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https://github.com/tanay-dwivedi/london-housing-data-analysis
The project involves conducting a comprehensive analysis of London's housing data to extract valuable insights, support decision-making processes, and identify potential issues within the housing market.
https://github.com/tanay-dwivedi/london-housing-data-analysis
dataanalysis houseprice matplotlib-pyplot plotly-express python seaborn visualization
Last synced: 12 days ago
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The project involves conducting a comprehensive analysis of London's housing data to extract valuable insights, support decision-making processes, and identify potential issues within the housing market.
- Host: GitHub
- URL: https://github.com/tanay-dwivedi/london-housing-data-analysis
- Owner: Tanay-Dwivedi
- Created: 2024-03-09T17:59:44.000Z (10 months ago)
- Default Branch: master
- Last Pushed: 2024-03-12T18:25:33.000Z (10 months ago)
- Last Synced: 2024-11-07T03:31:10.680Z (2 months ago)
- Topics: dataanalysis, houseprice, matplotlib-pyplot, plotly-express, python, seaborn, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 1.65 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# London Housing Data Analysis
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## Problem Statement
The problem at hand entails the comprehensive **analysis** of a dataset encompassing **London's housing data**. This analysis seeks to discern **trends** in housing, identify **regional disparities** in **average prices**, ascertain the **correlation** between the **number of houses sold** and **average prices**, and evaluate the influence of **crime rates** on **housing dynamics**.
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## Identify the Data
[Dataset](https://github.com/Tanay-Dwivedi/London-Housing-Data-Analysis/blob/master/house.csv)
Identifying the data involves recognizing key variables such as date, area, average price, houses sold, and number of crimes within the dataset. This process also includes understanding the structure, format, and quality of the data to ensure accurate analysis and interpretation.
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## Aim of the analysis
**1. Insight Generation:**
The primary aim of the analysis is to extract valuable insights from the London housing dataset, shedding light on trends, patterns, and relationships within the data.
**2. Decision Support:**
Another objective is to provide decision support to stakeholders in the real estate industry and urban planning sectors, aiding in strategic decision-making processes.
**3. Problem Identification:**
Furthermore, the analysis aims to identify potential issues or challenges within the housing market, such as regional disparities or the impact of crime rates, to inform policy-making and intervention strategies.
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