In this assignment we will analyse and visualise trajectory data to classify driving patterns. The data was collected by the American based rideshare company Lyft $($www$.$lyft$.$com$).$ The data is presented in csv format and has the following format: Attribute Description TripId The id of a trajectory TimeStep The record number for a point of a trajectory TimeStamp The timestamp for a point of a trajectory Speed The ground velocity Acceleration The rate of change of speed Heading The bearing which is a value between $0$ and $359$ Latitude The latitude coordinate of GPS observation Longitude The longitude coordinate of GPS observation You can download the file here: data.csv You should read the CSV file into a Pandas Data frame and use Pandas to perform the $11$ tasks listed below. These tasks are designed to give you an opportunity to demonstrate the following learning outcomes and to satisfy the assessment criteria: Clean irregularities in the raw data file to convert it into a proper CVS format. Read data from the cleaned CVS file into a Pandas Data frame. Convert between different Date$/$time formats. Filter$/$restrict the rows and columns in Pandas Data frames to help answer the queries. Use aggregation operations $($such as mean, median, sum, max$)$ and to summarize data. Use group by to summarize data for various categories. Create new columns that are computed based on other existing columns. Demonstrate appropriate use of a variety of types of Plots to visualize data $($using Pandas$).$ All plots should have meaningful titles, axes labels and user$-$friendly data labels and be scaled large enough to easy see the details required. Markdown headings should be added to clearly separate and explain each of the tasks and markdown should be provided to discuss$/$summarize the key observations. Don$$t repeat yourself $$ use functions to avoid duplicating the same logic in multiple places. Use programming best practice $$ write clear simple Python code and use well$-$chosen identified names for all variables and functions. Note that the raw CSV data may require "cleaning" before it can be processed. Everything should be included in a single Jupyter notebook $($which you will need to create yourself $$ no skeleton solution for this assignment$).$ Tasks: Use markdown to document the data cleaning that you performed. Read the cleaned CSV file into a Pandas data frame Convert the timestamp into an YYYYMMDDHHMMSS format Convert speed from miles$/$hour to kilometre$/$hour Add a column that shows the accumulated time driven for each vehicle Show the minimum and maximum speed for each trip in a plot Plot the speed profile for trip T$1$ Add a column that indicates if a vehicle moves straight or is turning left$/$right Plot the trajectories of all vehicles. Use a loop to go thorugh all the trajectories. Add a column that indicates hard braking where deceleration is larger than $1\mathrm{.}5$m$/$s$2$ Plot a bar chart showing the travel time of each vehicle in ascending order