House Prices Prediction

Problem:

This challenge here is to predict the selling prices of houses in King County, USA based on the information provided. Hence, it is a regression problem and the task here is to minimize the error of prediction.

Data:

The House Prices Prediction data has around 21,000 records with 21 features. Each record in the dataset has relevant information about the houses like number of bedrooms, number of bathrooms, area of the house, locality of the house etc.

Approach:

  • Exploratory data analysis and Data Visualization
  • Normalizing Data
  • Feature Engineering
  • Regressor Models Implementation. The following models were implemented
    • Random Forest Regressor
    • XGBoost Regressor

Conclusions:

  • Random Forest performs slightly better than XGBoost based upon the MSE (mean square error) value

Technologies Used: Machine Learning, Python

Machine Learning Data visualization Python Data Science