/House-price-london

A project designed to explore London house price by using various python packages

Primary LanguageJupyter Notebook

House-price-london

A project for exploring london house prices in 2018
The inspriation is coming from my needs to buy a house

Part1

  • Filter through the data from the UK house dataset.
  • Obtain some insightful information from the filtered dataset.
  • Use GeoPandas to plot a London heatmap with different borourgh's average flat prices

Part2

  • Explore further with the London house price 2018 dataset.
  • Process the dataset to generate more insights (e.g. smoothing, secondary data calculation, etc.)
  • Use fbprophet to do some seasonality analysis, and trying to predict the average London flat price for the rest of 2018

Brand new approach

  • Use Zoopla to scrape housing data
  • An simple API was built
  • All files are in package directory
  • There are jupyter notebook demonstrations to illustrate how these package can be used.

Covered areas

district_postcode = [
    ('Hackney', 'E8'), ('E-head', 'E1'), ('Bethal green', 'E2'), ('EC-head', 'EC1'), 
    ('Bishopsgate', 'EC2'),('French chruch street', 'EC3'), ('Fleet strett', 'EC4'), 
    ('N-head', 'N1'),  ('Highbury', 'N5'), ('Highgate', 'N6'), ('Finsbury park', 'N4'),
     ('NW-head', 'NW1'),('Cricklewood', 'NW2'), ('Hampstead', 'NW3'), ('Kentish town', 'NW5'),
       ('Kilburn', 'NW6'), ('St John wood', 'NW8'),('SE-Head', 'SE1'),
      ('Greenwich', 'SE10'), ('SW-Head', 'SW1'), ('Chelsea', 'SW3'),
       ('Clapham', 'SW4'), ('Earls court', 'SW5'),('Fulham', 'SW6'),
       ('South kensington', 'SW7'), ('South lambeth', 'SW8'), ('Stockwell', 'SW9'),
        ('West Brompton', 'SW10'), ('SW-head', 'SW11'), ('Paddington','W2'),
      ('North Kensington', 'W10'), ('Notting hill', 'W11'), ('West Kensington', 'W14'),
       ('WC-head', 'WC1'), ('Strand', 'WC2')
      ]

GUI for visualisation (In development)

Overview

latest update (2019-09-01)

  • Used PySide2 instead of Tkinter
  • Complete new design
  • Web browsing capability
  • 30-day moving average smoothing

Consolidated graph interface

  • All graphs are in one tab now.