A brief analysis of Sci-kits Boston house prices dataset and after a while, I will also add a prediction tool written in Python. This was done for a course assignment. In the analysis part, some variables have been tested for dropping. Dropping a couple of values seemed to have no significant decrease in our accuracy so we've dropped them. They'll also be dropped in the valuation tool.
This was written on Jupyter notebook with Python by using the libraries and modules which can be seen below.
Data: Boston housing prices dataset, which belongs to Scikit. The sklearn Boston dataset is used wisely in regression and is famous dataset from the 1970’s. There are 506 instances and 14 attributes.
Models: Regressions
pandas
matplotlib
numpy
seaborn
scikit-learn
statsmodel
I will add a python tool that will predict the houseprice based from some given parameters. Stay tuned :)