Airbnb-Rental-Price-Prediction:

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

Problem Statement:

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• 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:

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• Rows: 77000+
• Columns: 97
• Source: http://insideairbnb.com/get-the-data.html

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Data Analysis and Visualization:

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HeatMap:

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Bedroom VS Price:

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London Borough vs Price:

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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.

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.

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Thank You!

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