maksimekin/dc-residential-data-analysis

Data Analysis Practice on D.C. Residential Data by Kaggle. Regression problem (Price Prediction).

D.C. Residential Properties Data

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:

1. Supervised Learning task, because given labeled traning examples.
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.
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: 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