This project is done on AirBnb NYC data set from Kaggle https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data. The data analytics and visualizations are performed and then the predictive model is built to predict the price.
The data set has the following attributes/features:
-> id - id of the person int64
-> name - name of the person object
-> host_id - id of the host int64
-> host_name - name of the host object
-> neighbourhood_group - neighbourhood groups object
-> neighbourhood - names of the neighbourhood object
-> latitude - latitude of the region float64
-> longitude - longitude of the region float64
-> room_type - type of the room object
-> price - price of the room int64
-> minimum_nights - minimum number of nights in the room int64
-> number_of_reviews - total number of reviews given int64
-> last_review - the date of the review which was given last datetime64[ns]
-> reviews_per_month - number of reviews per month float64
-> calculated_host_listings_count - total count of the listings of host int64
-> availability_365 - availability of the rooms int64
With the above features, the dataset is explored, cleaned, preprocessed and analysed with visualisations. Then, the predictive model is built using regression models and the price is predicted.
The work done is solely done by me. Do not copy the work, with due respect. Kindly mention me if you are doing some improvements to the code.
If you feel the insights are good, feel free to star the repository.