R Project on KC House Sales Data Analysis

This repository contains the R project analysis of the KC House Sales dataset, aimed at predicting house sales in King County, Washington State, USA, utilizing Multiple Linear Regression (MLR). The data, sourced from Kaggle datasets under the name "KC_Housesales_Data", comprises historical data of houses sold between May 2014 to May 2015.

Data Source

The dataset can be found at: KC House Sales Data on Kaggle

Project Overview

The project involves the following key steps:

• Data preprocessing and exploration to understand the dataset characteristics.
• Exploratory Data Analysis (EDA) to identify patterns, outliers, and relationships between variables.
• Data visualization to support EDA findings.
• Building a Multiple Linear Regression model to predict house prices.
• Evaluating model performance and comparing it with a one-layer forward neural network as a reference.

Libraries and Tools Used

• tidyverse for data manipulation and visualization.
• corrplot for visualizing correlations between variables.
• lubridate for date-time manipulation.
• caTools, GGally, caret, and leaps for various stages of model building and evaluation.

Instructions for Use

To replicate or extend the analysis:

1. Download the dataset from Kaggle.
2. Install the required R libraries mentioned above.
3. Run the R Markdown file myproject.Rmd for step-by-step execution of the analysis.

Acknowledgments

Data provided by Kaggle datasets. Analysis and model building by Teoman Selcuk as part of the MTH 404 R Project.

*Note: For detailed understanding and insights, users are encouraged to go through the R Markdown file myproject.Rmd.