EDA - King County Housing Data Analysis

The King County Housing Data Analysis project focuses on performing exploratory data analysis (EDA) on the King County Housing dataset. This dataset contains detailed information about home sales in King County, USA. The project aims to provide valuable insights and recommendations to clients and stakeholders interested in the housing market in this region.

Table of Contents

• Introduction
• Project Goals
• Insights
• Setup

Introduction

This project analyzes housing data from the King County dataset with the purpose of providing valuable insights to stakeholders. Our stakeholders is Charles Christensen, who is a seller with a keen interest in making high-return investments. Charles is also interested in whether the properties have been renovated, the significance of renovations, the best neighborhoods for investment, and timing considerations.

Main focus on one specific client:

Name	Client	Characteristics
Charles Christensen	Seller	Invest with big returns, wondering about renovation?, which Neighborhood? Timing?

Here are the key factors in brief:

• Emphasis on high-return investments.
• Assessment of the impact of renovations.
• Evaluation of the impact of neighborhoods.
• Consideration of timing factors."

Project Goals

Project's main objectives were:

1. Investment Focus:
   • Find properties where sold for higher price than bought, see what brought them to high returns, such as location, condition, and renovation
2. Renovation Impact:
   • Do renovated properties have higher selling prices? Investigate the differences in price for properties with and without renovations. Calculate return of investment.
   • Analyze the relationship between year_renovated and price to see if recently renovated properties fetch higher prices.
3. Best Neighborhoods for Investment:
   • Average selling prices by neighborhoods
   • Trends in price across different areas to identify neighboorhoods with the highest growth
   • Have a look at the sqft_living /sqft_lot (future development potential, market trends)
4. Timing:
   • Explore how property prices have varied over time
   • Are there any specific months/seasons when properties tend to sell for higher prices?
5. Property Features and their Values:
   • How features like bedrooms, bathrooms, waterfront, and others correlate with the selling price
   • Which features are the most important

Insights

• The significance of location in determining house prices.
• The impact of property characteristics on pricing.
• How market trends and historical data can inform future decisions.
• The value of data analysis in real estate decision-making.

Setup

Virtual Environment

In this repo you will find a requirements.txt file. It contains all the libraries you will need for this exercise.

Set up your Environment

Plotly in Jupter Lab requires node.you Check by run the following commands:

node -v

If you haven't installed it yet, begin at step_1. Otherwise, proceed to step_2.

macOS type the following commands :

Step_1: Update Homebrew and install Node by following commands:

brew update
brew install node

Step_2: Install the virtual environment and the required packages by following commands:

pyenv local 3.11.3
python -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

WindowsOS type the following commands :

Step_1: Update Chocolatey and install Node by following commands:

choco upgrade chocolatey
choco install nodejs

Step_2: Install the virtual environment and the required packages by following commands.

For PowerShell CLI :

python -m venv .venv
.venv\Scripts\Activate.ps1
pip install --upgrade pip
pip install -r requirements.txt

For BASH CLI :

python -m venv .venv
source .venv/Scripts/activate
pip install --upgrade pip
pip install -r requirements.txt

Note: If you encounter an error when trying to run pip install --upgrade pip, try using the following command:

python.exe -m pip install --upgrade pip