The task involves analyzing a dataset that contains information about housing prices and certain features related to the housing market. Specifically, the dataset provides the average number of rooms per dwelling and the median value of owner-occupied homes in $1000's. The goal is to identify relationships between these variables, while addressing the presence of data anomalies, such as outliers.
The dataset contains a mix of *inliers* (data points that fit the general trend or pattern) and *outliers* (data points that deviate significantly from the expected pattern). The presence of outliers can skew the results of any predictive modeling or analysis. Therefore, the aim is to visualize and model the relationship between the number of rooms and the home prices while excluding these outliers to get a more accurate model of the underlying trend.
### Solution:
In the solution, a scatter plot is used to visualize the data, with different markers for inliers and outliers:
- **Inliers**: These are the data points that follow the general trend of the relationship between the number of rooms and the home prices. They are shown as blue circles on the plot.
- **Outliers**: These are the data points that do not follow the expected pattern and are significantly different from the inliers. They are represented as brown squares.
A **RANSAC (Random Sample Consensus)** regression line is plotted in red on the graph. RANSAC is a robust method for fitting a model to data that may contain outliers. It helps identify the best fit line that excludes outliers, thereby providing a more accurate representation of the underlying relationship between the two variables (average number of rooms and median home price).
The plot clearly shows the main trend of home prices increasing with the number of rooms, while also distinguishing between valid data points (inliers) and those that do not fit the pattern (outliers). The red line represents the model derived from the inliers, which is less influenced by the outliers, resulting in a more reliable analysis of the relationship between the variables.
### Summary:
- The plot highlights the relationship between the average number of rooms and the median home price.
- Outliers are identified and differentiated from the inliers.
- The RANSAC regression line offers a robust fit to the data, ensuring that the relationship between rooms and home prices is accurately modeled despite the presence of outliers.
