House Price Prediction done on Bangalore House Price Dataset using Random Forest Regression with Hyper Parameter Tuning.
Dataset Link: https://www.kaggle.com/amitabhajoy/bengaluru-house-price-data?select=Bengaluru_House_Data.csv
The dataset contains 13320 instances and 9 attributes.
1. Area_Type: This attribute gives the information about the area in which is the house is
there like whether it is in a built-up area or a plot area or a super built-up area etc.,.
2. Availability: This attributes lets the customer know when the house will be available to
move-in.
3. Location: This attribute lets the client know about the exact location of the house.
4. Size: This attribute displays the size of the house whether it is a 2BHK or a 3BHK etc.
5. Society: This attribute gives information about the society in which the house is a part of.
6. Total_sqft: As the name suggests, this attributes informs about the total size of the house
in sqft.
7. #bath: This attribute gives the client the number of bathrooms present in the house.
8. #balcony: This attribute informs the customer about the number of balconies present in
the house.
9. #price: This attribute tells the price of the overall house.