PROBLEM:
House price prediction can help a customer determine the price of a house depending upon the location ,city, area , number of bedrooms and so on. Using machine learning techniques, we can predict the price of a house accurately.
COLUMNS IN THE DATASET:
1.Name-this is the name of the property
2.Property Title-gives a gist about the number of bedrooms in the house, type of house and so on.
3.Price-gives the price of that particular property, either in Lakhs or Crores. This is our target column
4.Total_Area-gives the area occupied by the property in sqft
5.Price_Per_SQFT-gives the price per sqft for that property
6.Description-gives a brief summary about the property
7.Baths-gives the number of bathrooms in the house
8.Location-specifies the locality and city of the property
9.Balcony-specifies whether the house has a balcony or not.
This dataset has been obtained from the public domain.
CONCLUSION:
The price of the house depending upon the necessary parameters is calculated using this project.This project also gives insights about:
1.how type of house is affecting the price
2.popular localities in each city
3.how BHK/balcony are affecting the price