This project predicts houses prices data, collected from Aqar website. We performed linear regression algorithm to predict the price according to different features the house may have, size, floors, pools,number of rooms and other features.
The dataset is collected and scrapped from Aqar website. The chosen cities are Riyadh, Jeddah, Dammam, and Al-Khobar. This dataset focused on the rental houses.
The dataset represent the price of housing (Villa, Apartment, and chalet) in different city in Saudi Arabia
- Number of features: 25 features/Columns
- Number of rows: 2974 rows
| Columns | Description |
|---|---|
| city | city where house locate in |
| district | district where house locate in |
| front | What is the house front is north, west .. etc |
| size | size in m^2 |
| propertyage | property age for the house |
| bedrooms | number of bedrooms |
| bathrooms | number of bathrooms |
| livingrooms | number of livingrooms |
| kitchen | show whether the house have a kitchen or not |
| garage | show whether the house have a garage or not |
| driverroom | show whether the house have a driverroom or not |
| maidroom | show whether the house have a maid_room or not |
| furnished | show whether the house is furnished or not |
| ac | show whether the house have a ac or not |
| roof | show whether the house have a space for roof on top or not |
| pool | show whether the house have a pool or not |
| frontyard | show whether the house have a frontyard or not |
| basement | show whether the house have a basement or not |
| duplex | show whether the house is a duplex or not |
| stairs | show whether the house have a stairs or not |
| elevator | show whether the house have an elevator or not |
| fireplace | show whether the house have a fireplace or not |
| price | show the price of the house |
| details | shows any additional details from the house owner about the house |
- Python
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Sklearn
- Plotly