House Price Prediction, King county

CONCEPT

In this data analysis endeavor, the primary objective is to forecast the selling prices of houses by leveraging advanced regression modeling techniques. The focal point of the investigation is the response variable – the intrinsic value that dictates the market dynamics: the price of houses. Through meticulous examination of various features such as bedrooms, bathrooms, square footage, and more, the project aims to unravel patterns, correlations, and outliers that significantly influence housing prices.

Navigating through exploratory data analysis, linear regression, and multiple regression models, the project aspires to distill key insights into the intricate interplay of factors determining real estate valuations. This endeavor is not just a predictive analytics venture but a comprehensive exploration of the nuances shaping the housing market landscape. By encapsulating the essence of price prediction and unraveling the multifaceted relationships embedded in the dataset, this project stands as a testament to analytical prowess in decoding the intricacies of housing market dynamics.

DATA

The dataset under scrutiny encompasses a wealth of information pertaining to 21,613 houses, encapsulating 21 variables that intricately describe these residential properties. Focused on the real estate landscape of King County, Washington, USA, the dataset spans house sales transpiring between May 2014 and May 2015, providing a comprehensive view of the greater Seattle metropolitan region. Leveraging variables ranging from the number of bedrooms and bathrooms to square footage, the dataset becomes the cornerstone for predicting house prices. This rich repository of real estate data becomes the canvas upon which predictive analytics unfold, delving deep into the dynamics that govern housing valuations in one of the United States' vibrant urban hubs.

APPROACH
To unravel patterns in housing prices, the approach commenced with thorough Exploratory Data Analysis (EDA), isolating outliers through boxplots and uncovering a robust linear correlation between price and living area. Feature selection involved converting categorical variables, like bedrooms and bathrooms, into factors. Employing the tidyverse suite, specifically dplyr and ggplot2 packages, streamlined data manipulation and visualization.

ALGORITHMS

Three regression models were sequentially applied to refine predictions. Beginning with simple linear regression, the analysis progressed to multiple linear regression, incorporating factors such as square footage, bathrooms, and floors. The final model, a comprehensive multiple linear regression (R² = 0.65), showcased its predictive power. The lm() function from the base R package facilitated model fitting, while the tidymodels framework supported streamlined modeling procedures. This iterative algorithmic approach encapsulates the project's evolution, from initial exploration to a refined predictive model.