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CST4070 - Applied Data Analytics Assignment Help

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CST4070 - Applied Data Analytics - Tools, Practical Big Data Handling, Cloud Distribution Assignment - Middlesex University, UK

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The challenge -

Santander Cycles (formerly Barclays Cycle Hire) is a public bicycle hire scheme in London. The scheme's bicycles are popularly known as Boris Bikes, after then-Mayor of London, Boris Johnson. The operation of the scheme has been contracted by Transport for London (TfL). The recent success of the scheme has led to its expansion into many areas of London and its rapid growth has led to real challenges balancing bike sharing supply with bike sharing demand.

To provide a possible solution to this problem, bike sharing usage prediction is critical. To this purpose, the Transport for London (TfL), has released three dataset named bike_journeys , bike_stations and London_census whose structure is illustrated in the next section. As part of the challenge, you need to define and implement a data science method able to predict the total number of bikes rented in each bike station with the temporal granularity of one hour time slot, so to help TfL to balance bike sharing supply with bike sharing demand.

Special attention must be paid to the interpretation of the final adopted model, to understand which factors are associated with an high/low demand of rented bikes in London, such as population composition from census data, weekends, peak hours, and so forth.

Data -

You have three dataset available, which have the following structure.

1. bike_journeys data:

§  Journey_Duration: duration of the bike journey in seconds.

§  Journey_ID: the id of the journey.

§  End_Date: a numeric field indicating the day of the month when the journey terminated (e.g., 1, 2, ..., 30, 31).

§  End_Month: a numeric field indicating the month when the journey terminated (e.g., 1, 2, ..., 11, 12).

§  End_Year: a numeric field indicating the year when the journey terminated (e.g., 2017).

§  End_Hour: a numeric field indicating the hour when the journey terminated (e.g., 1, 2, ..., 23, 24).

§  End_Minute: a numeric field indicating the minute when the journey terminated (e.g., 1, 2, ..., 59, 60).

§  EndStationID: the id of the station where the journey terminated.

§  Start_Date: a numeric field indicating the day of the month when the journey started (e.g., 1, 2, ..., 30, 31).

§  Start_Month: a numeric field indicating the month when the journey stated (e.g., 1, 2, ..., 11, 12).

§  Start_Year: a numeric field indicating the year when the journey started (e.g., 2017).

§  Start_Hour: a numeric field indicating the hour when the journey started (e.g., 1, 2, ..., 23, 24).

§  Start_Minute: a numeric field indicating the minute when the journey started (e.g., 1, 2, ..., 59, 60).

§  StartStationID: the id of the station where the journey started.

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2. bike_stations data:

§  Station_ID: the id of a bike station.

§  Capacity: a numeric value indicating the maximum capacity of bikes of the station.

§  Latitude: the latitude where the station is located.

§  Longitude: the longitude where the station is located.

§  Station_Name: a string indicating the name of the station (e.g., "River Street , Clerkenwell", "Phillimore Gardens, Kensington").

3. LondonCensus data:

§  WardCode: geographical unit of analysis for the census data. It is a code corresponding to an electoral London area.

§  WardName: name of the corresponding electoral London area.

§  Borough: London Borough to which the ward corresponds to.

§  NESW: whether the ward is located in the north, south, west, east part of London.

§  AreaSqKm: square kilometres associated with the corresponding ward.

§  lon, lat: coordinates (longitude, latitude) associated with the centre of the ward.

§  IncomeScor: proportion of the population experiencing deprivation relating to low income. The more deprived is an area, the higher the score.

§  LivingEnSc: quality of the local environment. The more deprived is an area, the higher the score.

§  NoEmployee: number of people having an occupation.

§  GrenSpace: percentage of green space associated with the ward.

§  PopDen: population divided by the surface of the ward area.

§  BornUK: total number of people who were born in the UK.

§  NotBornUK: total number of people who were not born in the UK.

§  NoCTFtoH: number of properties in council tax band F-H (the highest median house price)

§  NoDwelling: number of properties in each ward.

§  NoFlats: number of flats in each ward.

§  NoHouses: number of houses in each ward.

§  NoOwndDwel: number of owned properties in each ward.

§  MedHPrice: median house price.

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Part A - Group report

Your group report document (maximum 1500 words) must contain a clear description of how you design a method to solve the above challenge and the result you get by applying your method. You need to structure your group report as follows:

1. Problem definition and goals. Describe the problem, your goals and the challenges involved.

2. Pre-processing. Write whether your data needs any cleaning or any pre-processing.

3. Hypothesis. Write here your scientific hypothesis, which needs to be reasonable and falsifiable.

4. Data processing. You need to write here your metrics along with your R code processing your data. You need to write clear and unambiguous metrics. Comment your R code explaining how it is able to transform your data in the final format you need.

5. Algorithms. Write here the machine learning tasks you need to use along with the R code needed. Comment your R code.

6. Data Understanding. You need to write here the output you get and how you interpret it.

7. Main finding. List the most surprising finding you have got and the main limitations of your method.

Part B - Individual report

Goal of the individual submission is to show how you, individually, have contributed to the project.

As individual submission, you need to export your individual R Notebook file into a PDF file. Please, make sure that the number of comments do not exceed 1500 words. You need to show evidence that you can apply R language, together with analytical and critical thinking, to solve your data science challenge.

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The CST4070 Applied Data Analytics Assignment is a challenging but rewarding task. In this assignment, you will be asked to apply the principles and practices of data analytics to solve a real-world problem.

To begin, it is important to understand the different steps involved in the data analytics process. These steps include:

Data collection: Gathering the data that is relevant to the problem being solved.
Data cleaning: Preparing the data so that it is suitable for analysis.
Exploratory data analysis: Examining the data to identify patterns and trends.
Model building: Developing a model that can be used to predict or classify new data.
Model evaluation: Assessing the performance of the model.
Model deployment: Putting the model into production so that it can be used to solve real-world problems.

Once you understand the data analytics process, you can begin to apply it to the problem in your assignment. To do this, follow these steps:

Identify the problem that you want to solve. What is the question that you want to answer with your data analysis?
Collect the data that is relevant to the problem. What data do you need to answer your question?
Clean the data so that it is suitable for analysis. This may involve removing outliers, correcting errors, and converting the data into a consistent format.
Perform exploratory data analysis to identify patterns and trends in the data. This will help you to understand the data and to develop a hypothesis about the problem.
Build a model to predict or classify new data. There are many different types of machine learning models that you can use for this task.
Evaluate the performance of the model on a held-out test set. This will help you to determine how well the model will generalize to new data.
Deploy the model to production so that it can be used to solve real-world problems. This may involve integrating the model into a software application or making it available as a web service.

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