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

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