs used bike rides,of which 40% of 30-40 age group people took more rides compare to all other age groups. Male took around 76% of all bike rides,where as female took around %24 of them. 88% of Subscribers are using bike service compare to customers using bike service with 11.7%.People use this service on weekdays more than weekends. 8am and 5pm are the peak hours for this service. Also, people use this service when they are in lunch time as well.Customers' rides seems increasing slightly but subscibers' rides reached 6 times more than customers' on October 2018. There is a decrease on November 2018 for subscribers but it seems like it is related with winter season. Subscribers' average trip duration is around 11 minutes. Customers' average trip duration is around 28 minutes. Subscribers and customers trip distance were about the same, which is slightly more than one mile. Furthermore, I extended my analysis using Multi-variate exploration between Subscriers and Customers. From both user types: Subscriers and Customers Men took more rides compare to Female riders. In which 20-40 age group people who used most of the service compare to all other age groups between April-October and slow down during winter season. 20-40 years Subscribers took most of the rides during weekdays between 10 am to 3 -4pm and after hours between 9-11pm.Between 40-50 years subscribers used service mostly during weekends compare to weekdays.Between 20-50 yrs Customers took bike rides between 10 am to 4 pm from Monday to Friday and most of them took after hours between 9pm to 11pm due to less traffic. However, these customers took less rides on weekends when compare to weekdays this is because older people who are close to retirement age have more leisure time compare to younger people who use it for daily commute or busy work schedules during weekdays.

For Data Analysis, First I started exploring dataframe with People age some of them has over 100 years before 1900. For Analysis purpose I limited dataset to less than 60 years age people.For Univariate Exploration, I started exploring data by Monthly,Weekly,Hourly Bike Trends and Percentage of Age groups and Genders.In Bi-variate Exploration,analyzed by calculating Percentage of Subscribers and Customers and then Monthly,Weekly and Hourly usage by user types and also calculated Average Trip duration and distance for both Subscribers and Customers. In Multi-variate Exploration, I extended analysis by taking three variables using different age groups monthly trends by user types (Subscribers and Customers). Finally, I used heatmap to show when bikes are high in demand during Weekdays,Different hours by user types(Subscribers and Customers).