House_Price_Prediction-R

Description:

House price prediction is the process of using data to estimate the value of a house. This can be done for a variety of reasons, such as to determine the value of a home for sale, to assess the risk of a mortgage, or to make investment decisions.
There are a variety of different methods that can be used to predict house prices. Some of the most common methods include:
Linear regression: This is a simple method that uses a straight line to predict the value of a house.
Random forest: This is a more complex method that uses a group of decision trees to make predictions.
Gradient boosting: This is a technique that combines multiple models to make predictions.
The accuracy of house price prediction models can vary depending on the quality of the data and the method that is used. However, these models can be a valuable tool for making informed decisions about the housing market.

The dataset you provided contains the following features:

date: The date the house was sold.
price: The price of the house in US dollars.
bedrooms: The number of bedrooms in the house.
bathrooms: The number of bathrooms in the house.
sqft_living: The square footage of the living space in the house.
sqft_lot: The square footage of the lot the house is on.
floors: The number of floors in the house.
waterfront: A binary variable indicating whether the house has a waterfront view.
view: A categorical variable indicating the quality of the view from the house.
condition: A categorical variable indicating the condition of the house.
sqft_above: The square footage of the living space above ground level.
sqft_basement: The square footage of the living space below ground level.
yr_built: The year the house was built.
yr_renovated: The year the house was renovated.
street: The name of the street the house is on.
city: The city the house is in.
statezip: The state and zip code of the house.
country: The country the house is in.
These features can be used to predict the price of a house. For example, a house with more bedrooms and bathrooms is likely to be more expensive than a house with fewer bedrooms and bathrooms. A house with a waterfront view is also likely to be more expensive than a house without a waterfront view. The condition of the house can also affect its price. A house in good condition is likely to be more expensive than a house in poor condition.