Reference no: EM132742924

Big Data Product: Weapons and Drugs

In the television documentary "Ross Kemp and the Armed Police" broadcast 6th September 2018 by ITV, multiple claims were made regarding violent crime in the UK.

These claims were:

Violent Crime is increasing

There are more firearms incidents per head in Birmingham than anywhere else in the UK

Crimes involving firearms are closely associated with drugs offences

In this assignment you will investigate these claims using real, publicly available data sets that will be made available to you and placed in Amazon S3. These include, but are not limited to:

Street Level Crime Data published by the UK Home Office. This dataset contains 19 million data rows giving a crime type, together with their location as a latitude and longitude.

Land Registry Price Paid Data: This gives the postcode of a property, the property type from an enumeration of D (Detached), S (Semi-Detached), T (Terraced), F (Flats/Maisonettes) and the price paid.

Postcode Data: This data set is based on material provided by the Ordinance Survey. It gives a latitude and longitude to every postcode. This is useful as it relates between the Land Registry Price Paid dataset postcode, and the original crime dataset
latitude/longitude.
Specifics
Process the data prepared for you using Apache Spark.
Filter the dataset so that crimes refer to relevant events only.

Using appropriate visualization methods, statistics, and machine learning, determine whether the claims made by Ross Kemp were true, false, or could not be determined.

Explain the reasoning behind your code so that it is clear what each block is intended to achieve, and why.

Report critically on the advantages, disadvantages, and limitations of the methods used.

Your submission will be a Jupyter Notebook containing both code (typically Python), and explanatory text (Markdown) limited to 2500 words (plus references). References from scientific literature must be used and your discussion must be your own words. DO NOT CUT AND PASTE FROM THE INTERNET.

Attachment:- Big Data Product.rar