MaksimEkin / NYC-Airbnb-Data-Analysis

Data Analysis Practice on NYC Airbnb Data by Kaggle - Regression problem (Price prediction).

New York City Airbnb Open Data - Practice

Attribution: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, by Aurelien Geron (O'Reilly). Copyright 2019 Kiwisoft S.A.S, 978-1-492-03264-9

Machine Learning Practice. Implimenting the project following the Chapter-2 project on O'REILLY's Hands-On Machine Learning.

Goal: Predict the unit price for a Airbnb post, given all the other metrics. Predict NYC Airbnb Rental Prices
Approach:

1. Supervised Learning task, because given labeled traning examples (each instance comes with expected output, i.e. unit's price).
2. Regression task, since we need to predict a value.
3. Multiple regression problem since the system will use multiple features to make a prediction.
4. Univariate regression problem since we are only trying to predict a single value for each unit.
5. There is no continuous flow of data, no need to adjust to changing data, and the data is small enough to fit in memmory: Batch Learning

Possible Performance Measure: Root Mean Square Error (RMSE), and Mean Absolute Error (MAE).

Data: New York City Airbnb Open Data | Kaggle

Project Author: Maksim Ekin Eren