as pd

Load the Excel file to check its contents

file_path = '/mnt/data/173326182765401bbelfd.xlsx' excel_data = pd.ExcelFile(file_path)

Display sheet names to understand the structure

excel_data data from the "Data" sheet to inspect its structure data = excel_data.parse('Data')

Display the first few rows of the datasetThe dataset contains the following columns:

1. Selling Price: The price at which the property was sold.
2. Baths: Number of bathrooms.
3. Sq Ft: Square footage of the property.
4. Bedrooms: Number of bedrooms.

Let me proceed to calculate the required regression models and predictions based on the provided questions..linear_model import LinearRegression import numpy as np

Prepare the data for regression analysis

X_full = data[['Baths', 'Sq Ft', 'Bedrooms']] y = data['Selling Price']

Perform regression with all three independent variables

model_full = LinearRegression() model_full.fit(X_full, y)

Coefficients for the full model

coefficients_full = model_full.coef_ intercept_full = model_full.intercept_

Prepare the data for the reduced regression (Sq Ft and Bedrooms only)

X_reduced = data[['Sq Ft', 'Bedrooms']]

Perform regression with reduced variables

model_reduced = LinearRegression() model_reduced.fit(X_reduced, y)

Coefficients for the reduced model

coefficients_reduced = model_reduced.coef_ intercept_reduced = model_reduced.intercept_

Predict the selling price for a house with 4 bedrooms and 2650 square feet

house_features = np.array([[2650, 4]]) # [Sq Ft, Bedrooms] predicted_price = model_reduced.predict(house_features)

coefficients_full, intercept_full, coefficients_reduced,Here are the results for the regression models and prediction:

Full Regression Model (using Baths, Sq Ft, and Bedrooms):

• Coefficients:
  • Baths: -1386.21
  • Sq Ft: 60.28
  • Bedrooms: 54797.08
• Intercept: -5531.01

Reduced Regression Model (using Sq Ft and Bedrooms only):

• Coefficients:
  • Sq Ft: 59.73
  • Bedrooms: 54309.21
• Intercept: -5882.76

Predicted Selling Price:

For a house with 4 bedrooms and 2650 square feet, the predicted selling price (using the reduced model) is $369,647.

Would you like a detailed breakdown of the calculations or insights into the model? Here are five follow-up questions you might consider:

1. How do we interpret the coefficients of the regression models?
2. Why does removing one variable (Baths) improve or simplify the model?
3. What is the goodness-of-fit (R²) for both models?
4. Can we visualize the relationships between these variables and Selling Price?
5. Are there any significant interaction effects between the independent variables?

Tip: In regression analysis, always verify the model's assumptions (linearity, independence, etc.) to ensure reliable predictions.