# Data Exploration
- Features Available
- Missing Values
- House prices kernel estimation per geographic coordinates
- Price distribution per building area
- Average house price per year built
- House price distribution per region

# Feature Eng
- Pegar no Address e separar por Address Name
- Date, separar por Day, Month, Year.

# Data Preparation
# Modeling
- Regression Trees
- Bagging
- Random Forest
- Boosting
- Kernel Approaches (SVM)

# Dataset
(https://www.kaggle.com/anthonypino/melbourne-housing-market)

- Suburb: Suburb
- Address: Address (eliminar)
- Rooms: Number of rooms
- Price: Price in Australian dollars
- Method:
- S - property sold;
- SP - property sold prior;
- PI - property passed in;
- PN - sold prior not disclosed;
- SN - sold not disclosed;
- NB - no bid;
- VB - vendor bid;
- W - withdrawn prior to auction;
- SA - sold after auction;
- SS - sold after auction price not disclosed.
- N/A - price or highest bid not available.
- Type:
- br - bedroom(s);
- h - house,cottage,villa, semi,terrace;
- u - unit, duplex;
- t - townhouse;
- dev site - development site;
- o res - other residential.

- SellerG: Real Estate Agent
- Date: Date sold
- Distance: Distance from CBD in Kilometres
- Regionname: General Region (West, North West, North, North east …etc)
- Propertycount: Number of properties that exist in the suburb.
- Bedroom2 : Scraped # of Bedrooms (from different source)
- Bathroom: Number of Bathrooms
- Car: Number of carspots
- Landsize: Land Size in Metres
- BuildingArea: Building Size in Metres
- YearBuilt: Year the house was built
- CouncilArea: Governing council for the area
- Lattitude: Self explanitory
- Longtitude: Self explanitory