Essay: Real-Time Availability Of Singapore Carpark Information.

Design parsing methods to extract information from web pages and answer the following questions:

a) Assuming that you live in Toa Payoh town and owns a car. Hence, you are interested to find out what how many carparks are there available in Toa Payoh. Using the data information available, construct a Python program to count the total number of carparks in “Toa Payoh” and the total number of car parks decks available.

b) Assuming that you are driving home to Toa Payoh and hence you are interested to know which of the car parks will provide you the highest chance to getting a lot. Construct another Python program that enable you to check and display in real-time what is the availability of the carparks in Toa Payoh.

c) Based on the results in (a) and (b), how can you improve your program(s) and tabulate your chances of getting a lot in Toa Payoh. You should rank it from highest to lowest chance.

Finding Available Car Parks by Using Live Data

The main aim of this project is to develop a real time avaibility of Singapore's carpark information which can be implemented in Jupyter Python Notebook. The the MySQL database is designed using AWS on the RDS data services. Thus, the analysis of a real time car details will be investigated.

By assuming that I live in Toa Payoh and own a car, the total number of car parks available can be found by using the live car’s dataset in Toa Payon. The anlaysis of the Toa Payoh’s dataset is required as it can provide the information of total number of car parks decks available.

df.count()

df.groupby('car park').count()[['car_park_decks']]

By assuming that I am driving home to Toa Payoh, I need a car parking lot. Thus, on Toa Payoh’s dataset is utilized to find and display the higest chances to get a lot in Toa Payoh, in real-time.

df.groupby('car park')['car_park_decks'].count()

df.set_index(["car park", "free_parking"]).count(level="car park")

Based on the results of Question a and b, the program can be improved and the chances of getting a lot can be found by finding the dataset and tabulating the list of car information, as follows.

df[['car park', 'free_parking']]

The ranks from highest to lowest chance order is represented in the above figure.

Based on the observation of Toa payoh’s data, two issues are as follows:

Difficulty to find the dataset and creating the Python code for analyzing the accuracy of the datasets, in real-time car parking information.

Analyzing the dataset at the same time can create the SQLAlchemy. The SQL database is created by using the Python and ORM packages of the stored information and dataset can be downloaded in URL resources on the stored information. The database schema is represented below.

1. Creating the Pythoncan help to find the dataset on "calculatechartsRank", which can calculate the information.

1. Based on the data available, it is possible to find andtrack the cars. Then, the specified average rating is presented on the ranking information.

The analysis of the dataset and finding the charts can order the song name, singer and top rating can be achieved on the dataset which can implement the Python function and calculates each car's rating to find the chart (Surhone, Timpledon and Marseken, 2010).

We can create thecloud SQL database by using the AWS service on the relational database service. Creating the canvas group on the SQL database and simultaneously the title of the database implementing on the MYSQL workbeach can be created.

Displaying the retrieved record'sscheme on the database on “displayDinfo”

The MySQL workbench can be displayed on the “show order product details” on project name , customer, order details and displaysthe product description details.

1. Wecan analyze the Json dataset, then find and create new mlab cloud database on the Json structure. Later, data is inserted on the order details information.

1. Creating the Pythoncode and displaying the retrieved information on “displayOrdersDoc”

1. Creating the Pythonfunction by using the pandas datagram then analyses and displays the data, "create productDF", "customerDF", "create customer DF", "orderDF" and finds the endpoint values on the RDS database (Nguyen, 2017).

1. The analysis of the Order dataset and findingthe product order lists can be displayed on the Python consol The SQL database can be linked with the Python pandas datagram and displays the product's total value creating limits.

1. Creating AWS of the RDS database can connect thePython function's datagram of dataset (Anthony, 2017).

References

Anthony, A. (2017). Mastering AWS Security. Birmingham: Packt Publishing.

Nguyen, H. (2017). relational database services. Research and Development on Information and Communication Technology, 3(14), pp.19-26.

Surhone, L., Timpledon, M. and Marseken, S. (2010). MySQL workbench. Beau Bassin, Mauritius: Betascript Pub.