AirbnbPricePrediction

Airbnb Pricing Predictions

A Statistical Model to predict the optimal Airbnb Listing price in given listing information (e.g. bedrooms, type of bed, location, ratings) and take into account.

Data Sources

http://insideairbnb.com/get-the-data/

Technologies Used

• Scikit learn
• Pandas
• Numpy
• Matplotlib
• Seaborn
• Scipy
• Models
  • Linear Regression
    • Elastic Net
    • Gradient Boosting Regression
    • Random Forest Classifier
    • Ridge

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In order to run the code make sure you pre-install all the dependencies such as LIME, Flask etc.

DOWNLOAD THE DATASET:

Create a directory called "Data", and download the datasets from this link into the directory: https://drive.google.com/drive/folders/12TfxELKDHTCIpMYSbWPaMxTF8zhzRpAv?usp=drive_link

To run the project:

1. Run the file AirBnbFinalDemo_v8.ipynb
2. Specify the folder path where the CSV files are located path1 = "I:/class/Term2/BDM 3014/Project/CSVs/Batch1"
3. At last the code will generate a .pkl file
4. Run the app.py to view the UI
5. Access the webpage at http: //127.0.0.1:5000

Warning: certain models take a while to train and run!