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The objective of this project is to model the rental prices for Airbnb apartments in London.

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Airbnb-Rental-Price-Prediction:


The objective of this project was to model the rental prices for Airbnb apartments in London.

Problem Statement:


  • Airbnb is an online marketplace which allows users to post listings on their website and it earns commissions from every booking.
  • At present when someone wants to list an Airbnb rental, they have to manually analyze similar properties near their location and decide the price themselves.
  • Idea of our project is to form a model to estimate what the correct price of their rental should be given the features of their property.

Dataset:



Data Analysis and Visualization:


HeatMap: Heat Map


Bedroom VS Price: BedroomVSPrice


London Borough vs Price: London Borough vs Price


ProposedSolution: ProposedSolution

Model Creation & Selection:

Classification metric:

  • After feature engineering step we have created 2 bins for 'price' from 0-100 & 101-2001.
  • Splitting the data into Train and Test set(70-30).
  • Before performing Regression we have first done Classification to predict Price_bins.
  • We chose Random Forest and Logistic Regression because we wanted a algorithm which would allow to assign class weights to handle class imbalance problem.

ClassificationMetric

Regression metric:

  • After performing Classification on price_bins we have built XGBRegressor model for each price bin.
  • We have trained the model on log transformed Target variable as price is a relative term.
  • We have used L1 regularization to prevent overfitting.

RegressionMetric


Thank You!

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The objective of this project is to model the rental prices for Airbnb apartments in London.

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