https://github.com/athari22/house_sales_in_king_count_usa

The idea of the project is to do a Data analysis in a Real Estate Investment Trust. The Trust would like to start investing in Residential real estate.
https://github.com/athari22/house_sales_in_king_count_usa

analysis data data-science data-visualization ibm ibm-watson linearregression machine-learning matplotlib numpy pandas sklearn-library

Last synced: about 5 hours ago
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The idea of the project is to do a Data analysis in a Real Estate Investment Trust. The Trust would like to start investing in Residential real estate.

• Host: GitHub
• URL: https://github.com/athari22/house_sales_in_king_count_usa
• Owner: Athari22
• Created: 2022-07-24T18:36:58.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-07-24T18:42:10.000Z (over 2 years ago)
• Last Synced: 2023-12-10T10:25:21.407Z (11 months ago)
• Topics: analysis, data, data-science, data-visualization, ibm, ibm-watson, linearregression, machine-learning, matplotlib, numpy, pandas, sklearn-library
• Language: Jupyter Notebook
• Homepage:
• Size: 36.1 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# House Sales in King Count USA

## Project Overview

This project is a part of the [IBM Data Science Professional Certificate](https://www.coursera.org/professional-certificates/ibm-data-science).

## Description

The idea of the project is to do a Data analysis in a Real Estate Investment Trust. The Trust would like to start investing in Residential real estate. By determining the market price of a house given a set of features. The first step analyzes and predicts housing prices using attributes or features such as square footage, number of bedrooms, number of floors, and so on. 

## Dataset

The dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015. It was taken from [here](https://www.kaggle.com/datasets/harlfoxem/housesalesprediction?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-wwwcourseraorg-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2022-01-01). It was also slightly modified for the purposes of this course.