Data-Analytic-Project-1

Project Title - Analysing House Prices In Different Boroughs In London

Team Members - Diego (Team Leader), Vera, Lewis, Prav, Hamim, Sultana

Project Description/Outline:

Analysis of Property Prices In the different Boroughs In London and other influences on the property price.

Research Questions:

1. What are the most popular types of properties for sale in the London?
2. What is the correlation between house prices and average salary in London? Which areas have a higher salary?
3. How do property prices in London compare with other major cities in the UK over the last 25 years?
4. What is the correlation between house prices and crime statistics?
5. With the use of Apis, what are the correlation of average prices with certain amenities (Restaurants and Bars?

Conclusion:

We looked at 33 boroughs, extrapolated it, and narrowed it down to the top and bottom five to give an idea of the most affluent and poorest boroughs. We discovered the following through our research:

1. According to the data we gathered, flats and apartments are the most common kind of property to live in, and square footage is the factor that has the biggest impact on how much a property is worth.
2. Central London has the highest population density compared to other 'London Postcodes.
3. The highest house prices are in following areas: Kensington and Chelsea, Westminster, Camden Hammersmith & Fulham, Richmond upon Thames, and Islington.
4. Where the crimes were at the highest, these were tourist areas.
5. House prices have increased almost 8 times the cost over the last 25 years.
6. Average salary did not correlate with the prices of the house.

Data Sets Used:

1. https://www.kaggle.com/datasets/arnavkulkarni/housing-prices-in-london
2. https://www.kaggle.com/datasets/justinas/housing-in-london
3. https://www.gov.uk/government/statistical-data-sets/uk-house-price-index-data-downloads-june-2020

References:

https://www.youtube.com/watch?v=Wqmtf9SA_kk&t=1046s&ab_channel=NeuralNine

Rough Breakdown of Tasks:

1. Data Collection: Identify and gather data sources, such as public sources, csv files and APIs. Then extract and clean the data.
2. Data Analysis: Explore and visualise the data to identify patterns and trends (produce 6–8 visualizations of data). Conduct statistical analyses such as finding mean, median and etc, to answer the research questions.
3. Location Analysis: Research and analyse different areas and boroughs in London and create matplotlib map.
4. Presentation: Create a presentation (on Powerpoint) summarising the findings and insights. Could create extra slides for appendix for more information.