This data project aims to predict the prices for rental houses in Nairobi County. Various house details are used to make the machine learning model to predict the prices.
These features are:
- Location - the represents the location/division/ward where the house is located
- Bedrooms - this represents the number of bedrooms in the house.
- Bathrooms - this represents the number of bathrooms present in the house.
- Parking - this represents the number of parking slots that a tenant can have.
- Price - this represents the house rent price of the house.
Before building the machine model of the house, we first analye the data to gain insights from the data on the houses in Nairobi. This will help us understand which types of houses are more common in Nairobi, why prices in a certain location is capped where it is, where you can find the cheapest house with the features you want and many other valuable insights from the data.
For this project we got the dataset from different sources.
- Kaggle Nairobi dataset
- Scrapping real estate websites and added a filter to have houses in Nairobi only
After getting the dataset from the different sources we merged the datasets to form the final dataset, housing_data.csv