08Aristodemus24 / housing-prices-predictor

A full implementation of multivariate Linear Regression from scratch to predict housing prices using the California Housing dataset

PRODUCTION DONE. AWAITING DEPLOYMENT

Usage:

1. clone repository with git clone https://github.com/08Aristodemus24/housing-prices-predictor.git
2. navigate to directory with readme.md and requirements.txt file
3. run command; conda create -n <name of env e.g. housing-prices-predictor> python=3.11.2. Note that 3.11.2 must be the python version otherwise packages to be installed would not be compatible with a different python version
4. once environment is created activate it by running command conda activate
5. then run conda activate housing-prices-predictor
6. check if pip is installed by running conda list -e and checking list
7. if it is there then move to step 8, if not then install pip by typing conda install pip
8. if pip exists or install is done run pip install -r requirements.txt in the directory you are currently in

Things to implement:

• instead of a linear model implement a function to engineer new features that results in a more polynomial equation to use as our model:
• note that we have to normalize data first before passing data to map_feature() which engineers new features out of the current features to make the equation more polynomial
• I'm actually passing unnormalized X values in the test model so I need to find a way to recover previous standard deviation and mean calculated from the training the data which was used to normalized both training and cross validation data

References:

• https://flask.palletsprojects.com/en/3.0.x/patterns/wtforms/