Housing Market Data Analysis in R Project

Perform EDA, data visualization, hypothesis testing, and linear regression in R on house market data intermediate

With Elitsa Kaloyanova

Type: Course project

Duration: 7 Hours

Case Description

Overview: In the world of real estate, the value of a house could be influenced by many factors. This Housing Market Data Analysis in R project dives into understanding how specific variables, ranging from crime rates to the number of rooms, affect the median market value. You’ll harness the power of R and use its dyplr and ggplot2 packages to analyze and understand housing data. By leveraging data analytics and machine learning, we aim to provide insightful visualizations and predictive models to realtors, home buyers, and stakeholders involved in the housing market. Ultimately, you’ll create a simple linear regression that predicts a house’s market value based on a powerful predictor.

Dataset: The dataset used in this project is sourced from a CSV file containing the following variables:

  • Crime.Rate: Local crime rate per capita
  • Average.Rooms: Average number of rooms in homes
  • Highway.Access: Proximity to highways
  • Pupil.Teacher.Ratio: Ratio of students to teachers in local schools
  • Median.Home.Value: Median value of houses

Project requirements

For this Housing Market Analysis in R project, you’ll need R and R studio installed. You’ll need to have the following libraries installed:

  • ggplot2 
  • dplyr
  • car

Project files

You can download the housing_data.csv  and the skeleton for the Housing Market Data Analysis in R project with the provided files.

Start project
Project content
  • 2 Project files
  • Guided and unguided instructions
  • Part 1: Data Analysis
  • Part 2: Data Visualization
  • Part 3: Hypothesis Testing
  • Part 4: Linear Regression
  • Quiz
Topics covered
Data visualization Data Analysis