This repository contains code and data files for analyzing London housing data. The data was collected from the London House Prices dataset by Justin Cirtautas, (check it here!) and contains information on housing prices, sales, and other related factors.
I used KNN, RandomForest, and LGBM to develop three different prediction models for London housing data. The KNN model is a non-parametric model that uses nearest neighbors to make predictions, while the RandomForest model uses decision trees and aggregates their results to make predictions. The LGBM model is a gradient boosting model that sequentially adds decision trees to improve the model's accuracy.
To develop these models, I first gathered and cleaned the London housing data. I then split the data into training and testing sets, and used the training set to train each of the models. After training, I used the testing set to evaluate the accuracy of each model.
To ensure that the code runs correctly, it is recommended to set up a virtual Python environment.
You can use the following steps to set up a virtual environment using venv:
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Open a command prompt or terminal window
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Navigate to the project directory
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Run the following command to create a virtual environment:
python3 -m venv housing-project
- Activate the virtual environment by running the following command:
source housing-project/bin/activate
- Install the required packages by running the following command:
pip install 'package-name'
After setting up the virtual environment, you can run the code in the housing.py file using your virtual environment.
- This repository was created by Thijs Verreck.