The objective of the project is to create a machine learning model. We are doing a supervised learning and our aim is to do predictive analysis to predict San Francisco Airbnb Rental Pricing.
- 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.
The dataset is obtained from Kaggle.
Link: https://www.kaggle.com/jeploretizo/san-francisco-airbnb-listings
The analysis and model creation can be found in the .ipynb file.
The main packages used are numpy, pandas, matplotlib, seaborn and sklearn.
The web app has been build using basic HTML, CSS, Javascript, Flask and Herkou.
Link: https://airbnb-rental-price-predict.herokuapp.com/
- Use multiple Algorithms
- Optimize Flask app.py
- Update the Front-End