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HousePricePredict

Topic from: Kaggle

Group Selection: Group I

Group name: WeIsExperts

CHAN Kai Kin, 3035111468

CHEN Jian, 3035419286

WU Kangzhang, 3035455242

LEE Lip Tong, 3035418610

Topic: House Price Prediction Based On Random Forest

Methodology:

Our group plans to introduce Random Forest as a regression technique. In our demonstration we will explore how various attributes affect the price of houses. Graphic explanation would mainly be used with minimal mathematical notations.

We will comprehensively explain these pointers during our lesson:

  • Importance
  • Decision Trees
  • What are Random Forests?
  • Bagging/ Dividing feature space
  • Pruning the tree (overfitting prevention)
  • Kernel Induced Random Forest
  • Out of bag error (performance metric)
  • Variable Importance
  • Applications (implementation details)
  • Advantages & Disadvantages

Demo:

Our live demo would be running a regression using random forest. The input data would take the sales price and numerous housing properties, which are used to train and test the regression model. The final regression model would be take any data to predict the corresponding housing price.

Dataset