Hello,
In this phase of my analysis, I delved into correlation analysis and predictive modeling, aiming to uncover relationships between economic indicators and forecast median housing prices. Let’s break down what I’ve accomplished:
Correlation Analysis:I began by selecting a subset of economic indicators, including “Logan Passengers,” “Hotel Occupancy Rate,” “Unemployment Rate,” “Median Housing Price,” and “Housing Sales Volume.” I then created a correlation matrix to visualize the relationships between these indicators. The correlation heatmap displayed the strength and direction of these relationships, providing insights into how changes in one variable may affect others.
Predictive Modeling:
My analysis also involved predictive modeling, specifically focused on forecasting median housing prices. I identified predictor variables, which included “Unemployment Rate,” “Logan Passengers,” and “Total Jobs,” while the target variable was “Median Housing Price.” The dataset was split into training and testing sets to evaluate the model’s performance.
I applied a Linear Regression model to predict median housing prices based on the selected predictor variables. The model was trained on the training data, and predictions were made on the testing data. I assessed the model’s performance using Mean Squared Error (MSE) and R-squared (R²) as key metrics. These metrics provide insights into how well the model predicts median housing prices based on the chosen economic indicators.
In summary, this phase of the analysis involved correlation analysis to understand the relationships between economic indicators and predictive modeling to forecast median housing prices. These insights can be invaluable for decision-making in economic planning and housing market assessments.