Predicting House Prices - 
A Regression Algorithm Benchmarking Project

Objectives:

 

The project addresses the challenge of accurately predicting house prices based on various factors like the number of rooms, area, and location. It aims to identify the most effective regression algorithm for this purpose by benchmarking linear regression and Bayesian ridge regression models.

Approach:


The problem was solved through a structured approach:

  • Data Preparation: Cleaned and preprocessed the housing dataset to ensure quality and suitability for modeling.
  • Algorithm Implementation: Built linear regression and Bayesian ridge regression models to analyze the data.
  • Model Evaluation: Used metrics like MAE, MSE, and RMSE to measure the performance of the models.
  • Visualization: Visualized relationships between variables using Python libraries to gain deeper insights into the data.
  • Benchmarking: Compared the results of both models to determine the more accurate algorithm for predicting house prices.

Results and Insights:


The project successfully provided accurate predictions for house prices and demonstrated the strengths of both regression models. By evaluating and comparing their performance, the project highlighted the suitability of Bayesian ridge regression for handling certain aspects of the dataset, offering valuable insights for practical applications in real estate pricing.

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