Predict_housing_prices

This is a fast API Server that been set up with jupyter note books to serve and predict the price of a house price for house given the house details, like Bedrooms and other.

🏡💰🏠Housing Market Data Attributes 🏠💰🏡

In the analysis of the housing market, it's essential to understand the various attributes that characterize each housing unit. These attributes provide valuable information for potential buyers, renters, and investors, helping them make informed decisions about their housing choices. Below, we describe the key data attributes used in our housing market analysis:

Bedroom Count:

This attribute represents the number of bedrooms in the housing unit, providing insights into its size and capacity.

Net Square Meters (Net Sqm):

Net square meters refer to the total usable interior space within the housing unit, excluding common areas like corridors and stairwells. It quantifies the size of the property.

Center Distance:

This attribute measures the distance of the housing unit from the central or downtown area of a city or town. It is a valuable metric for potential buyers or renters to assess proximity to urban amenities and activities.

Metro Distance:

Metro distance indicates the distance between the housing unit and the nearest metro or subway station. This information is particularly useful for individuals who rely on public transportation for their daily commute.

Floor:

The floor attribute specifies the level or story of the housing unit within the building, offering insights into its placement and accessibility within the structure.

Age:

The age of the property represents the number of years since its construction or renovation. It plays a crucial role in assessing the condition of the property and potential maintenance requirements.

Price:

Price is the cost associated with purchasing or renting the housing unit. It is a fundamental factor for individuals making housing decisions and can be influenced by various attributes such as bedroom count, size, location, and age.

usage

clone the project

git clone https://github.com/kaybrian/House_price_prediction_Class_presentation.git

create a virtual env

python -m venv env

activate the env

source env/bin/activate

install the virtual env requirements dependencies

pip install -r requirements.txt

run the project in the .ipynb

the project has been installed canbe run in the file with a vs code extension of jupyter notebook

Author

Kayongo Johnson Brian

KayO-GH/python_kigali_predict_housing_prices