Purpose of this project is to implement machine learning algorithms on a real data set. Visualization and exploration of the dataset and data preparation are also the important parts of this project. Linear Regression, SVR and Random Forest have been implemented. After, tuning process of the algorithms, performance changings have been analyzed and optimal parameters have been found for the required functions of the algorithms. Performance comparison among these algorithms has been done by using different evaluation metrics. To complete all of those statistical computing and graphics operations, R language has been used.
Name of the dataset that has been used in this project is “House Sales in King County, USA” (https://www.kaggle.com/harlfoxem/housesalesprediction). This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015. Thus, aim of this project is to implement machine learning models to predict the price of the houses. Also, there are 21613 rows and 21 columns in this dataset. Description for each attribute explained below:
ID: Unique ID for each home sold
Date: Date of the home sale
Price: Price of each home sold
Bedrooms: Number of bedrooms/house
Bathrooms: Number of bathrooms/house
Sqft_living: Square footage of the apartments interior living space
Sqft_lot: Square footage of the land space
Floors: Number of floors
Waterfront: A variable for whether the apartment was overlooking the waterfront or not
View: An index from 0 to 4 of how good the view of the property was
Condition: An index from 1 to 5 on the condition of the apartment
Grade: An index from 1 to 13, where 1-3 falls short of building construction and design, 7 has an average level of construction and design, and 11-13 have a high-quality level of construction and design.
Aqft_above: The square footage of the interior housing space that is above ground level
Aqft_basement: The square footage of the interior housing space that is below ground
Yr_built: The year the house was initially built
Yr_renovated: The year of the house’s last renovation
Zipcode: What zip code area the house is in
Lat: Latitude coordinate
Long: Longitude coordinate
Sqft_living15: Living room area in 2015
Sqft_lot15: Lot size area in 2015