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 house price, given all the other metrics.
Approach:
- Supervised Learning task, because given labeled traning examples.
- Regression task, since we need to predict a value.
- Multiple regression problem since the system will use multiple features to make a prediction.
- Univariate regression problem since we are only trying to predict a single value.
- 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: DC Residential Properties | Kaggle
Project Author: Maksim Ekin Eren
Attributes Used for Predictions:
- Heating unit type
- AC (yes/no)
- Number of Rooms
- Number of Bedrooms
- AYB: The earliest time the main portion of the building was built
- EYB: The year an improvement was built more recent than actual year built
- Number of stories
- GBA: Gross building area in square feet
- Building style
- Structure type
- Real estate grade
- Current condition
- External wall type
- Roof type
- Internal wall type
- Number of kitchens
- Number of fireplaces
- Land area
- Zipcode
- Latitude
- Longitude
- Neighborhood
- Ward
- Quadrant