EmreCey/kc-house-sales-eda

KC House Sales - EDA Assignment

This repository contains my Exploratory Data Analysis (EDA) assignment based on the King County House Sales dataset.

Presentation Video

<video controls style="max-width: 720px;"> <source src="https://huggingface.co/datasets/BarWachsman7/kc-house-sales-eda/resolve/main/presentation.mp4" type="video/mp4"> </video>

If the embedded video does not load, the presentation video can also be opened here:

Open presentation video

Backup Video Link

In case the embedded video does not load, the presentation video is also available here:

Watch the presentation on YouTube

Notebook

The full Jupyter Notebook containing the EDA process, visualizations, and conclusions is available here:

Open the full notebook

Project Overview

The goal of this project is to analyze the main factors that influence house prices in King County. The analysis focuses on understanding how variables such as living area, location, house grade, condition, number of bedrooms, number of bathrooms, and time-related features are related to house prices.

Research Question

Which features are most strongly associated with house prices, and how can we use exploratory data analysis to better understand the housing market?

Dataset

The dataset includes residential house sales data from King County. Each row represents a house sale, and the columns include information such as:

• —Sale price
• —Number of bedrooms and bathrooms
• —Living area size
• —Lot size
• —Number of floors
• —Waterfront view
• —House condition and grade
• —Year built
• —Zipcode
• —Latitude and longitude

Files in this Repository

• —kc_house_data.csv - The dataset used for the analysis.
• —Assignment_1_EDA_Final_Bar_Wachsman.ipynb - The full Jupyter Notebook containing the EDA process, visualizations, and conclusions.
• —README.md - A short explanation of the project and repository.

Dataset File

The dataset used in this project is available here:

Open / Download the dataset

Main Steps in the Analysis

1. 1.Loading and understanding the dataset.
2. 2.Checking missing values and duplicate rows.
3. 3.Cleaning and preparing the data.
4. 4.Identifying outliers and suspicious values.
5. 5.Performing univariate and bivariate analysis.
6. 6.Creating visualizations to explore relationships between variables.
7. 7.Analyzing correlations between numerical features and house price.
8. 8.Summarizing the main insights and conclusions.

Main Insights

The analysis shows that house price is strongly related to property size, especially living area, as well as house grade and location. Geographical features such as zipcode, latitude, and longitude also appear to play an important role in explaining price differences. Some variables, such as bedrooms, bathrooms, and floors, are useful but less informative when analyzed alone.

Author

Bar Wachsman