- In this project we predict the price of house in india using Machine Learning.
- See the data analysis pdf for understanding the data.
- The data for this is taken from here.
- Train.csv - 29451 rows x 12 columns
- Test.csv - 68720 rows x 11 columns
| Column | Description |
|---|---|
| POSTED_BY | Category marking who has listed the property |
| UNDER_CONSTRUCTION | Under Construction or Not |
| RERA | Rera approved or Not |
| BHK_NO. | Number of Rooms |
| BHK_OR_RK | Type of property |
| SQUARE_FT | Total area of the house in square feet |
| READYTOMOVE | Category marking Ready to move or Not |
| RESALE | Category marking Resale or not |
| ADDRESS | Address of the property |
| LONGITUDE | Longitude of the property |
| LATITUDE | Latitude of the property |
The Machine Learning models used in this project are
- Linear Regression
- Decision Tree
- Random Forest
- Gradient Boosting
- From the BarPlot we can see that Gradient Boosting has the highest score.
- We will use Gradient Boosting to make predictions on the test dataset.
- The result of the model is saved in csv file named "prediction.csv".